+ 
+ 
+
+
+
+
+
+
+
+## Video Results
+
+| Model | +Instance Segmentation | +Box Detection | +||||||
|---|---|---|---|---|---|---|---|---|
| LVIS | +SA-Co/Gold | +LVIS | +COCO | +SA-Co/Gold | +||||
| cgF1 | +AP | +cgF1 | +cgF1 | +AP | +AP | +APo + | +cgF1 | +|
| Human | +- | +- | +72.8 | +- | +- | +- | +- | +74.0 | +
| OWLv2* | +29.3 | +43.4 | +24.6 | +30.2 | +45.5 | +46.1 | +23.9 | +24.5 | +
| DINO-X | +- | +38.5 | +21.3 | +- | +52.4 | +56.0 | +- | +22.5 | +
| Gemini 2.5 | +13.4 | +- | +13.0 | +16.1 | +- | +- | +- | +14.4 | +
| SAM 3 | +37.2 | +48.5 | +54.1 | +40.6 | +53.6 | +56.4 | +55.7 | +55.7 | +
* Partially trained on LVIS, APo refers to COCO-O accuracy
+ +
+
+
+
+
+
+## SA-Co Dataset
+
+We release 2 image benchmarks, [SA-Co/Gold](scripts/eval/gold/README.md) and
+[SA-Co/Silver](scripts/eval/silver/README.md), and a video benchmark
+[SA-Co/VEval](scripts/eval/veval/README.md). The datasets contain images (or videos) with annotated noun phrases. Each image/video and noun phrase pair is annotated with instance masks and unique IDs of each object matching the phrase. Phrases that have no matching objects (negative prompts) have no masks, shown in red font in the figure. See the linked READMEs for more details on how to download and run evaluations on the datasets.
+
+* HuggingFace host: [SA-Co/Gold](https://huggingface.co/datasets/facebook/SACo-Gold), [SA-Co/Silver](https://huggingface.co/datasets/facebook/SACo-Silver) and [SA-Co/VEval](https://huggingface.co/datasets/facebook/SACo-VEval)
+* Roboflow host: [SA-Co/Gold](https://universe.roboflow.com/sa-co-gold), [SA-Co/Silver](https://universe.roboflow.com/sa-co-silver) and [SA-Co/VEval](https://universe.roboflow.com/sa-co-veval)
+
+
+
+## Development
+
+To set up the development environment:
+
+```bash
+pip install -e ".[dev,train]"
+```
+
+To format the code:
+
+```bash
+ufmt format .
+```
+
+## Contributing
+
+See [contributing](CONTRIBUTING.md) and the
+[code of conduct](CODE_OF_CONDUCT.md).
+
+## License
+
+This project is licensed under the SAM License - see the [LICENSE](LICENSE) file
+for details.
+
+## Acknowledgements
+
+We would like to thank the following people for their contributions to the SAM 3 project: Alex He, Alexander Kirillov,
+Alyssa Newcomb, Ana Paula Kirschner Mofarrej, Andrea Madotto, Andrew Westbury, Ashley Gabriel, Azita Shokpour,
+Ben Samples, Bernie Huang, Carleigh Wood, Ching-Feng Yeh, Christian Puhrsch, Claudette Ward, Daniel Bolya,
+Daniel Li, Facundo Figueroa, Fazila Vhora, George Orlin, Hanzi Mao, Helen Klein, Hu Xu, Ida Cheng, Jake Kinney,
+Jiale Zhi, Jo Sampaio, Joel Schlosser, Justin Johnson, Kai Brown, Karen Bergan, Karla Martucci, Kenny Lehmann,
+Maddie Mintz, Mallika Malhotra, Matt Ward, Michelle Chan, Michelle Restrepo, Miranda Hartley, Muhammad Maaz,
+Nisha Deo, Peter Park, Phillip Thomas, Raghu Nayani, Rene Martinez Doehner, Robbie Adkins, Ross Girshik, Sasha
+Mitts, Shashank Jain, Spencer Whitehead, Ty Toledano, Valentin Gabeur, Vincent Cho, Vivian Lee, William Ngan,
+Xuehai He, Yael Yungster, Ziqi Pang, Ziyi Dou, Zoe Quake.
+
+## Citing SAM 3
+
+If you use SAM 3 or the SA-Co dataset in your research, please use the following BibTeX entry.
+
+```bibtex
+@misc{carion2025sam3segmentconcepts,
+ title={SAM 3: Segment Anything with Concepts},
+ author={Nicolas Carion and Laura Gustafson and Yuan-Ting Hu and Shoubhik Debnath and Ronghang Hu and Didac Suris and Chaitanya Ryali and Kalyan Vasudev Alwala and Haitham Khedr and Andrew Huang and Jie Lei and Tengyu Ma and Baishan Guo and Arpit Kalla and Markus Marks and Joseph Greer and Meng Wang and Peize Sun and Roman Rädle and Triantafyllos Afouras and Effrosyni Mavroudi and Katherine Xu and Tsung-Han Wu and Yu Zhou and Liliane Momeni and Rishi Hazra and Shuangrui Ding and Sagar Vaze and Francois Porcher and Feng Li and Siyuan Li and Aishwarya Kamath and Ho Kei Cheng and Piotr Dollár and Nikhila Ravi and Kate Saenko and Pengchuan Zhang and Christoph Feichtenhofer},
+ year={2025},
+ eprint={2511.16719},
+ archivePrefix={arXiv},
+ primaryClass={cs.CV},
+ url={https://arxiv.org/abs/2511.16719},
+}
+```
diff --git a/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/README_TRAIN.md b/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/README_TRAIN.md
new file mode 100644
index 0000000000000000000000000000000000000000..01904c27418b9c8765b933a9c08699a7d67b13a1
--- /dev/null
+++ b/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/README_TRAIN.md
@@ -0,0 +1,190 @@
+# Training
+
+This repository supports finetuning SAM3 models on custom datasets in multi-node setup or local execution. The training script is located at `sam3/train.py` and uses Hydra configuration management to handle complex training setups.
+
+
+## Installation
+
+```bash
+cd sam3
+pip install -e ".[train]"
+```
+
+### Training Script Usage
+
+The main training script is located at `sam3/train.py`. It uses Hydra configuration management to handle complex training setups.
+
+#### Basic Usage
+
+```bash
+# Example: Train on Roboflow dataset
+python sam3/train/train.py -c configs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml
+# Example: Train on ODinW13 dataset
+python sam3/train/train.py -c configs/odinw13/odinw_text_only_train.yaml
+```
+Follow [`Roboflow 100-VL`](https://github.com/roboflow/rf100-vl/) to download the roboflow 100-vl datasets. Follow [`GLIP`](https://github.com/microsoft/GLIP) to download the ODinW datasets. The data folder should be organized as follows, and put your roboflow_vl_100_root and odinw_data_root in the job configs.
+```
+roboflow_vl_100_root:
+ 13-lkc01
+ train
+ valid
+ test
+ 2024-frc
+ actions
+ ...
+odinw_data_root:
+ AerialMaritimeDrone
+ large
+ train
+ valid
+ test
+ Aquarium
+ ...
+```
+
+#### Command Line Arguments
+
+The training script supports several command line arguments:
+
+```bash
+python sam3/train/train.py \
+ -c CONFIG_NAME \
+ [--use-cluster 0|1] \
+ [--partition PARTITION_NAME] \
+ [--account ACCOUNT_NAME] \
+ [--qos QOS_NAME] \
+ [--num-gpus NUM_GPUS] \
+ [--num-nodes NUM_NODES]
+```
+
+**Arguments:**
+- `-c, --config`: **Required.** Path to the configuration file (e.g., `sam3/train/configs/roboflow_v100_full_ft_100_images.yaml`)
+- `--use-cluster`: Whether to launch on a cluster (0: local, 1: cluster). Default: uses config setting
+- `--partition`: SLURM partition name for cluster execution
+- `--account`: SLURM account name for cluster execution
+- `--qos`: SLURM QOS (Quality of Service) setting
+- `--num-gpus`: Number of GPUs per node. Default: uses config setting
+- `--num-nodes`: Number of nodes for distributed training. Default: uses config setting
+
+#### Local Training Examples
+
+```bash
+# Single GPU training
+python sam3/train/train.py -c configs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml --use-cluster 0 --num-gpus 1
+
+# Multi-GPU training on a single node
+python sam3/train/train.py -c configs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml --use-cluster 0 --num-gpus 4
+
+# Force local execution even if config specifies GPUs
+python sam3/train/train.py -c configs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml --use-cluster 0
+```
+
+#### Cluster Training Examples
+
+```bash
+# Basic cluster training with default settings from config
+python sam3/train/train.py -c configs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml --use-cluster 1
+
+# Cluster training with specific SLURM settings
+python sam3/train/train.py -c configs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml \
+ --use-cluster 1 \
+ --partition gpu_partition \
+ --account my_account \
+ --qos high_priority \
+ --num-gpus 8 \
+ --num-nodes 2
+```
+
+### Configuration Files
+
+Training configurations are stored in `sam3/train/configs/`. The configuration files use Hydra's YAML format and support:
+
+- **Dataset Configuration**: Data paths, transforms, and loading parameters
+- **Model Configuration**: Architecture settings, checkpoint paths, and model parameters
+- **Training Configuration**: Batch sizes, learning rates, optimization settings
+- **Launcher Configuration**: Distributed training and cluster settings
+- **Logging Configuration**: TensorBoard, experiment tracking, and output directories
+
+#### Key Configuration Sections
+
+```yaml
+# Paths to datasets and checkpoints
+paths:
+ bpe_path: /path/to/bpe/file
+ dataset_root: /path/to/dataset
+ experiment_log_dir: /path/to/logs
+
+# Launcher settings for local/cluster execution
+launcher:
+ num_nodes: 1
+ gpus_per_node: 2
+ experiment_log_dir: ${paths.experiment_log_dir}
+
+# Cluster execution settings
+submitit:
+ use_cluster: True
+ timeout_hour: 72
+ cpus_per_task: 10
+ partition: null
+ account: null
+```
+
+### Monitoring Training
+
+The training script automatically sets up logging and saves outputs to the experiment directory:
+
+```bash
+# Logs are saved to the experiment_log_dir specified in config
+experiment_log_dir/
+├── config.yaml # Original configuration
+├── config_resolved.yaml # Resolved configuration with all variables expanded
+├── checkpoints/ # Model checkpoints (if skip_checkpointing=False)
+├── tensorboard/ # TensorBoard logs
+├── logs/ # Text logs
+└── submitit_logs/ # Cluster job logs (if using cluster)
+```
+
+You can monitor training progress using TensorBoard:
+
+```bash
+tensorboard --logdir /path/to/experiment_log_dir/tensorboard
+```
+
+### Job Arrays for Dataset Sweeps
+
+The Roboflow and ODinW configuration supports job arrays for training multiple models on different datasets:
+
+This feature is specifically enabled via,
+```yaml
+submitit:
+ job_array:
+ num_tasks: 100
+ task_index: 0
+```
+
+The configuration includes a complete list of 100 Roboflow supercategories, and the `submitit.job_array.task_index` automatically selects which dataset to use based on the array job index.
+
+```bash
+# Submit job array to train on different Roboflow datasets
+# The job array index selects which dataset from all_roboflow_supercategories
+python sam3/train/train.py -c configs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml \
+ --use-cluster 1
+```
+
+### Reproduce ODinW13 10-shot results
+Running the following job will give the results on the ODinW13 seed 300, see `odinw_train.train_file: fewshot_train_shot10_seed300` in the config file.
+```bash
+# Example: Train on ODinW13 dataset
+python sam3/train/train.py -c configs/odinw13/odinw_text_only_train.yaml
+```
+Change `odinw_train.train_file` to `fewshot_train_shot10_seed30` and `fewshot_train_shot10_seed3` to get the results for the other two seeds. Final results are aggregated from the three seeds. Notice that a small number of jobs may diverge during training, in which case we just use the last checkpoint's result before it diverges.
+
+
+### Eval Script Usage
+With a similar setup as the training config, the training script `sam3/train.py` can also be used for evaluation, too, when setting `trainer.mode = val` in the job config. Run the following job will give the results on the zero-shot results on RF100-VL and ODinW13 datasets.
+```bash
+# Example: Evaluate on Roboflow dataset
+python sam3/train/train.py -c configs/roboflow_v100/roboflow_v100_eval.yaml
+# Example: Evaluate on ODinW13 dataset
+python sam3/train/train.py -c configs/odinw13/odinw_text_only.yaml
+```
diff --git a/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/assets/images/groceries.jpg b/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/assets/images/groceries.jpg
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+version https://git-lfs.github.com/spec/v1
+oid sha256:7073dfecb5a3ecafb6152124113163a0ea1c1c70f92999ec892b519eca63e3d3
+size 168066
diff --git a/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/assets/images/test_image.jpg b/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/assets/images/test_image.jpg
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+version https://git-lfs.github.com/spec/v1
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+size 70626
diff --git a/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/assets/images/truck.jpg b/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/assets/images/truck.jpg
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:941715e721c8864324a1425b445ea4dde0498b995c45ddce0141a58971c6ff99
+size 271475
diff --git a/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/examples/saco_gold_silver_eval_example.ipynb b/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/examples/saco_gold_silver_eval_example.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c5ae4dae4be2364b2436de3b8478160a01c1dd18
--- /dev/null
+++ b/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/examples/saco_gold_silver_eval_example.ipynb
@@ -0,0 +1,2214 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "417b89e9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Copyright (c) Meta Platforms, Inc. and affiliates."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "0e0d2e74",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import copy\n",
+ "import json\n",
+ "import os\n",
+ "\n",
+ "import numpy as np\n",
+ "\n",
+ "from pycocotools.coco import COCO\n",
+ "from sam3.eval.cgf1_eval import CGF1Evaluator"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1ceba210-cb61-4998-a153-c13c612e6182",
+ "metadata": {},
+ "source": [
+ "# SA-Co/Gold"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "b31ab5d3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Update to the directory where the GT annotation and PRED files exist\n",
+ "GT_DIR = # PUT YOUR PATH HERE\n",
+ "PRED_DIR = # PUT YOUR PATH HERE"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "25613248",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Relative file names for GT files for 7 SA-Co/Gold subsets\n",
+ "saco_gold_gts = {\n",
+ " # MetaCLIP Captioner\n",
+ " \"metaclip_nps\": [\n",
+ " \"gold_metaclip_merged_a_release_test.json\",\n",
+ " \"gold_metaclip_merged_b_release_test.json\",\n",
+ " \"gold_metaclip_merged_c_release_test.json\",\n",
+ " ],\n",
+ " # SA-1B captioner\n",
+ " \"sa1b_nps\": [\n",
+ " \"gold_sa1b_merged_a_release_test.json\",\n",
+ " \"gold_sa1b_merged_b_release_test.json\",\n",
+ " \"gold_sa1b_merged_c_release_test.json\",\n",
+ " ],\n",
+ " # Crowded\n",
+ " \"crowded\": [\n",
+ " \"gold_crowded_merged_a_release_test.json\",\n",
+ " \"gold_crowded_merged_b_release_test.json\",\n",
+ " \"gold_crowded_merged_c_release_test.json\",\n",
+ " ],\n",
+ " # FG Food\n",
+ " \"fg_food\": [\n",
+ " \"gold_fg_food_merged_a_release_test.json\",\n",
+ " \"gold_fg_food_merged_b_release_test.json\",\n",
+ " \"gold_fg_food_merged_c_release_test.json\",\n",
+ " ],\n",
+ " # FG Sports\n",
+ " \"fg_sports_equipment\": [\n",
+ " \"gold_fg_sports_equipment_merged_a_release_test.json\",\n",
+ " \"gold_fg_sports_equipment_merged_b_release_test.json\",\n",
+ " \"gold_fg_sports_equipment_merged_c_release_test.json\",\n",
+ " ],\n",
+ " # Attributes\n",
+ " \"attributes\": [\n",
+ " \"gold_attributes_merged_a_release_test.json\",\n",
+ " \"gold_attributes_merged_b_release_test.json\",\n",
+ " \"gold_attributes_merged_c_release_test.json\",\n",
+ " ],\n",
+ " # Wiki common\n",
+ " \"wiki_common\": [\n",
+ " \"gold_wiki_common_merged_a_release_test.json\",\n",
+ " \"gold_wiki_common_merged_b_release_test.json\",\n",
+ " \"gold_wiki_common_merged_c_release_test.json\",\n",
+ " ],\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2703e989",
+ "metadata": {},
+ "source": [
+ "## Run offline evaluation for all 7 SA-Co/Gold subsets"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0314ddca-46e7-47fd-9f66-346c4f8baf96",
+ "metadata": {},
+ "source": [
+ "We assume the inference has already been run for all 7 datasets. With the default configurations, the predictions are dumped in a predictable folder"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "cc28d29f",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing subset: metaclip_nps\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.28s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.26s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.27s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Loaded 26221 predictions\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 33057/33057 [00:10<00:00, 3171.54it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accumulating results\n",
+ "cgF1 metric, IoU type=segm\n",
+ " Average cgF1 @[ IoU=0.50:0.95] = 0.473\n",
+ " Average precision @[ IoU=0.50:0.95] = 0.609\n",
+ " Average recall @[ IoU=0.50:0.95] = 0.532\n",
+ " Average F1 @[ IoU=0.50:0.95] = 0.568\n",
+ " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.759\n",
+ " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.586\n",
+ " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.652\n",
+ " Average IL_precision = 0.916\n",
+ " Average IL_recall = 0.760\n",
+ " Average IL_F1 = 0.830\n",
+ " Average IL_FPR = 0.013\n",
+ " Average IL_MCC = 0.807\n",
+ " Average cgF1 @[ IoU=0.50 ] = 0.568\n",
+ " Average precision @[ IoU=0.50 ] = 0.732\n",
+ " Average recall @[ IoU=0.50 ] = 0.639\n",
+ " Average F1 @[ IoU=0.50 ] = 0.682\n",
+ " Average positive_macro_F1 @[ IoU=0.50 ] = 0.872\n",
+ " Average positive_micro_F1 @[ IoU=0.50 ] = 0.704\n",
+ " Average positive_micro_precision @[ IoU=0.50 ] = 0.783\n",
+ " Average cgF1 @[ IoU=0.75 ] = 0.515\n",
+ " Average precision @[ IoU=0.75 ] = 0.664\n",
+ " Average recall @[ IoU=0.75 ] = 0.580\n",
+ " Average F1 @[ IoU=0.75 ] = 0.619\n",
+ " Average positive_macro_F1 @[ IoU=0.75 ] = 0.815\n",
+ " Average positive_micro_F1 @[ IoU=0.75 ] = 0.638\n",
+ " Average positive_micro_precision @[ IoU=0.75 ] = 0.710\n",
+ "\n",
+ "{'cgF1_eval_segm_cgF1': 0.47258639187690543, 'cgF1_eval_segm_precision': 0.6094191949336503, 'cgF1_eval_segm_recall': 0.5321857924434281, 'cgF1_eval_segm_F1': 0.5681401781600861, 'cgF1_eval_segm_positive_macro_F1': 0.7594764589657407, 'cgF1_eval_segm_positive_micro_F1': 0.5858321816359353, 'cgF1_eval_segm_positive_micro_precision': 0.6516289867940714, 'cgF1_eval_segm_IL_precision': 0.9157483928108933, 'cgF1_eval_segm_IL_recall': 0.7596648256028061, 'cgF1_eval_segm_IL_F1': 0.8304356444812764, 'cgF1_eval_segm_IL_FPR': 0.013198590231849291, 'cgF1_eval_segm_IL_MCC': 0.8066924397311339, 'cgF1_eval_segm_cgF1@0.5': 0.5675504156248331, 'cgF1_eval_segm_precision@0.5': 0.7318687877530184, 'cgF1_eval_segm_recall@0.5': 0.6391170051959989, 'cgF1_eval_segm_F1@0.5': 0.6823056445199827, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8723414712702943, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7035524168467409, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.7825597234127607, 'cgF1_eval_segm_cgF1@0.75': 0.5149222822957974, 'cgF1_eval_segm_precision@0.75': 0.6640084211254257, 'cgF1_eval_segm_recall@0.75': 0.5798567730119127, 'cgF1_eval_segm_F1@0.75': 0.6190362594405769, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8153807596867352, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6383130136529085, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.7099991898479673}\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.22s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.23s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.21s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Loaded 26221 predictions\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 33057/33057 [00:08<00:00, 3762.56it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accumulating results\n",
+ "cgF1 metric, IoU type=bbox\n",
+ " Average cgF1 @[ IoU=0.50:0.95] = 0.500\n",
+ " Average precision @[ IoU=0.50:0.95] = 0.645\n",
+ " Average recall @[ IoU=0.50:0.95] = 0.563\n",
+ " Average F1 @[ IoU=0.50:0.95] = 0.601\n",
+ " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.813\n",
+ " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.620\n",
+ " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.690\n",
+ " Average IL_precision = 0.916\n",
+ " Average IL_recall = 0.760\n",
+ " Average IL_F1 = 0.831\n",
+ " Average IL_FPR = 0.013\n",
+ " Average IL_MCC = 0.807\n",
+ " Average cgF1 @[ IoU=0.50 ] = 0.571\n",
+ " Average precision @[ IoU=0.50 ] = 0.736\n",
+ " Average recall @[ IoU=0.50 ] = 0.642\n",
+ " Average F1 @[ IoU=0.50 ] = 0.686\n",
+ " Average positive_macro_F1 @[ IoU=0.50 ] = 0.878\n",
+ " Average positive_micro_F1 @[ IoU=0.50 ] = 0.707\n",
+ " Average positive_micro_precision @[ IoU=0.50 ] = 0.787\n",
+ " Average cgF1 @[ IoU=0.75 ] = 0.530\n",
+ " Average precision @[ IoU=0.75 ] = 0.683\n",
+ " Average recall @[ IoU=0.75 ] = 0.596\n",
+ " Average F1 @[ IoU=0.75 ] = 0.636\n",
+ " Average positive_macro_F1 @[ IoU=0.75 ] = 0.842\n",
+ " Average positive_micro_F1 @[ IoU=0.75 ] = 0.656\n",
+ " Average positive_micro_precision @[ IoU=0.75 ] = 0.731\n",
+ "\n",
+ "{'cgF1_eval_bbox_cgF1': 0.5003430734030954, 'cgF1_eval_bbox_precision': 0.6454559084753544, 'cgF1_eval_bbox_recall': 0.5625467145593948, 'cgF1_eval_bbox_F1': 0.6011063859782098, 'cgF1_eval_bbox_positive_macro_F1': 0.8129272614186818, 'cgF1_eval_bbox_positive_micro_F1': 0.6198941576942177, 'cgF1_eval_bbox_positive_micro_precision': 0.6903838360796344, 'cgF1_eval_bbox_IL_precision': 0.916437098044895, 'cgF1_eval_bbox_IL_recall': 0.7598020554320895, 'cgF1_eval_bbox_IL_F1': 0.830800752892585, 'cgF1_eval_bbox_IL_FPR': 0.013092112361504437, 'cgF1_eval_bbox_IL_MCC': 0.8071427471815361, 'cgF1_eval_bbox_cgF1@0.5': 0.5705998939047701, 'cgF1_eval_bbox_precision@0.5': 0.7360818482088336, 'cgF1_eval_bbox_recall@0.5': 0.6415316986326687, 'cgF1_eval_bbox_F1@0.5': 0.6855123686584659, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8775997789207387, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7069380179618158, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.7873179304150807, 'cgF1_eval_bbox_cgF1@0.75': 0.5297149268767383, 'cgF1_eval_bbox_precision@0.75': 0.6833433592887199, 'cgF1_eval_bbox_recall@0.75': 0.59556749986514, 'cgF1_eval_bbox_F1@0.75': 0.6363934960024782, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.8423507795177905, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6562840696103028, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.7309084997915145}\n",
+ "Processing subset: sa1b_nps\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.42s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.42s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.44s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Loaded 50994 predictions\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12893/12893 [00:12<00:00, 1019.95it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accumulating results\n",
+ "cgF1 metric, IoU type=segm\n",
+ " Average cgF1 @[ IoU=0.50:0.95] = 0.537\n",
+ " Average precision @[ IoU=0.50:0.95] = 0.613\n",
+ " Average recall @[ IoU=0.50:0.95] = 0.624\n",
+ " Average F1 @[ IoU=0.50:0.95] = 0.618\n",
+ " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.749\n",
+ " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.626\n",
+ " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.627\n",
+ " Average IL_precision = 0.957\n",
+ " Average IL_recall = 0.918\n",
+ " Average IL_F1 = 0.937\n",
+ " Average IL_FPR = 0.055\n",
+ " Average IL_MCC = 0.858\n",
+ " Average cgF1 @[ IoU=0.50 ] = 0.662\n",
+ " Average precision @[ IoU=0.50 ] = 0.755\n",
+ " Average recall @[ IoU=0.50 ] = 0.769\n",
+ " Average F1 @[ IoU=0.50 ] = 0.762\n",
+ " Average positive_macro_F1 @[ IoU=0.50 ] = 0.868\n",
+ " Average positive_micro_F1 @[ IoU=0.50 ] = 0.771\n",
+ " Average positive_micro_precision @[ IoU=0.50 ] = 0.773\n",
+ " Average cgF1 @[ IoU=0.75 ] = 0.584\n",
+ " Average precision @[ IoU=0.75 ] = 0.666\n",
+ " Average recall @[ IoU=0.75 ] = 0.679\n",
+ " Average F1 @[ IoU=0.75 ] = 0.672\n",
+ " Average positive_macro_F1 @[ IoU=0.75 ] = 0.803\n",
+ " Average positive_micro_F1 @[ IoU=0.75 ] = 0.680\n",
+ " Average positive_micro_precision @[ IoU=0.75 ] = 0.682\n",
+ "\n",
+ "{'cgF1_eval_segm_cgF1': 0.5368885175297502, 'cgF1_eval_segm_precision': 0.6126733177941744, 'cgF1_eval_segm_recall': 0.623866410830199, 'cgF1_eval_segm_F1': 0.6181692134008732, 'cgF1_eval_segm_positive_macro_F1': 0.7490377907736454, 'cgF1_eval_segm_positive_micro_F1': 0.6255271013781429, 'cgF1_eval_segm_positive_micro_precision': 0.6272971894411186, 'cgF1_eval_segm_IL_precision': 0.9571307299155163, 'cgF1_eval_segm_IL_recall': 0.9180350114102273, 'cgF1_eval_segm_IL_F1': 0.9371748135185498, 'cgF1_eval_segm_IL_FPR': 0.054851556832937805, 'cgF1_eval_segm_IL_MCC': 0.8582977721459122, 'cgF1_eval_segm_cgF1@0.5': 0.6617782611463625, 'cgF1_eval_segm_precision@0.5': 0.7551805547951372, 'cgF1_eval_segm_recall@0.5': 0.7689771507351261, 'cgF1_eval_segm_F1@0.5': 0.7619664171091266, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8675783572433642, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7710357437976187, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.7732059252215057, 'cgF1_eval_segm_cgF1@0.75': 0.584028878830561, 'cgF1_eval_segm_precision@0.75': 0.6664635050035673, 'cgF1_eval_segm_recall@0.75': 0.6786393053852088, 'cgF1_eval_segm_F1@0.75': 0.6724463056533416, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8029021282211246, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6804501861520328, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.6823712921904396}\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.35s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.35s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.38s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Loaded 50994 predictions\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12893/12893 [00:07<00:00, 1636.66it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accumulating results\n",
+ "cgF1 metric, IoU type=bbox\n",
+ " Average cgF1 @[ IoU=0.50:0.95] = 0.554\n",
+ " Average precision @[ IoU=0.50:0.95] = 0.633\n",
+ " Average recall @[ IoU=0.50:0.95] = 0.642\n",
+ " Average F1 @[ IoU=0.50:0.95] = 0.637\n",
+ " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.786\n",
+ " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.645\n",
+ " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.648\n",
+ " Average IL_precision = 0.957\n",
+ " Average IL_recall = 0.918\n",
+ " Average IL_F1 = 0.937\n",
+ " Average IL_FPR = 0.055\n",
+ " Average IL_MCC = 0.858\n",
+ " Average cgF1 @[ IoU=0.50 ] = 0.656\n",
+ " Average precision @[ IoU=0.50 ] = 0.749\n",
+ " Average recall @[ IoU=0.50 ] = 0.760\n",
+ " Average F1 @[ IoU=0.50 ] = 0.755\n",
+ " Average positive_macro_F1 @[ IoU=0.50 ] = 0.863\n",
+ " Average positive_micro_F1 @[ IoU=0.50 ] = 0.764\n",
+ " Average positive_micro_precision @[ IoU=0.50 ] = 0.768\n",
+ " Average cgF1 @[ IoU=0.75 ] = 0.589\n",
+ " Average precision @[ IoU=0.75 ] = 0.673\n",
+ " Average recall @[ IoU=0.75 ] = 0.683\n",
+ " Average F1 @[ IoU=0.75 ] = 0.678\n",
+ " Average positive_macro_F1 @[ IoU=0.75 ] = 0.817\n",
+ " Average positive_micro_F1 @[ IoU=0.75 ] = 0.686\n",
+ " Average positive_micro_precision @[ IoU=0.75 ] = 0.690\n",
+ "\n",
+ "{'cgF1_eval_bbox_cgF1': 0.5536231468412395, 'cgF1_eval_bbox_precision': 0.6328165449001351, 'cgF1_eval_bbox_recall': 0.6417390943649743, 'cgF1_eval_bbox_F1': 0.6371965950434251, 'cgF1_eval_bbox_positive_macro_F1': 0.785505899426481, 'cgF1_eval_bbox_positive_micro_F1': 0.6449030271971777, 'cgF1_eval_bbox_positive_micro_precision': 0.6481993029451039, 'cgF1_eval_bbox_IL_precision': 0.957272212654602, 'cgF1_eval_bbox_IL_recall': 0.9180461328469408, 'cgF1_eval_bbox_IL_F1': 0.9372484265333024, 'cgF1_eval_bbox_IL_FPR': 0.05468042729410095, 'cgF1_eval_bbox_IL_MCC': 0.8584595256861315, 'cgF1_eval_bbox_cgF1@0.5': 0.655649783867308, 'cgF1_eval_bbox_precision@0.5': 0.7494286121079209, 'cgF1_eval_bbox_recall@0.5': 0.7599953615328335, 'cgF1_eval_bbox_F1@0.5': 0.7546250062240573, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8630646236311263, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7637515389479476, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.7676460229909632, 'cgF1_eval_bbox_cgF1@0.75': 0.5891939922914327, 'cgF1_eval_bbox_precision@0.75': 0.6734724947681224, 'cgF1_eval_bbox_recall@0.75': 0.6829682826014282, 'cgF1_eval_bbox_F1@0.75': 0.6781371571048335, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.8172625456348024, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6863386970055625, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.6898435339270219}\n",
+ "Processing subset: crowded\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.42s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.42s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.42s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Loaded 82963 predictions\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20241/20241 [00:10<00:00, 2003.85it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accumulating results\n",
+ "cgF1 metric, IoU type=segm\n",
+ " Average cgF1 @[ IoU=0.50:0.95] = 0.611\n",
+ " Average precision @[ IoU=0.50:0.95] = 0.643\n",
+ " Average recall @[ IoU=0.50:0.95] = 0.686\n",
+ " Average F1 @[ IoU=0.50:0.95] = 0.664\n",
+ " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.689\n",
+ " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.677\n",
+ " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.669\n",
+ " Average IL_precision = 0.933\n",
+ " Average IL_recall = 0.913\n",
+ " Average IL_F1 = 0.923\n",
+ " Average IL_FPR = 0.018\n",
+ " Average IL_MCC = 0.902\n",
+ " Average cgF1 @[ IoU=0.50 ] = 0.735\n",
+ " Average precision @[ IoU=0.50 ] = 0.773\n",
+ " Average recall @[ IoU=0.50 ] = 0.825\n",
+ " Average F1 @[ IoU=0.50 ] = 0.798\n",
+ " Average positive_macro_F1 @[ IoU=0.50 ] = 0.830\n",
+ " Average positive_micro_F1 @[ IoU=0.50 ] = 0.815\n",
+ " Average positive_micro_precision @[ IoU=0.50 ] = 0.805\n",
+ " Average cgF1 @[ IoU=0.75 ] = 0.667\n",
+ " Average precision @[ IoU=0.75 ] = 0.702\n",
+ " Average recall @[ IoU=0.75 ] = 0.748\n",
+ " Average F1 @[ IoU=0.75 ] = 0.724\n",
+ " Average positive_macro_F1 @[ IoU=0.75 ] = 0.748\n",
+ " Average positive_micro_F1 @[ IoU=0.75 ] = 0.739\n",
+ " Average positive_micro_precision @[ IoU=0.75 ] = 0.730\n",
+ "\n",
+ "{'cgF1_eval_segm_cgF1': 0.6107822436866207, 'cgF1_eval_segm_precision': 0.6429349935778358, 'cgF1_eval_segm_recall': 0.6856112974213837, 'cgF1_eval_segm_F1': 0.663537765323902, 'cgF1_eval_segm_positive_macro_F1': 0.6891461338399572, 'cgF1_eval_segm_positive_micro_F1': 0.6772901260675144, 'cgF1_eval_segm_positive_micro_precision': 0.6692661193229303, 'cgF1_eval_segm_IL_precision': 0.9330264669889466, 'cgF1_eval_segm_IL_recall': 0.9132687540181867, 'cgF1_eval_segm_IL_F1': 0.9230413942007347, 'cgF1_eval_segm_IL_FPR': 0.018415390455738805, 'cgF1_eval_segm_IL_MCC': 0.901802964754481, 'cgF1_eval_segm_cgF1@0.5': 0.7346796584523991, 'cgF1_eval_segm_precision@0.5': 0.77334498201075, 'cgF1_eval_segm_recall@0.5': 0.8246775518006043, 'cgF1_eval_segm_F1@0.5': 0.7981368549267828, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.829596777967627, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.8146786905412517, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.8050169926635622, 'cgF1_eval_segm_cgF1@0.75': 0.6666293625039935, 'cgF1_eval_segm_precision@0.75': 0.7017176741205416, 'cgF1_eval_segm_recall@0.75': 0.7482958149470458, 'cgF1_eval_segm_F1@0.75': 0.7242086950813854, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.748186632903701, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.7392184197193072, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.7304562192291248}\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.36s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.37s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.37s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Loaded 82963 predictions\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20241/20241 [00:07<00:00, 2785.45it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accumulating results\n",
+ "cgF1 metric, IoU type=bbox\n",
+ " Average cgF1 @[ IoU=0.50:0.95] = 0.617\n",
+ " Average precision @[ IoU=0.50:0.95] = 0.650\n",
+ " Average recall @[ IoU=0.50:0.95] = 0.692\n",
+ " Average F1 @[ IoU=0.50:0.95] = 0.670\n",
+ " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.731\n",
+ " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.684\n",
+ " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.677\n",
+ " Average IL_precision = 0.933\n",
+ " Average IL_recall = 0.913\n",
+ " Average IL_F1 = 0.923\n",
+ " Average IL_FPR = 0.018\n",
+ " Average IL_MCC = 0.902\n",
+ " Average cgF1 @[ IoU=0.50 ] = 0.730\n",
+ " Average precision @[ IoU=0.50 ] = 0.769\n",
+ " Average recall @[ IoU=0.50 ] = 0.818\n",
+ " Average F1 @[ IoU=0.50 ] = 0.793\n",
+ " Average positive_macro_F1 @[ IoU=0.50 ] = 0.829\n",
+ " Average positive_micro_F1 @[ IoU=0.50 ] = 0.809\n",
+ " Average positive_micro_precision @[ IoU=0.50 ] = 0.801\n",
+ " Average cgF1 @[ IoU=0.75 ] = 0.668\n",
+ " Average precision @[ IoU=0.75 ] = 0.704\n",
+ " Average recall @[ IoU=0.75 ] = 0.749\n",
+ " Average F1 @[ IoU=0.75 ] = 0.726\n",
+ " Average positive_macro_F1 @[ IoU=0.75 ] = 0.776\n",
+ " Average positive_micro_F1 @[ IoU=0.75 ] = 0.741\n",
+ " Average positive_micro_precision @[ IoU=0.75 ] = 0.733\n",
+ "\n",
+ "{'cgF1_eval_bbox_cgF1': 0.6170650320984323, 'cgF1_eval_bbox_precision': 0.6504449386041414, 'cgF1_eval_bbox_recall': 0.691669313906474, 'cgF1_eval_bbox_F1': 0.6703740536459668, 'cgF1_eval_bbox_positive_macro_F1': 0.7305480221708001, 'cgF1_eval_bbox_positive_micro_F1': 0.6842570452919619, 'cgF1_eval_bbox_positive_micro_precision': 0.677099838003926, 'cgF1_eval_bbox_IL_precision': 0.9330264669889466, 'cgF1_eval_bbox_IL_recall': 0.9132687540181867, 'cgF1_eval_bbox_IL_F1': 0.9230413942007347, 'cgF1_eval_bbox_IL_FPR': 0.018415390455738805, 'cgF1_eval_bbox_IL_MCC': 0.901802964754481, 'cgF1_eval_bbox_cgF1@0.5': 0.7298487590689315, 'cgF1_eval_bbox_precision@0.5': 0.7693209759495023, 'cgF1_eval_bbox_recall@0.5': 0.818079563737977, 'cgF1_eval_bbox_F1@0.5': 0.7929014858831388, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8289958931744248, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.8093217560752147, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.8008473542838228, 'cgF1_eval_bbox_cgF1@0.75': 0.6681534554056624, 'cgF1_eval_bbox_precision@0.75': 0.7042930379997401, 'cgF1_eval_bbox_recall@0.75': 0.7489302375506022, 'cgF1_eval_bbox_F1@0.75': 0.7258761503763029, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.7757641647406367, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.7409084706076228, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.7331546048467947}\n",
+ "Processing subset: fg_food\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.12s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.12s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.10s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Loaded 10846 predictions\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13794/13794 [00:03<00:00, 3963.81it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accumulating results\n",
+ "cgF1 metric, IoU type=segm\n",
+ " Average cgF1 @[ IoU=0.50:0.95] = 0.534\n",
+ " Average precision @[ IoU=0.50:0.95] = 0.734\n",
+ " Average recall @[ IoU=0.50:0.95] = 0.583\n",
+ " Average F1 @[ IoU=0.50:0.95] = 0.650\n",
+ " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.825\n",
+ " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.673\n",
+ " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.795\n",
+ " Average IL_precision = 0.917\n",
+ " Average IL_recall = 0.713\n",
+ " Average IL_F1 = 0.802\n",
+ " Average IL_FPR = 0.006\n",
+ " Average IL_MCC = 0.794\n",
+ " Average cgF1 @[ IoU=0.50 ] = 0.582\n",
+ " Average precision @[ IoU=0.50 ] = 0.800\n",
+ " Average recall @[ IoU=0.50 ] = 0.635\n",
+ " Average F1 @[ IoU=0.50 ] = 0.708\n",
+ " Average positive_macro_F1 @[ IoU=0.50 ] = 0.885\n",
+ " Average positive_micro_F1 @[ IoU=0.50 ] = 0.733\n",
+ " Average positive_micro_precision @[ IoU=0.50 ] = 0.866\n",
+ " Average cgF1 @[ IoU=0.75 ] = 0.561\n",
+ " Average precision @[ IoU=0.75 ] = 0.771\n",
+ " Average recall @[ IoU=0.75 ] = 0.612\n",
+ " Average F1 @[ IoU=0.75 ] = 0.682\n",
+ " Average positive_macro_F1 @[ IoU=0.75 ] = 0.854\n",
+ " Average positive_micro_F1 @[ IoU=0.75 ] = 0.706\n",
+ " Average positive_micro_precision @[ IoU=0.75 ] = 0.835\n",
+ "\n",
+ "{'cgF1_eval_segm_cgF1': 0.5340544897290387, 'cgF1_eval_segm_precision': 0.734205779536348, 'cgF1_eval_segm_recall': 0.5831005937305522, 'cgF1_eval_segm_F1': 0.6499373841120774, 'cgF1_eval_segm_positive_macro_F1': 0.8250979043741161, 'cgF1_eval_segm_positive_micro_F1': 0.6727883081524807, 'cgF1_eval_segm_positive_micro_precision': 0.7952174681839976, 'cgF1_eval_segm_IL_precision': 0.9171210458919072, 'cgF1_eval_segm_IL_recall': 0.7133163691999013, 'cgF1_eval_segm_IL_F1': 0.8024804230776101, 'cgF1_eval_segm_IL_FPR': 0.006024573919459011, 'cgF1_eval_segm_IL_MCC': 0.7937927625341562, 'cgF1_eval_segm_cgF1@0.5': 0.5816253704026515, 'cgF1_eval_segm_precision@0.5': 0.7996003836243679, 'cgF1_eval_segm_recall@0.5': 0.6350364862736196, 'cgF1_eval_segm_F1@0.5': 0.7078307231011086, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8854304579823623, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.732716897727604, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.8660462915809068, 'cgF1_eval_segm_cgF1@0.75': 0.5605505836352753, 'cgF1_eval_segm_precision@0.75': 0.7706293552321807, 'cgF1_eval_segm_recall@0.75': 0.6120279179303726, 'cgF1_eval_segm_F1@0.75': 0.6821828949054192, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8542637117087882, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.7061674155931289, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.8346678027555117}\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.09s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.08s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.08s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Loaded 10846 predictions\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13794/13794 [00:03<00:00, 4490.07it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accumulating results\n",
+ "cgF1 metric, IoU type=bbox\n",
+ " Average cgF1 @[ IoU=0.50:0.95] = 0.538\n",
+ " Average precision @[ IoU=0.50:0.95] = 0.737\n",
+ " Average recall @[ IoU=0.50:0.95] = 0.588\n",
+ " Average F1 @[ IoU=0.50:0.95] = 0.654\n",
+ " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.859\n",
+ " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.676\n",
+ " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.797\n",
+ " Average IL_precision = 0.919\n",
+ " Average IL_recall = 0.714\n",
+ " Average IL_F1 = 0.804\n",
+ " Average IL_FPR = 0.006\n",
+ " Average IL_MCC = 0.795\n",
+ " Average cgF1 @[ IoU=0.50 ] = 0.583\n",
+ " Average precision @[ IoU=0.50 ] = 0.798\n",
+ " Average recall @[ IoU=0.50 ] = 0.637\n",
+ " Average F1 @[ IoU=0.50 ] = 0.708\n",
+ " Average positive_macro_F1 @[ IoU=0.50 ] = 0.898\n",
+ " Average positive_micro_F1 @[ IoU=0.50 ] = 0.733\n",
+ " Average positive_micro_precision @[ IoU=0.50 ] = 0.864\n",
+ " Average cgF1 @[ IoU=0.75 ] = 0.560\n",
+ " Average precision @[ IoU=0.75 ] = 0.766\n",
+ " Average recall @[ IoU=0.75 ] = 0.612\n",
+ " Average F1 @[ IoU=0.75 ] = 0.680\n",
+ " Average positive_macro_F1 @[ IoU=0.75 ] = 0.875\n",
+ " Average positive_micro_F1 @[ IoU=0.75 ] = 0.704\n",
+ " Average positive_micro_precision @[ IoU=0.75 ] = 0.829\n",
+ "\n",
+ "{'cgF1_eval_bbox_cgF1': 0.5378561873516663, 'cgF1_eval_bbox_precision': 0.7365034817881422, 'cgF1_eval_bbox_recall': 0.5876295139864577, 'cgF1_eval_bbox_F1': 0.6536480883299698, 'cgF1_eval_bbox_positive_macro_F1': 0.8586355938093078, 'cgF1_eval_bbox_positive_micro_F1': 0.6764422338399003, 'cgF1_eval_bbox_positive_micro_precision': 0.7970161989834335, 'cgF1_eval_bbox_IL_precision': 0.9193020709713172, 'cgF1_eval_bbox_IL_recall': 0.7138018622237071, 'cgF1_eval_bbox_IL_F1': 0.8036220047681086, 'cgF1_eval_bbox_IL_FPR': 0.005866962657110365, 'cgF1_eval_bbox_IL_MCC': 0.7951250830369732, 'cgF1_eval_bbox_cgF1@0.5': 0.5827707578455898, 'cgF1_eval_bbox_precision@0.5': 0.7980019820579024, 'cgF1_eval_bbox_recall@0.5': 0.6366969450550463, 'cgF1_eval_bbox_F1@0.5': 0.7082322331345615, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8975145206561829, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7329296613555469, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.8635675488958368, 'cgF1_eval_bbox_cgF1@0.75': 0.5597151953142597, 'cgF1_eval_bbox_precision@0.75': 0.7664335511202087, 'cgF1_eval_bbox_recall@0.75': 0.6115096347599293, 'cgF1_eval_bbox_F1@0.75': 0.6802130814411795, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.8745268555395833, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.7039335159399481, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.829405387472316}\n",
+ "Processing subset: fg_sports_equipment\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.08s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.08s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.08s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Loaded 6562 predictions\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12107/12107 [00:03<00:00, 3306.95it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accumulating results\n",
+ "cgF1 metric, IoU type=segm\n",
+ " Average cgF1 @[ IoU=0.50:0.95] = 0.655\n",
+ " Average precision @[ IoU=0.50:0.95] = 0.733\n",
+ " Average recall @[ IoU=0.50:0.95] = 0.701\n",
+ " Average F1 @[ IoU=0.50:0.95] = 0.717\n",
+ " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.840\n",
+ " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.738\n",
+ " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.778\n",
+ " Average IL_precision = 0.962\n",
+ " Average IL_recall = 0.850\n",
+ " Average IL_F1 = 0.903\n",
+ " Average IL_FPR = 0.006\n",
+ " Average IL_MCC = 0.888\n",
+ " Average cgF1 @[ IoU=0.50 ] = 0.737\n",
+ " Average precision @[ IoU=0.50 ] = 0.825\n",
+ " Average recall @[ IoU=0.50 ] = 0.789\n",
+ " Average F1 @[ IoU=0.50 ] = 0.807\n",
+ " Average positive_macro_F1 @[ IoU=0.50 ] = 0.920\n",
+ " Average positive_micro_F1 @[ IoU=0.50 ] = 0.830\n",
+ " Average positive_micro_precision @[ IoU=0.50 ] = 0.875\n",
+ " Average cgF1 @[ IoU=0.75 ] = 0.697\n",
+ " Average precision @[ IoU=0.75 ] = 0.780\n",
+ " Average recall @[ IoU=0.75 ] = 0.746\n",
+ " Average F1 @[ IoU=0.75 ] = 0.763\n",
+ " Average positive_macro_F1 @[ IoU=0.75 ] = 0.879\n",
+ " Average positive_micro_F1 @[ IoU=0.75 ] = 0.785\n",
+ " Average positive_micro_precision @[ IoU=0.75 ] = 0.827\n",
+ "\n",
+ "{'cgF1_eval_segm_cgF1': 0.6552238992458554, 'cgF1_eval_segm_precision': 0.7333019582948739, 'cgF1_eval_segm_recall': 0.7013045276359755, 'cgF1_eval_segm_F1': 0.7168964364723446, 'cgF1_eval_segm_positive_macro_F1': 0.8401797467726777, 'cgF1_eval_segm_positive_micro_F1': 0.7375018641548449, 'cgF1_eval_segm_positive_micro_precision': 0.7777500429595898, 'cgF1_eval_segm_IL_precision': 0.9624470012341326, 'cgF1_eval_segm_IL_recall': 0.8497326198664531, 'cgF1_eval_segm_IL_F1': 0.9025839944636483, 'cgF1_eval_segm_IL_FPR': 0.0060564618534671814, 'cgF1_eval_segm_IL_MCC': 0.8884369397448539, 'cgF1_eval_segm_cgF1@0.5': 0.7373951194278512, 'cgF1_eval_segm_precision@0.5': 0.8252586823786763, 'cgF1_eval_segm_recall@0.5': 0.7892487451810898, 'cgF1_eval_segm_F1@0.5': 0.8068021593354898, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.920431978172454, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.8299915125541946, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.8752805967752407, 'cgF1_eval_segm_cgF1@0.75': 0.6970450572765126, 'cgF1_eval_segm_precision@0.75': 0.7801034623682158, 'cgF1_eval_segm_recall@0.75': 0.7460638608622613, 'cgF1_eval_segm_F1@0.75': 0.7626540805113754, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8790410692537068, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.7845746007327135, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.8273883555153815}\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.06s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.06s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.06s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Loaded 6562 predictions\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12107/12107 [00:03<00:00, 3661.16it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accumulating results\n",
+ "cgF1 metric, IoU type=bbox\n",
+ " Average cgF1 @[ IoU=0.50:0.95] = 0.681\n",
+ " Average precision @[ IoU=0.50:0.95] = 0.760\n",
+ " Average recall @[ IoU=0.50:0.95] = 0.730\n",
+ " Average F1 @[ IoU=0.50:0.95] = 0.745\n",
+ " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.879\n",
+ " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.766\n",
+ " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.806\n",
+ " Average IL_precision = 0.962\n",
+ " Average IL_recall = 0.850\n",
+ " Average IL_F1 = 0.903\n",
+ " Average IL_FPR = 0.006\n",
+ " Average IL_MCC = 0.888\n",
+ " Average cgF1 @[ IoU=0.50 ] = 0.735\n",
+ " Average precision @[ IoU=0.50 ] = 0.821\n",
+ " Average recall @[ IoU=0.50 ] = 0.788\n",
+ " Average F1 @[ IoU=0.50 ] = 0.804\n",
+ " Average positive_macro_F1 @[ IoU=0.50 ] = 0.921\n",
+ " Average positive_micro_F1 @[ IoU=0.50 ] = 0.827\n",
+ " Average positive_micro_precision @[ IoU=0.50 ] = 0.870\n",
+ " Average cgF1 @[ IoU=0.75 ] = 0.711\n",
+ " Average precision @[ IoU=0.75 ] = 0.794\n",
+ " Average recall @[ IoU=0.75 ] = 0.762\n",
+ " Average F1 @[ IoU=0.75 ] = 0.777\n",
+ " Average positive_macro_F1 @[ IoU=0.75 ] = 0.901\n",
+ " Average positive_micro_F1 @[ IoU=0.75 ] = 0.800\n",
+ " Average positive_micro_precision @[ IoU=0.75 ] = 0.842\n",
+ "\n",
+ "{'cgF1_eval_bbox_cgF1': 0.6806904643208772, 'cgF1_eval_bbox_precision': 0.7600893518323295, 'cgF1_eval_bbox_recall': 0.7300429019642443, 'cgF1_eval_bbox_F1': 0.744713228137678, 'cgF1_eval_bbox_positive_macro_F1': 0.8793127218535274, 'cgF1_eval_bbox_positive_micro_F1': 0.766166324102149, 'cgF1_eval_bbox_positive_micro_precision': 0.806161117331975, 'cgF1_eval_bbox_IL_precision': 0.9624470012341326, 'cgF1_eval_bbox_IL_recall': 0.8497326198664531, 'cgF1_eval_bbox_IL_F1': 0.9025839944636483, 'cgF1_eval_bbox_IL_FPR': 0.0060564618534671814, 'cgF1_eval_bbox_IL_MCC': 0.8884369397448539, 'cgF1_eval_bbox_cgF1@0.5': 0.7348434113860801, 'cgF1_eval_bbox_precision@0.5': 0.8205550136275866, 'cgF1_eval_bbox_recall@0.5': 0.7881183467784425, 'cgF1_eval_bbox_F1@0.5': 0.8039596834419491, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.9208045886576268, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.8271193806924737, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.8702918216440054, 'cgF1_eval_bbox_cgF1@0.75': 0.7106209729640045, 'cgF1_eval_bbox_precision@0.75': 0.7935089183088213, 'cgF1_eval_bbox_recall@0.75': 0.7621413877989296, 'cgF1_eval_bbox_F1@0.75': 0.7774589352243091, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.9005662678327496, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.799855275229871, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.8416063646394022}\n",
+ "Processing subset: attributes\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.07s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.06s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.06s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Loaded 5834 predictions\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9222/9222 [00:03<00:00, 2820.36it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accumulating results\n",
+ "cgF1 metric, IoU type=segm\n",
+ " Average cgF1 @[ IoU=0.50:0.95] = 0.549\n",
+ " Average precision @[ IoU=0.50:0.95] = 0.643\n",
+ " Average recall @[ IoU=0.50:0.95] = 0.670\n",
+ " Average F1 @[ IoU=0.50:0.95] = 0.656\n",
+ " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.872\n",
+ " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.720\n",
+ " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.778\n",
+ " Average IL_precision = 0.797\n",
+ " Average IL_recall = 0.819\n",
+ " Average IL_F1 = 0.808\n",
+ " Average IL_FPR = 0.048\n",
+ " Average IL_MCC = 0.763\n",
+ " Average cgF1 @[ IoU=0.50 ] = 0.600\n",
+ " Average precision @[ IoU=0.50 ] = 0.703\n",
+ " Average recall @[ IoU=0.50 ] = 0.733\n",
+ " Average F1 @[ IoU=0.50 ] = 0.717\n",
+ " Average positive_macro_F1 @[ IoU=0.50 ] = 0.930\n",
+ " Average positive_micro_F1 @[ IoU=0.50 ] = 0.787\n",
+ " Average positive_micro_precision @[ IoU=0.50 ] = 0.850\n",
+ " Average cgF1 @[ IoU=0.75 ] = 0.569\n",
+ " Average precision @[ IoU=0.75 ] = 0.667\n",
+ " Average recall @[ IoU=0.75 ] = 0.695\n",
+ " Average F1 @[ IoU=0.75 ] = 0.680\n",
+ " Average positive_macro_F1 @[ IoU=0.75 ] = 0.890\n",
+ " Average positive_micro_F1 @[ IoU=0.75 ] = 0.746\n",
+ " Average positive_micro_precision @[ IoU=0.75 ] = 0.806\n",
+ "\n",
+ "{'cgF1_eval_segm_cgF1': 0.5492693860624807, 'cgF1_eval_segm_precision': 0.6432095082730505, 'cgF1_eval_segm_recall': 0.670390051111405, 'cgF1_eval_segm_F1': 0.6564685998234858, 'cgF1_eval_segm_positive_macro_F1': 0.8717482377036931, 'cgF1_eval_segm_positive_micro_F1': 0.7200264336735771, 'cgF1_eval_segm_positive_micro_precision': 0.7777168057004865, 'cgF1_eval_segm_IL_precision': 0.7970687706893637, 'cgF1_eval_segm_IL_recall': 0.8187608565033231, 'cgF1_eval_segm_IL_F1': 0.807768708426457, 'cgF1_eval_segm_IL_FPR': 0.0480320213411565, 'cgF1_eval_segm_IL_MCC': 0.7628461406064031, 'cgF1_eval_segm_cgF1@0.5': 0.6002145763651893, 'cgF1_eval_segm_precision@0.5': 0.7028636035479556, 'cgF1_eval_segm_recall@0.5': 0.7325649901724294, 'cgF1_eval_segm_F1@0.5': 0.7173570363948575, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.9296877722553353, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.786809481513619, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.849845702782115, 'cgF1_eval_segm_cgF1@0.75': 0.5692213046663587, 'cgF1_eval_segm_precision@0.75': 0.6665721387419297, 'cgF1_eval_segm_recall@0.75': 0.6947399321885362, 'cgF1_eval_segm_F1@0.75': 0.6803146414763769, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8904105590365932, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.7461810113031079, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.8059650049377783}\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.05s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.05s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.06s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Loaded 5834 predictions\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9222/9222 [00:02<00:00, 3370.79it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accumulating results\n",
+ "cgF1 metric, IoU type=bbox\n",
+ " Average cgF1 @[ IoU=0.50:0.95] = 0.565\n",
+ " Average precision @[ IoU=0.50:0.95] = 0.660\n",
+ " Average recall @[ IoU=0.50:0.95] = 0.689\n",
+ " Average F1 @[ IoU=0.50:0.95] = 0.674\n",
+ " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.901\n",
+ " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.739\n",
+ " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.798\n",
+ " Average IL_precision = 0.798\n",
+ " Average IL_recall = 0.819\n",
+ " Average IL_F1 = 0.808\n",
+ " Average IL_FPR = 0.048\n",
+ " Average IL_MCC = 0.764\n",
+ " Average cgF1 @[ IoU=0.50 ] = 0.602\n",
+ " Average precision @[ IoU=0.50 ] = 0.703\n",
+ " Average recall @[ IoU=0.50 ] = 0.734\n",
+ " Average F1 @[ IoU=0.50 ] = 0.718\n",
+ " Average positive_macro_F1 @[ IoU=0.50 ] = 0.934\n",
+ " Average positive_micro_F1 @[ IoU=0.50 ] = 0.788\n",
+ " Average positive_micro_precision @[ IoU=0.50 ] = 0.850\n",
+ " Average cgF1 @[ IoU=0.75 ] = 0.580\n",
+ " Average precision @[ IoU=0.75 ] = 0.679\n",
+ " Average recall @[ IoU=0.75 ] = 0.708\n",
+ " Average F1 @[ IoU=0.75 ] = 0.693\n",
+ " Average positive_macro_F1 @[ IoU=0.75 ] = 0.911\n",
+ " Average positive_micro_F1 @[ IoU=0.75 ] = 0.760\n",
+ " Average positive_micro_precision @[ IoU=0.75 ] = 0.820\n",
+ "\n",
+ "{'cgF1_eval_bbox_cgF1': 0.5646399041045982, 'cgF1_eval_bbox_precision': 0.6603061905215142, 'cgF1_eval_bbox_recall': 0.6892275617275063, 'cgF1_eval_bbox_F1': 0.6744070010794116, 'cgF1_eval_bbox_positive_macro_F1': 0.9009593951299972, 'cgF1_eval_bbox_positive_micro_F1': 0.7394009518446127, 'cgF1_eval_bbox_positive_micro_precision': 0.7975684658366964, 'cgF1_eval_bbox_IL_precision': 0.7981961664046244, 'cgF1_eval_bbox_IL_recall': 0.8189705027073623, 'cgF1_eval_bbox_IL_F1': 0.8084493997068858, 'cgF1_eval_bbox_IL_FPR': 0.04777792605795036, 'cgF1_eval_bbox_IL_MCC': 0.7636450868719721, 'cgF1_eval_bbox_cgF1@0.5': 0.6015189050487545, 'cgF1_eval_bbox_precision@0.5': 0.7034306576855498, 'cgF1_eval_bbox_recall@0.5': 0.7342408779449281, 'cgF1_eval_bbox_F1@0.5': 0.7184556528947154, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.9340793389274347, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7876943299834277, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.8496575051487155, 'cgF1_eval_bbox_cgF1@0.75': 0.5804244074542156, 'cgF1_eval_bbox_precision@0.75': 0.678763802700204, 'cgF1_eval_bbox_recall@0.75': 0.7084936162032075, 'cgF1_eval_bbox_F1@0.75': 0.693260171561296, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.9107485801358162, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.7600708986837572, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.8198629856211306}\n",
+ "Processing subset: wiki_common\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.23s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.21s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.21s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Loaded 8045 predictions\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 65452/65452 [00:11<00:00, 5775.55it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accumulating results\n",
+ "cgF1 metric, IoU type=segm\n",
+ " Average cgF1 @[ IoU=0.50:0.95] = 0.425\n",
+ " Average precision @[ IoU=0.50:0.95] = 0.677\n",
+ " Average recall @[ IoU=0.50:0.95] = 0.509\n",
+ " Average F1 @[ IoU=0.50:0.95] = 0.581\n",
+ " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.811\n",
+ " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.608\n",
+ " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.757\n",
+ " Average IL_precision = 0.822\n",
+ " Average IL_recall = 0.607\n",
+ " Average IL_F1 = 0.698\n",
+ " Average IL_FPR = 0.004\n",
+ " Average IL_MCC = 0.699\n",
+ " Average cgF1 @[ IoU=0.50 ] = 0.482\n",
+ " Average precision @[ IoU=0.50 ] = 0.767\n",
+ " Average recall @[ IoU=0.50 ] = 0.577\n",
+ " Average F1 @[ IoU=0.50 ] = 0.658\n",
+ " Average positive_macro_F1 @[ IoU=0.50 ] = 0.905\n",
+ " Average positive_micro_F1 @[ IoU=0.50 ] = 0.689\n",
+ " Average positive_micro_precision @[ IoU=0.50 ] = 0.857\n",
+ " Average cgF1 @[ IoU=0.75 ] = 0.450\n",
+ " Average precision @[ IoU=0.75 ] = 0.717\n",
+ " Average recall @[ IoU=0.75 ] = 0.539\n",
+ " Average F1 @[ IoU=0.75 ] = 0.615\n",
+ " Average positive_macro_F1 @[ IoU=0.75 ] = 0.844\n",
+ " Average positive_micro_F1 @[ IoU=0.75 ] = 0.644\n",
+ " Average positive_micro_precision @[ IoU=0.75 ] = 0.801\n",
+ "\n",
+ "{'cgF1_eval_segm_cgF1': 0.42532135099113527, 'cgF1_eval_segm_precision': 0.6770954780038412, 'cgF1_eval_segm_recall': 0.5089505000184188, 'cgF1_eval_segm_F1': 0.5810550953744452, 'cgF1_eval_segm_positive_macro_F1': 0.8110173727833955, 'cgF1_eval_segm_positive_micro_F1': 0.6084924575531054, 'cgF1_eval_segm_positive_micro_precision': 0.7565875096115964, 'cgF1_eval_segm_IL_precision': 0.8222698066935975, 'cgF1_eval_segm_IL_recall': 0.6066350707705976, 'cgF1_eval_segm_IL_F1': 0.6981813291457964, 'cgF1_eval_segm_IL_FPR': 0.003917989709314776, 'cgF1_eval_segm_IL_MCC': 0.6989755513181787, 'cgF1_eval_segm_cgF1@0.5': 0.4817908882049053, 'cgF1_eval_segm_precision@0.5': 0.766985739738834, 'cgF1_eval_segm_recall@0.5': 0.5765180664001743, 'cgF1_eval_segm_F1@0.5': 0.6582016716689654, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.9051793905267748, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.6892814595536413, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.8570310238186531, 'cgF1_eval_segm_cgF1@0.75': 0.4501092776435151, 'cgF1_eval_segm_precision@0.75': 0.7165538006875134, 'cgF1_eval_segm_recall@0.75': 0.5386100291574232, 'cgF1_eval_segm_F1@0.75': 0.6149194365440128, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8441369689666682, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.643955681703983, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.8006782989648239}\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.18s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.19s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.18s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Loaded 8045 predictions\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 65452/65452 [00:10<00:00, 6310.26it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accumulating results\n",
+ "cgF1 metric, IoU type=bbox\n",
+ " Average cgF1 @[ IoU=0.50:0.95] = 0.443\n",
+ " Average precision @[ IoU=0.50:0.95] = 0.706\n",
+ " Average recall @[ IoU=0.50:0.95] = 0.529\n",
+ " Average F1 @[ IoU=0.50:0.95] = 0.605\n",
+ " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.865\n",
+ " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.633\n",
+ " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.788\n",
+ " Average IL_precision = 0.822\n",
+ " Average IL_recall = 0.607\n",
+ " Average IL_F1 = 0.698\n",
+ " Average IL_FPR = 0.004\n",
+ " Average IL_MCC = 0.699\n",
+ " Average cgF1 @[ IoU=0.50 ] = 0.483\n",
+ " Average precision @[ IoU=0.50 ] = 0.770\n",
+ " Average recall @[ IoU=0.50 ] = 0.578\n",
+ " Average F1 @[ IoU=0.50 ] = 0.660\n",
+ " Average positive_macro_F1 @[ IoU=0.50 ] = 0.909\n",
+ " Average positive_micro_F1 @[ IoU=0.50 ] = 0.691\n",
+ " Average positive_micro_precision @[ IoU=0.50 ] = 0.860\n",
+ " Average cgF1 @[ IoU=0.75 ] = 0.459\n",
+ " Average precision @[ IoU=0.75 ] = 0.732\n",
+ " Average recall @[ IoU=0.75 ] = 0.549\n",
+ " Average F1 @[ IoU=0.75 ] = 0.628\n",
+ " Average positive_macro_F1 @[ IoU=0.75 ] = 0.875\n",
+ " Average positive_micro_F1 @[ IoU=0.75 ] = 0.657\n",
+ " Average positive_micro_precision @[ IoU=0.75 ] = 0.818\n",
+ "\n",
+ "{'cgF1_eval_bbox_cgF1': 0.4427363803296873, 'cgF1_eval_bbox_precision': 0.7055568361999337, 'cgF1_eval_bbox_recall': 0.5294148470670139, 'cgF1_eval_bbox_F1': 0.6048754410558315, 'cgF1_eval_bbox_positive_macro_F1': 0.8646998403591537, 'cgF1_eval_bbox_positive_micro_F1': 0.6334075340614461, 'cgF1_eval_bbox_positive_micro_precision': 0.7883902742397528, 'cgF1_eval_bbox_IL_precision': 0.8222698066935975, 'cgF1_eval_bbox_IL_recall': 0.6066350707705976, 'cgF1_eval_bbox_IL_F1': 0.6981813291457964, 'cgF1_eval_bbox_IL_FPR': 0.003917989709314776, 'cgF1_eval_bbox_IL_MCC': 0.6989755513181787, 'cgF1_eval_bbox_cgF1@0.5': 0.4830441402948427, 'cgF1_eval_bbox_precision@0.5': 0.769787514130574, 'cgF1_eval_bbox_recall@0.5': 0.5776103612892368, 'cgF1_eval_bbox_F1@0.5': 0.6599450021609545, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.9093257078072422, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.6910744437110613, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.8601617307549769, 'cgF1_eval_bbox_cgF1@0.75': 0.4594543570790383, 'cgF1_eval_bbox_precision@0.75': 0.7321970410413953, 'cgF1_eval_bbox_recall@0.75': 0.5494043351540936, 'cgF1_eval_bbox_F1@0.75': 0.6277159968872291, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.8750107505715273, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6573253616847772, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.8181580793592987}\n"
+ ]
+ }
+ ],
+ "source": [
+ "results_gold = {}\n",
+ "results_gold_bbox = {}\n",
+ "\n",
+ "for subset_name, gts in saco_gold_gts.items():\n",
+ " print(\"Processing subset: \", subset_name)\n",
+ " gt_paths = [os.path.join(GT_DIR, gt) for gt in gts]\n",
+ " pred_path = os.path.join(PRED_DIR, f\"gold_{subset_name}/dumps/gold_{subset_name}/coco_predictions_segm.json\")\n",
+ " \n",
+ " evaluator = CGF1Evaluator(gt_path=gt_paths, verbose=True, iou_type=\"segm\") \n",
+ " summary = evaluator.evaluate(pred_path)\n",
+ " print(summary)\n",
+ "\n",
+ " cur_results = {}\n",
+ " cur_results[\"cgf1\"] = summary[\"cgF1_eval_segm_cgF1\"] * 100\n",
+ " cur_results[\"il_mcc\"] = summary[\"cgF1_eval_segm_IL_MCC\"]\n",
+ " cur_results[\"pmf1\"] = summary[\"cgF1_eval_segm_positive_micro_F1\"] * 100\n",
+ " results_gold[subset_name] = cur_results\n",
+ "\n",
+ " # Also eval bbox \n",
+ " evaluator = CGF1Evaluator(gt_path=gt_paths, verbose=True, iou_type=\"bbox\") \n",
+ " summary = evaluator.evaluate(pred_path)\n",
+ " print(summary)\n",
+ "\n",
+ " cur_results = {}\n",
+ " cur_results[\"cgf1\"] = summary[\"cgF1_eval_bbox_cgF1\"] * 100\n",
+ " cur_results[\"il_mcc\"] = summary[\"cgF1_eval_bbox_IL_MCC\"]\n",
+ " cur_results[\"pmf1\"] = summary[\"cgF1_eval_bbox_positive_micro_F1\"] * 100\n",
+ " results_gold_bbox[subset_name] = cur_results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "c808a7cf-ecda-445a-a827-53a16dee4504",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Compute averages\n",
+ "METRICS = [\"cgf1\", \"il_mcc\", \"pmf1\"]\n",
+ "avg_stats, avg_stats_bbox = {}, {}\n",
+ "for key in METRICS:\n",
+ " avg_stats[key] = sum(res[key] for res in results_gold.values()) / len(results_gold)\n",
+ " avg_stats_bbox[key] = sum(res[key] for res in results_gold_bbox.values()) / len(results_gold_bbox)\n",
+ "results_gold[\"Average\"] = avg_stats\n",
+ "results_gold_bbox[\"Average\"] = avg_stats_bbox"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "c26b1fb2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| Model | +SA-V test | +YT-Temporal-1B test | +SmartGlasses test | +LVVIS test | +BURST test | +|||
|---|---|---|---|---|---|---|---|---|
| cgF1 | +pHOTA | +cgF1 | +pHOTA | +cgF1 | +pHOTA | +mAP | +HOTA | +|
| Human | +53.1 | +70.5 | +71.2 | +78.4 | +58.5 | +72.3 | +- | +- | +
| SAM 3 | +30.3 | +58.0 | +50.8 | +69.9 | +36.4 | +63.6 | +36.3 | +44.5 | +
| metaclip_nps | sa1b_nps | crowded | fg_food | fg_sports_equipment | attributes | wiki_common | Average | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 |
| 47.26 | 0.81 | 58.58 | 53.69 | 0.86 | 62.55 | 61.08 | 0.9 | 67.73 | 53.41 | 0.79 | 67.28 | 65.52 | 0.89 | 73.75 | 54.93 | 0.76 | 72.0 | 42.53 | 0.7 | 60.85 | 54.06 | 0.82 | 66.11 |
| metaclip_nps | sa1b_nps | crowded | fg_food | fg_sports_equipment | attributes | wiki_common | Average | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 |
| 50.03 | 0.81 | 61.99 | 55.36 | 0.86 | 64.49 | 61.71 | 0.9 | 68.43 | 53.79 | 0.8 | 67.64 | 68.07 | 0.89 | 76.62 | 56.46 | 0.76 | 73.94 | 44.27 | 0.7 | 63.34 | 55.67 | 0.82 | 68.06 |
| bdd100k | droid | ego4d | food_rec | geode | inaturalist | nga_art | sav | yt1b | fathomnet | Average | ||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 |
| 46.61 | 0.78 | 60.13 | 45.58 | 0.76 | 60.35 | 38.64 | 0.62 | 62.56 | 52.96 | 0.79 | 67.21 | 70.07 | 0.89 | 78.73 | 65.8 | 0.82 | 80.67 | 38.06 | 0.66 | 57.62 | 44.36 | 0.67 | 66.05 | 42.07 | 0.72 | 58.36 | 51.53 | 0.86 | 59.98 | 49.57 | 0.76 | 65.17 |
| bdd100k | droid | ego4d | food_rec | geode | inaturalist | nga_art | sav | yt1b | fathomnet | Average | ||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 | cgf1 | il_mcc | pmf1 |
| 46.21 | 0.78 | 59.62 | 46.12 | 0.76 | 61.07 | 38.79 | 0.62 | 62.8 | 53.35 | 0.79 | 67.71 | 70.76 | 0.89 | 79.51 | 65.24 | 0.82 | 79.99 | 38.47 | 0.66 | 58.23 | 44.41 | 0.67 | 66.13 | 42.36 | 0.72 | 58.76 | 54.04 | 0.86 | 62.91 | 49.98 | 0.76 | 65.67 |
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n",
+ "# \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "644532a8",
+ "metadata": {},
+ "source": [
+ "## Environment Set-up"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "07fabfee",
+ "metadata": {},
+ "source": [
+ "First install `sam3` in your environment using the [installation instructions](https://github.com/facebookresearch/sam3?tab=readme-ov-file#installation) in the repository."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0be845da",
+ "metadata": {},
+ "source": [
+ "## Set-up"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "33681dd1",
+ "metadata": {},
+ "source": [
+ "Necessary imports and helper functions for displaying points, boxes, and masks."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fe773ede",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "using_colab = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "79250a4e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if using_colab:\n",
+ " import torch\n",
+ " import torchvision\n",
+ " print(\"PyTorch version:\", torch.__version__)\n",
+ " print(\"Torchvision version:\", torchvision.__version__)\n",
+ " print(\"CUDA is available:\", torch.cuda.is_available())\n",
+ " import sys\n",
+ " !{sys.executable} -m pip install opencv-python matplotlib scikit-learn\n",
+ " !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/sam3.git'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "69b28288",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "# if using Apple MPS, fall back to CPU for unsupported ops\n",
+ "os.environ[\"PYTORCH_ENABLE_MPS_FALLBACK\"] = \"1\"\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import matplotlib.pyplot as plt\n",
+ "from PIL import Image\n",
+ "import sam3\n",
+ "sam3_root = os.path.join(os.path.dirname(sam3.__file__), \"..\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "33a15e2f-c7e1-4e5d-862f-fcb751a60b89",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# select the device for computation\n",
+ "if torch.cuda.is_available():\n",
+ " device = torch.device(\"cuda\")\n",
+ "elif torch.backends.mps.is_available():\n",
+ " device = torch.device(\"mps\")\n",
+ "else:\n",
+ " device = torch.device(\"cpu\")\n",
+ "print(f\"using device: {device}\")\n",
+ "\n",
+ "if device.type == \"cuda\":\n",
+ " # use bfloat16 for the entire notebook\n",
+ " torch.autocast(\"cuda\", dtype=torch.bfloat16).__enter__()\n",
+ " # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)\n",
+ " if torch.cuda.get_device_properties(0).major >= 8:\n",
+ " torch.backends.cuda.matmul.allow_tf32 = True\n",
+ " torch.backends.cudnn.allow_tf32 = True\n",
+ "elif device.type == \"mps\":\n",
+ " print(\n",
+ " \"\\nSupport for MPS devices is preliminary. SAM 3 is trained with CUDA and might \"\n",
+ " \"give numerically different outputs and sometimes degraded performance on MPS. \"\n",
+ " \"See e.g. https://github.com/pytorch/pytorch/issues/84936 for a discussion.\"\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "29bc90d5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "np.random.seed(3)\n",
+ "\n",
+ "def show_mask(mask, ax, random_color=False, borders = True):\n",
+ " if random_color:\n",
+ " color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n",
+ " else:\n",
+ " color = np.array([30/255, 144/255, 255/255, 0.6])\n",
+ " h, w = mask.shape[-2:]\n",
+ " mask = mask.astype(np.uint8)\n",
+ " mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n",
+ " if borders:\n",
+ " import cv2\n",
+ " contours, _ = cv2.findContours(mask,cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) \n",
+ " # Try to smooth contours\n",
+ " contours = [cv2.approxPolyDP(contour, epsilon=0.01, closed=True) for contour in contours]\n",
+ " mask_image = cv2.drawContours(mask_image, contours, -1, (1, 1, 1, 0.5), thickness=2) \n",
+ " ax.imshow(mask_image)\n",
+ "\n",
+ "def show_points(coords, labels, ax, marker_size=375):\n",
+ " pos_points = coords[labels==1]\n",
+ " neg_points = coords[labels==0]\n",
+ " ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)\n",
+ " ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25) \n",
+ "\n",
+ "def show_box(box, ax):\n",
+ " x0, y0 = box[0], box[1]\n",
+ " w, h = box[2] - box[0], box[3] - box[1]\n",
+ " ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0, 0, 0, 0), lw=2)) \n",
+ "\n",
+ "def show_masks(image, masks, scores, point_coords=None, box_coords=None, input_labels=None, borders=True):\n",
+ " for i, (mask, score) in enumerate(zip(masks, scores)):\n",
+ " plt.figure(figsize=(10, 10))\n",
+ " plt.imshow(image)\n",
+ " show_mask(mask, plt.gca(), borders=borders)\n",
+ " if point_coords is not None:\n",
+ " assert input_labels is not None\n",
+ " show_points(point_coords, input_labels, plt.gca())\n",
+ " if box_coords is not None:\n",
+ " # boxes\n",
+ " show_box(box_coords, plt.gca())\n",
+ " if len(scores) > 1:\n",
+ " plt.title(f\"Mask {i+1}, Score: {score:.3f}\", fontsize=18)\n",
+ " plt.axis('off')\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "23842fb2",
+ "metadata": {},
+ "source": [
+ "## Example image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3c2e4f6b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image = Image.open(f\"{sam3_root}/assets/images/truck.jpg\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e30125fd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.imshow(image)\n",
+ "plt.axis('on')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "98b228b8",
+ "metadata": {},
+ "source": [
+ "## Selecting objects with SAM 3"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bb1927b",
+ "metadata": {},
+ "source": [
+ "First, load the SAM 3 model. Running on CUDA and using the default model are recommended for best results."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e28150b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sam3 import build_sam3_image_model\n",
+ "from sam3.model.sam3_image_processor import Sam3Processor\n",
+ "\n",
+ "bpe_path = f\"{sam3_root}/assets/bpe_simple_vocab_16e6.txt.gz\"\n",
+ "model = build_sam3_image_model(bpe_path=bpe_path, enable_inst_interactivity=True)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c925e829",
+ "metadata": {},
+ "source": [
+ "Process the image to produce an image embedding by calling `Sam3Processor.set_image`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d95d48dd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "processor = Sam3Processor(model)\n",
+ "inference_state = processor.set_image(image)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d8fc7a46",
+ "metadata": {},
+ "source": [
+ "To select the truck, choose a point on it. Points are input to the model in (x,y) format and come with labels 1 (foreground point) or 0 (background point). Multiple points can be input; here we use only one. The chosen point will be shown as a star on the image."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5c69570c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "input_point = np.array([[520, 375]])\n",
+ "input_label = np.array([1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a91ba973",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.imshow(image)\n",
+ "show_points(input_point, input_label, plt.gca())\n",
+ "plt.axis('on')\n",
+ "plt.show() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c765e952",
+ "metadata": {},
+ "source": [
+ "Predict with `SAM3Image.predict_inst`. The model returns masks, quality predictions for those masks, and low resolution mask logits that can be passed to the next iteration of prediction."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5373fd68",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "masks, scores, logits = model.predict_inst(\n",
+ " inference_state,\n",
+ " point_coords=input_point,\n",
+ " point_labels=input_label,\n",
+ " multimask_output=True,\n",
+ ")\n",
+ "sorted_ind = np.argsort(scores)[::-1]\n",
+ "masks = masks[sorted_ind]\n",
+ "scores = scores[sorted_ind]\n",
+ "logits = logits[sorted_ind]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c7f0e938",
+ "metadata": {},
+ "source": [
+ "With `multimask_output=True` (the default setting), SAM 3 outputs 3 masks, where `scores` gives the model's own estimation of the quality of these masks. This setting is intended for ambiguous input prompts, and helps the model disambiguate different objects consistent with the prompt. When `False`, it will return a single mask. For ambiguous prompts such as a single point, it is recommended to use `multimask_output=True` even if only a single mask is desired; the best single mask can be chosen by picking the one with the highest score returned in `scores`. This will often result in a better mask."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "47821187",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "masks.shape # (number_of_masks) x H x W"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9c227a6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "show_masks(image, masks, scores, point_coords=input_point, input_labels=input_label, borders=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3fa31f7c",
+ "metadata": {},
+ "source": [
+ "## Specifying a specific object with additional points"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "88d6d29a",
+ "metadata": {},
+ "source": [
+ "The single input point is ambiguous, and the model has returned multiple objects consistent with it. To obtain a single object, multiple points can be provided. If available, a mask from a previous iteration can also be supplied to the model to aid in prediction. When specifying a single object with multiple prompts, a single mask can be requested by setting `multimask_output=False`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f6923b94",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "input_point = np.array([[500, 375], [1125, 625]])\n",
+ "input_label = np.array([1, 1])\n",
+ "\n",
+ "mask_input = logits[np.argmax(scores), :, :] # Choose the model's best mask"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d98f96a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "masks, scores, _ = model.predict_inst(\n",
+ " inference_state,\n",
+ " point_coords=input_point,\n",
+ " point_labels=input_label,\n",
+ " mask_input=mask_input[None, :, :],\n",
+ " multimask_output=False,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0ce8b82f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "masks.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e06d5c8d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "show_masks(image, masks, scores, point_coords=input_point, input_labels=input_label)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c93e2087",
+ "metadata": {},
+ "source": [
+ "To exclude the car and specify just the window, a background point (with label 0, here shown in red) can be supplied."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9a196f68",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "input_point = np.array([[500, 375], [1125, 625]])\n",
+ "input_label = np.array([1, 0])\n",
+ "\n",
+ "mask_input = logits[np.argmax(scores), :, :] # Choose the model's best mask"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "81a52282",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "masks, scores, _ = model.predict_inst(\n",
+ " inference_state,\n",
+ " point_coords=input_point,\n",
+ " point_labels=input_label,\n",
+ " mask_input=mask_input[None, :, :],\n",
+ " multimask_output=False,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bfca709f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "show_masks(image, masks, scores, point_coords=input_point, input_labels=input_label)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "41e2d5a9",
+ "metadata": {},
+ "source": [
+ "## Specifying a specific object with a box"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d61ca7ac",
+ "metadata": {},
+ "source": [
+ "The model can also take a box as input, provided in xyxy format."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ea92a7b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "input_box = np.array([425, 600, 700, 875])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b35a8814",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "masks, scores, _ = model.predict_inst(\n",
+ " inference_state,\n",
+ " point_coords=None,\n",
+ " point_labels=None,\n",
+ " box=input_box[None, :],\n",
+ " multimask_output=False,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3ffb4906",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "show_masks(image, masks, scores, box_coords=input_box)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c1ed9f0a",
+ "metadata": {},
+ "source": [
+ "## Combining points and boxes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8455d1c5",
+ "metadata": {},
+ "source": [
+ "Points and boxes may be combined, just by including both types of prompts to the predictor. Here this can be used to select just the trucks's tire, instead of the entire wheel."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "90e2e547",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "input_box = np.array([425, 600, 700, 875])\n",
+ "input_point = np.array([[575, 750]])\n",
+ "input_label = np.array([0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6956d8c4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "masks, scores, logits = model.predict_inst(\n",
+ " inference_state,\n",
+ " point_coords=input_point,\n",
+ " point_labels=input_label,\n",
+ " box=input_box,\n",
+ " multimask_output=False,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eb519a31",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "show_masks(image, masks, scores, box_coords=input_box, point_coords=input_point, input_labels=input_label)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "45ddbca3",
+ "metadata": {},
+ "source": [
+ "## Batched prompt inputs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "df6f18a0",
+ "metadata": {},
+ "source": [
+ "`SAM3Image` can take multiple input prompts for the same image, using `predict_inst` method. For example, imagine we have several box outputs from an object detector."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0a06681b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "input_boxes = np.array([\n",
+ " [75, 275, 1725, 850],\n",
+ " [425, 600, 700, 875],\n",
+ " [1375, 550, 1650, 800],\n",
+ " [1240, 675, 1400, 750],\n",
+ "])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "117521a3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "masks, scores, _ = model.predict_inst(\n",
+ " inference_state,\n",
+ " point_coords=None,\n",
+ " point_labels=None,\n",
+ " box=input_boxes,\n",
+ " multimask_output=False,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6a8f5d49",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "masks.shape # (batch_size) x (num_predicted_masks_per_input) x H x W"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c00c3681",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.imshow(image)\n",
+ "for mask in masks:\n",
+ " show_mask(mask.squeeze(0), plt.gca(), random_color=True)\n",
+ "for box in input_boxes:\n",
+ " show_box(box, plt.gca())\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b9a27b5d",
+ "metadata": {},
+ "source": [
+ "## End-to-end batched inference\n",
+ "If all prompts are available in advance, it is possible to run SAM 3 directly in an end-to-end fashion. This also allows batching over images."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d485f75b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image1 = image # truck.jpg from above\n",
+ "image1_boxes = np.array([\n",
+ " [75, 275, 1725, 850],\n",
+ " [425, 600, 700, 875],\n",
+ " [1375, 550, 1650, 800],\n",
+ " [1240, 675, 1400, 750],\n",
+ "])\n",
+ "\n",
+ "image2 = Image.open(f\"{sam3_root}/assets/images/groceries.jpg\")\n",
+ "image2_boxes = np.array([\n",
+ " [450, 170, 520, 350],\n",
+ " [350, 190, 450, 350],\n",
+ " [500, 170, 580, 350],\n",
+ " [580, 170, 640, 350],\n",
+ "])\n",
+ "\n",
+ "img_batch = [image1, image2]\n",
+ "boxes_batch = [image1_boxes, image2_boxes]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "47932c99",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "inference_state = processor.set_image_batch(img_batch)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "97af3c54",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "masks_batch, scores_batch, _ = model.predict_inst_batch(\n",
+ " inference_state,\n",
+ " None,\n",
+ " None, \n",
+ " box_batch=boxes_batch, \n",
+ " multimask_output=False\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "226df881",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for image, boxes, masks in zip(img_batch, boxes_batch, masks_batch):\n",
+ " plt.figure(figsize=(10, 10))\n",
+ " plt.imshow(image) \n",
+ " for mask in masks:\n",
+ " show_mask(mask.squeeze(0), plt.gca(), random_color=True)\n",
+ " for box in boxes:\n",
+ " show_box(box, plt.gca())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "46f30085",
+ "metadata": {},
+ "source": [
+ "Similarly, we can have a batch of point prompts defined over a batch of images"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1ab929fc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image1 = image # truck.jpg from above\n",
+ "image1_pts = np.array([\n",
+ " [[500, 375]],\n",
+ " [[650, 750]]\n",
+ " ]) # Bx1x2 where B corresponds to number of objects \n",
+ "image1_labels = np.array([[1], [1]])\n",
+ "\n",
+ "image2_pts = np.array([\n",
+ " [[400, 300]],\n",
+ " [[630, 300]],\n",
+ "])\n",
+ "image2_labels = np.array([[1], [1]])\n",
+ "\n",
+ "pts_batch = [image1_pts, image2_pts]\n",
+ "labels_batch = [image1_labels, image2_labels]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "848f8287",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "masks_batch, scores_batch, _ = model.predict_inst_batch(inference_state, pts_batch, labels_batch, box_batch=None, multimask_output=True)\n",
+ "\n",
+ "# Select the best single mask per object\n",
+ "best_masks = []\n",
+ "for masks, scores in zip(masks_batch,scores_batch):\n",
+ " best_masks.append(masks[range(len(masks)), np.argmax(scores, axis=-1)])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "99b15c6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for image, points, labels, masks in zip(img_batch, pts_batch, labels_batch, best_masks):\n",
+ " plt.figure(figsize=(10, 10))\n",
+ " plt.imshow(image) \n",
+ " for mask in masks:\n",
+ " show_mask(mask, plt.gca(), random_color=True)\n",
+ " show_points(points, labels, plt.gca())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4c1594a5-a0de-4477-91d4-db4504a78a83",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "74e3d07e-b0de-48a5-9d29-d639a0dbcdfc",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d8b1de3a-a253-48ff-8a1c-d80742acbe86",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/examples/sam3_for_sam2_video_task_example.ipynb b/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/examples/sam3_for_sam2_video_task_example.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..49569d5c86d30dad75216df4695ac797888b8eb2
--- /dev/null
+++ b/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/examples/sam3_for_sam2_video_task_example.ipynb
@@ -0,0 +1,979 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3c3b1c46-9f5c-41c1-9101-85db8709ec0d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Copyright (c) Meta Platforms, Inc. and affiliates."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6e7a0db5-7f04-4845-8b11-684fe6e9f7f2",
+ "metadata": {},
+ "source": [
+ "# Video object segmentation with SAM 3"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "162d0b3c-4207-442d-969c-aa1cbb8fd4ad",
+ "metadata": {},
+ "source": [
+ "This notebook shows how to use SAM 3 for video object segmentation in videos, illustrating the use of the `Sam3TrackerPredictor` class.\n",
+ "\n",
+ "\n",
+ "This notebook follows the SAM 2 API for interactive video segmentation.\n",
+ "\n",
+ "\n",
+ " \n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "26616201-06df-435b-98fd-ad17c373bb4a",
+ "metadata": {},
+ "source": [
+ "## Environment Set-up"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8491a127-4c01-48f5-9dc5-f148a9417fdf",
+ "metadata": {},
+ "source": [
+ "First install `sam3` in your environment using the [installation instructions](https://github.com/facebookresearch/sam3?tab=readme-ov-file#installation) in the repository."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f74c53be-aab1-46b9-8c0b-068b52ef5948",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "using_colab = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d824a4b2-71f3-4da3-bfc7-3249625e6730",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if using_colab:\n",
+ " import torch\n",
+ " import torchvision\n",
+ " print(\"PyTorch version:\", torch.__version__)\n",
+ " print(\"Torchvision version:\", torchvision.__version__)\n",
+ " print(\"CUDA is available:\", torch.cuda.is_available())\n",
+ " import sys\n",
+ " !{sys.executable} -m pip install opencv-python matplotlib scikit-learn\n",
+ " !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/sam3.git'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "22e6aa9d-487f-4207-b657-8cff0902343e",
+ "metadata": {},
+ "source": [
+ "## Set-up"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d3cae821",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "\n",
+ "# select the device for computation\n",
+ "if torch.cuda.is_available():\n",
+ " device = torch.device(\"cuda\")\n",
+ "elif torch.backends.mps.is_available():\n",
+ " device = torch.device(\"mps\")\n",
+ "else:\n",
+ " device = torch.device(\"cpu\")\n",
+ "print(f\"using device: {device}\")\n",
+ "\n",
+ "if device.type == \"cuda\":\n",
+ " # use bfloat16 for the entire notebook\n",
+ " torch.autocast(\"cuda\", dtype=torch.bfloat16).__enter__()\n",
+ " # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)\n",
+ " if torch.cuda.get_device_properties(0).major >= 8:\n",
+ " torch.backends.cuda.matmul.allow_tf32 = True\n",
+ " torch.backends.cudnn.allow_tf32 = True\n",
+ "\n",
+ "elif device.type == \"mps\":\n",
+ " print(\n",
+ " \"\\nSupport for MPS devices is preliminary. SAM 3 is trained with CUDA and might \"\n",
+ " \"give numerically different outputs and sometimes degraded performance on MPS. \"\n",
+ " \"See e.g. https://github.com/pytorch/pytorch/issues/84936 for a discussion.\"\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e5318a85-5bf7-4880-b2b3-15e4db24d796",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import glob\n",
+ "import os\n",
+ "\n",
+ "import cv2\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "import sam3\n",
+ "import torch\n",
+ "from PIL import Image\n",
+ "from sam3.visualization_utils import show_box, show_mask, show_points\n",
+ "\n",
+ "# font size for axes titles\n",
+ "plt.rcParams[\"axes.titlesize\"] = 12\n",
+ "plt.rcParams[\"figure.titlesize\"] = 12\n",
+ "\n",
+ "sam3_root = os.path.join(os.path.dirname(sam3.__file__), \"..\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ae8e0779-751f-4224-9b04-ed0f0b406500",
+ "metadata": {},
+ "source": [
+ "### Loading the SAM 3 tracking predictor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f5f3245e-b4d6-418b-a42a-a67e0b3b5aec",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sam3.model_builder import build_sam3_video_model\n",
+ "\n",
+ "sam3_model = build_sam3_video_model()\n",
+ "predictor = sam3_model.tracker\n",
+ "predictor.backbone = sam3_model.detector.backbone"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dff46b10-c17a-4a26-8004-8c6d80806b0a",
+ "metadata": {},
+ "source": [
+ "#### Initialize the inference state"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f594ac71-a6b9-461d-af27-500fa1d1a420",
+ "metadata": {},
+ "source": [
+ "Just like SAM 2, SAM 3 requires stateful inference for interactive video segmentation, so we need to initialize an **inference state** on this video.\n",
+ "\n",
+ "During initialization, it loads all the JPEG frames in `video_path` and stores their pixels in `inference_state` (as shown in the progress bar below)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9baa05c9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "video_path = f\"{sam3_root}/assets/videos/bedroom.mp4\"\n",
+ "inference_state = predictor.init_state(video_path=video_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "edb1f3f6-d74d-4016-934c-8d2a14d1a543",
+ "metadata": {},
+ "source": [
+ "### Example 1: Segment & track one object"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aa2d3127-67b2-45d2-9f32-8fe3e10dc5eb",
+ "metadata": {},
+ "source": [
+ "Note: if you have run any previous tracking using this `inference_state`, please reset it first via `clear_all_points_in_video`.\n",
+ "\n",
+ "(The cell below is just for illustration; it's not needed to call `clear_all_points_in_video` here as this `inference_state` is just freshly initialized above.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d2646a1d-3401-438c-a653-55e0e56b7d9d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "predictor.clear_all_points_in_video(inference_state)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "26aeb04d-8cba-4f57-95da-6e5a1796003e",
+ "metadata": {},
+ "source": [
+ "#### Step 1: Add a first click on a frame"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "695c7749-b523-4691-aad0-7558c5d1d68c",
+ "metadata": {},
+ "source": [
+ "To get started, let's try to segment the child on the left.\n",
+ "\n",
+ "Here we make a **positive click** at (x, y) = (210, 350) with label `1`, by sending their coordinates and labels into the `add_new_points` API.\n",
+ "\n",
+ "Note: label `1` indicates a *positive click (to add a region)* while label `0` indicates a *negative click (to remove a region)*."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6778a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load the frames for visualization\n",
+ "cap = cv2.VideoCapture(video_path)\n",
+ "video_frames_for_vis = []\n",
+ "while True:\n",
+ " ret, frame = cap.read()\n",
+ " if not ret:\n",
+ " break\n",
+ " video_frames_for_vis.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
+ "cap.release()\n",
+ "frame0 = video_frames_for_vis[0]\n",
+ "\n",
+ "width, height = frame0.shape[1], frame0.shape[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3e749bab-0f36-4173-bf8d-0c20cd5214b3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ann_frame_idx = 0 # the frame index we interact with\n",
+ "ann_obj_id = 1 # give a unique id to each object we interact with (it can be any integers)\n",
+ "\n",
+ "# Let's add a positive click at (x, y) = (210, 350) to get started\n",
+ "points = np.array([[210, 350]], dtype=np.float32)\n",
+ "# for labels, `1` means positive click and `0` means negative click\n",
+ "labels = np.array([1], np.int32)\n",
+ "\n",
+ "rel_points = [[x / width, y / height] for x, y in points]\n",
+ "\n",
+ "points_tensor = torch.tensor(rel_points, dtype=torch.float32)\n",
+ "points_labels_tensor = torch.tensor(labels, dtype=torch.int32)\n",
+ "\n",
+ "_, out_obj_ids, low_res_masks, video_res_masks = predictor.add_new_points(\n",
+ " inference_state=inference_state,\n",
+ " frame_idx=ann_frame_idx,\n",
+ " obj_id=ann_obj_id,\n",
+ " points=points_tensor,\n",
+ " labels=points_labels_tensor,\n",
+ " clear_old_points=False,\n",
+ ")\n",
+ "\n",
+ "# show the results on the current (interacted) frame\n",
+ "plt.figure(figsize=(9, 6))\n",
+ "plt.title(f\"frame {ann_frame_idx}\")\n",
+ "plt.imshow(frame0)\n",
+ "show_points(points, labels, plt.gca())\n",
+ "show_mask((video_res_masks[0] > 0.0).cpu().numpy(), plt.gca(), obj_id=out_obj_ids[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "89457875-93fa-40ed-b6dc-4e1c971a27f9",
+ "metadata": {},
+ "source": [
+ "#### Step 2: Add a second click to refine the prediction"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a75eb21b-1413-452c-827b-a04093c30c78",
+ "metadata": {},
+ "source": [
+ "Hmm, it seems that although we wanted to segment the child on the left, the model predicts the mask for only the shorts -- this can happen since there is ambiguity from a single click about what the target object should be. We can refine the mask on this frame via another positive click on the child's shirt.\n",
+ "\n",
+ "Here we make a **second positive click** at (x, y) = (250, 220) with label `1` to expand the mask.\n",
+ "\n",
+ "Note: we need to send **all the clicks and their labels** (i.e. not just the last click) when calling `add_new_points`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1ab3ec7-2537-4158-bf98-3d0977d8908d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ann_frame_idx = 0 # the frame index we interact with\n",
+ "ann_obj_id = 1 # give a unique id to each object we interact with (it can be any integers)\n",
+ "\n",
+ "# Let's add a 2nd positive click at (x, y) = (250, 220) to refine the mask\n",
+ "# sending all clicks (and their labels) to `add_new_points_or_box`\n",
+ "points = np.array([[210, 350], [250, 220]], dtype=np.float32)\n",
+ "# for labels, `1` means positive click and `0` means negative click\n",
+ "labels = np.array([1, 1], np.int32)\n",
+ "\n",
+ "rel_points = [[x / width, y / height] for x, y in points]\n",
+ "\n",
+ "points_tensor = torch.tensor(rel_points, dtype=torch.float32)\n",
+ "points_labels_tensor = torch.tensor(labels, dtype=torch.int32)\n",
+ "\n",
+ "_, out_obj_ids, low_res_masks, video_res_masks = predictor.add_new_points(\n",
+ " inference_state=inference_state,\n",
+ " frame_idx=ann_frame_idx,\n",
+ " obj_id=ann_obj_id,\n",
+ " points=points_tensor,\n",
+ " labels=points_labels_tensor,\n",
+ " clear_old_points=False,\n",
+ ")\n",
+ "\n",
+ "# show the results on the current (interacted) frame\n",
+ "plt.figure(figsize=(9, 6))\n",
+ "plt.title(f\"frame {ann_frame_idx}\")\n",
+ "plt.imshow(frame0)\n",
+ "show_points(points, labels, plt.gca())\n",
+ "show_mask((video_res_masks[0] > 0.0).cpu().numpy(), plt.gca(), obj_id=out_obj_ids[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "df4ab457-d91d-4ac8-b350-fbcd549fd3fd",
+ "metadata": {},
+ "source": [
+ "With this 2nd refinement click, now we get a segmentation mask of the entire child on frame 0."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f52015ac-1b7b-4c59-bca3-c2b28484cf46",
+ "metadata": {},
+ "source": [
+ "#### Step 3: Propagate the prompts to get the masklet across the video"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "30b025bd-cd58-4bfb-9572-c8d2fd0a02ef",
+ "metadata": {},
+ "source": [
+ "To get the masklet throughout the entire video, we propagate the prompts using the `propagate_in_video` API."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab45e932-b0d5-4983-9718-6ee77d1ac31b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# run propagation throughout the video and collect the results in a dict\n",
+ "video_segments = {} # video_segments contains the per-frame segmentation results\n",
+ "for frame_idx, obj_ids, low_res_masks, video_res_masks, obj_scores in predictor.propagate_in_video(inference_state, start_frame_idx=0, max_frame_num_to_track=240, reverse=False, propagate_preflight=True):\n",
+ " video_segments[frame_idx] = {\n",
+ " out_obj_id: (video_res_masks[i] > 0.0).cpu().numpy()\n",
+ " for i, out_obj_id in enumerate(out_obj_ids)\n",
+ " }\n",
+ "\n",
+ "# render the segmentation results every few frames\n",
+ "vis_frame_stride = 30\n",
+ "plt.close(\"all\")\n",
+ "for out_frame_idx in range(0, len(video_frames_for_vis), vis_frame_stride):\n",
+ " plt.figure(figsize=(6, 4))\n",
+ " plt.title(f\"frame {out_frame_idx}\")\n",
+ " plt.imshow(video_frames_for_vis[out_frame_idx])\n",
+ " for out_obj_id, out_mask in video_segments[out_frame_idx].items():\n",
+ " show_mask(out_mask, plt.gca(), obj_id=out_obj_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3e801b70-72df-4a72-b3fe-84f145e5e3f6",
+ "metadata": {},
+ "source": [
+ "#### Step 4: Add new prompts to further refine the masklet"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "478958ab-29b4-4a75-bba4-adb1b03d0a2b",
+ "metadata": {},
+ "source": [
+ "It appears that in the output masklet above, there are some small imperfections in boundary details on frame 150.\n",
+ "\n",
+ "With SAM 3 we can fix the model predictions interactively. We can add a **negative click** at (x, y) = (82, 415) on this frame with label `0` to refine the masklet. Here we call the `add_new_points_or_box` API with a different `frame_idx` argument to indicate the frame index we want to refine."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1a572ea9-5b7e-479c-b30c-93c38b121131",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ann_frame_idx = 150 # further refine some details on this frame\n",
+ "ann_obj_id = 1 # give a unique id to the object we interact with (it can be any integers)\n",
+ "\n",
+ "# show the segment before further refinement\n",
+ "plt.figure(figsize=(9, 6))\n",
+ "plt.title(f\"frame {ann_frame_idx} -- before refinement\")\n",
+ "plt.imshow(video_frames_for_vis[ann_frame_idx])\n",
+ "show_mask(video_segments[ann_frame_idx][ann_obj_id], plt.gca(), obj_id=ann_obj_id)\n",
+ "\n",
+ "# Let's add a negative click on this frame at (x, y) = (82, 415) to refine the segment\n",
+ "points = np.array([[82, 410]], dtype=np.float32)\n",
+ "# for labels, `1` means positive click and `0` means negative click\n",
+ "labels = np.array([0], np.int32)\n",
+ "\n",
+ "rel_points = [[x / width, y / height] for x, y in points]\n",
+ "\n",
+ "points_tensor = torch.tensor(rel_points, dtype=torch.float32)\n",
+ "points_labels_tensor = torch.tensor(labels, dtype=torch.int32)\n",
+ "\n",
+ "_, out_obj_ids, low_res_masks, video_res_masks = predictor.add_new_points(\n",
+ " inference_state=inference_state,\n",
+ " frame_idx=ann_frame_idx,\n",
+ " obj_id=ann_obj_id,\n",
+ " points=points_tensor,\n",
+ " labels=points_labels_tensor,\n",
+ " clear_old_points=False,\n",
+ ")\n",
+ "\n",
+ "\n",
+ "# show the segment after the further refinement\n",
+ "plt.figure(figsize=(9, 6))\n",
+ "plt.title(f\"frame {ann_frame_idx} -- after refinement\")\n",
+ "plt.imshow(video_frames_for_vis[ann_frame_idx])\n",
+ "show_points(points, labels, plt.gca())\n",
+ "show_mask((video_res_masks > 0.0).cpu().numpy(), plt.gca(), obj_id=ann_obj_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "50a3950a-acf1-435c-bd64-94297267b5e9",
+ "metadata": {},
+ "source": [
+ "#### Step 5: Propagate the prompts (again) to get the masklet across the video"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b1954ecf-c2ec-4f9c-8d10-c4f527a10cd2",
+ "metadata": {},
+ "source": [
+ "Let's get an updated masklet for the entire video. Here we call `propagate_in_video` again to propagate all the prompts after adding the new refinement click above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "baa96690-4a38-4a24-aa17-fd2f4db0e232",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# run propagation throughout the video and collect the results in a dict\n",
+ "video_segments = {} # video_segments contains the per-frame segmentation results\n",
+ "for frame_idx, obj_ids, low_res_masks, video_res_masks, obj_scores in predictor.propagate_in_video(inference_state, start_frame_idx=0, max_frame_num_to_track=300, reverse=False, propagate_preflight=True):\n",
+ " video_segments[frame_idx] = {\n",
+ " out_obj_id: (video_res_masks[i] > 0.0).cpu().numpy()\n",
+ " for i, out_obj_id in enumerate(out_obj_ids)\n",
+ " }\n",
+ "\n",
+ "# render the segmentation results every few frames\n",
+ "vis_frame_stride = 30\n",
+ "plt.close(\"all\")\n",
+ "for out_frame_idx in range(0, len(video_frames_for_vis), vis_frame_stride):\n",
+ " plt.figure(figsize=(6, 4))\n",
+ " plt.title(f\"frame {out_frame_idx}\")\n",
+ " plt.imshow(video_frames_for_vis[out_frame_idx])\n",
+ " for out_obj_id, out_mask in video_segments[out_frame_idx].items():\n",
+ " show_mask(out_mask, plt.gca(), obj_id=out_obj_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "607507e3-6a2b-4fd7-944c-2371bdab9d01",
+ "metadata": {},
+ "source": [
+ "The segments now look good on all frames."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2502bb5a-3e1f-43d0-9f58-33f8676fff0d",
+ "metadata": {},
+ "source": [
+ "### Example 2: Segment an object using box prompt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8e2d26c8-0432-48c6-997e-4a3b77bb5f6d",
+ "metadata": {},
+ "source": [
+ "Note: if you have run any previous tracking using this `inference_state`, please reset it first via `clear_all_points_in_video`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6dbe9183-abbb-4283-b0cb-d24f3d7beb34",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "predictor.clear_all_points_in_video(inference_state)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ceb6eae9-0f4c-434f-8089-a46c9ca59da5",
+ "metadata": {},
+ "source": [
+ "In addition to using clicks as inputs, SAM 3 also supports segmenting and tracking objects in a video via **bounding boxes**.\n",
+ "\n",
+ "In the example below, we segment the child on the right using a **box prompt** of (x_min, y_min, x_max, y_max) = (300, 0, 500, 400) on frame 0 as input into the `add_new_points_or_box` API."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1cbfb273-4e14-495b-bd89-87a8baf52ae7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ann_frame_idx = 0 # the frame index we interact with\n",
+ "ann_obj_id = 4 # give a unique id to each object we interact with (it can be any integers)\n",
+ "\n",
+ "# Let's add a box at (x_min, y_min, x_max, y_max) = (300, 0, 500, 400) to get started\n",
+ "box = np.array([[300, 0, 500, 400]], dtype=np.float32)\n",
+ "\n",
+ "rel_box = [[xmin / width, ymin / height, xmax / width, ymax / height] for xmin, ymin, xmax, ymax in box]\n",
+ "rel_box = np.array(rel_box, dtype=np.float32)\n",
+ "\n",
+ "_, out_obj_ids, low_res_masks, video_res_masks = predictor.add_new_points_or_box(\n",
+ " inference_state=inference_state,\n",
+ " frame_idx=ann_frame_idx,\n",
+ " obj_id=ann_obj_id,\n",
+ " box=rel_box,\n",
+ ")\n",
+ "\n",
+ "# show the results on the current (interacted) frame\n",
+ "plt.figure(figsize=(9, 6))\n",
+ "plt.title(f\"frame {ann_frame_idx}\")\n",
+ "plt.imshow(video_frames_for_vis[ann_frame_idx])\n",
+ "show_box(box[0], plt.gca())\n",
+ "show_mask((video_res_masks[0] > 0.0).cpu().numpy(), plt.gca(), obj_id=ann_obj_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bd3f9ba7-bf4d-47e5-9b02-8a424cab42cc",
+ "metadata": {},
+ "source": [
+ "Here, SAM 3 gets a pretty good segmentation mask of the entire child, even though the input bounding box is not perfectly tight around the object.\n",
+ "\n",
+ "Similar to the previous example, if the returned mask from is not perfect when using a box prompt, we can also further **refine** the output using positive or negative clicks. To illustrate this, here we make a **positive click** at (x, y) = (460, 60) with label `1` to expand the segment around the child's hair.\n",
+ "\n",
+ "Note: to refine the segmentation mask from a box prompt, we need to send **both the original box input and all subsequent refinement clicks and their labels** when calling `add_new_points_or_box`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "54906315-ab4c-4088-b866-4c22134d5b66",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ann_frame_idx = 0 # the frame index we interact with\n",
+ "ann_obj_id = 4 # give a unique id to each object we interact with (it can be any integers)\n",
+ "\n",
+ "# Let's add a positive click at (x, y) = (460, 60) to refine the mask\n",
+ "points = np.array([[460, 60]], dtype=np.float32)\n",
+ "# for labels, `1` means positive click and `0` means negative click\n",
+ "labels = np.array([1], np.int32)\n",
+ "# note that we also need to send the original box input along with\n",
+ "# the new refinement click together into `add_new_points_or_box`\n",
+ "box = np.array([[300, 0, 500, 400]], dtype=np.float32)\n",
+ "\n",
+ "rel_box = [[xmin / width, ymin / height, xmax / width, ymax / height] for xmin, ymin, xmax, ymax in box]\n",
+ "rel_box = np.array(rel_box, dtype=np.float32)\n",
+ "\n",
+ "rel_points = [[x / width, y / height] for x, y in points]\n",
+ "\n",
+ "points_tensor = torch.tensor(rel_points, dtype=torch.float32)\n",
+ "points_labels_tensor = torch.tensor(labels, dtype=torch.int32)\n",
+ "\n",
+ "_, out_obj_ids, low_res_masks, video_res_masks = predictor.add_new_points_or_box(\n",
+ " inference_state=inference_state,\n",
+ " frame_idx=ann_frame_idx,\n",
+ " obj_id=ann_obj_id,\n",
+ " points=points_tensor,\n",
+ " labels=points_labels_tensor,\n",
+ " box=rel_box,\n",
+ ")\n",
+ "\n",
+ "# show the results on the current (interacted) frame\n",
+ "plt.figure(figsize=(9, 6))\n",
+ "plt.title(f\"frame {ann_frame_idx}\")\n",
+ "plt.imshow(video_frames_for_vis[ann_frame_idx])\n",
+ "show_box(box[0], plt.gca())\n",
+ "show_points(points, labels, plt.gca())\n",
+ "show_mask((video_res_masks[0][0] > 0.0).cpu().numpy(), plt.gca(), obj_id=out_obj_ids[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "73128cd6-dbfa-49f7-8d79-1a8e19835f7f",
+ "metadata": {},
+ "source": [
+ "Then, to get the masklet throughout the entire video, we propagate the prompts using the `propagate_in_video` API."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9cd90557-a0dc-442e-b091-9c74c831bef8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# run propagation throughout the video and collect the results in a dict\n",
+ "video_segments = {} # video_segments contains the per-frame segmentation results\n",
+ "for frame_idx, obj_ids, low_res_masks, video_res_masks, obj_scores in predictor.propagate_in_video(inference_state, start_frame_idx=0, max_frame_num_to_track=300, reverse=False, propagate_preflight=True):\n",
+ " video_segments[frame_idx] = {\n",
+ " out_obj_id: (video_res_masks[i] > 0.0).cpu().numpy()\n",
+ " for i, out_obj_id in enumerate(out_obj_ids)\n",
+ " }\n",
+ "\n",
+ "# render the segmentation results every few frames\n",
+ "vis_frame_stride = 30\n",
+ "plt.close(\"all\")\n",
+ "for out_frame_idx in range(0, len(video_frames_for_vis), vis_frame_stride):\n",
+ " plt.figure(figsize=(6, 4))\n",
+ " plt.title(f\"frame {out_frame_idx}\")\n",
+ " plt.imshow(video_frames_for_vis[out_frame_idx])\n",
+ " for out_obj_id, out_mask in video_segments[out_frame_idx].items():\n",
+ " show_mask(out_mask, plt.gca(), obj_id=out_obj_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e023f91f-0cc5-4980-ae8e-a13c5749112b",
+ "metadata": {},
+ "source": [
+ "Note that in addition to clicks or boxes, SAM 3 also supports directly using a **mask prompt** as input via the `add_new_mask` method in the `Sam3TrackerPredictor` class. This can be helpful in e.g. semi-supervised VOS evaluations (see [tools/vos_inference.py](https://github.com/facebookresearch/sam2/blob/main/tools/vos_inference.py) for an example)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "da018be8-a4ae-4943-b1ff-702c2b89cb68",
+ "metadata": {},
+ "source": [
+ "### Example 3: Segment multiple objects simultaneously"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dea6c04c-3072-4876-b394-879321a48c4a",
+ "metadata": {},
+ "source": [
+ "Note: if you have run any previous tracking using this `inference_state`, please reset it first via `clear_all_points_in_video`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "29b874c8-9f39-42d3-a667-54a0bd696410",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "predictor.clear_all_points_in_video(inference_state)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "48f3f7e6-4821-468c-84e4-f3a0435c9149",
+ "metadata": {},
+ "source": [
+ "#### Step 1: Add two objects on a frame"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "95158714-86d7-48a9-8365-b213f97cc9ca",
+ "metadata": {},
+ "source": [
+ "SAM 3 can also segment and track two or more objects at the same time. One way, of course, is to do them one by one. However, it would be more efficient to batch them together (e.g. so that we can share the image features between objects to reduce computation costs).\n",
+ "\n",
+ "This time, let's focus on object parts and segment **the shirts of both childen** in this video. Here we add prompts for these two objects and assign each of them a unique object id."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e22d896d-3cd5-4fa0-9230-f33e217035dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "prompts = {} # hold all the clicks we add for visualization"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "59d9ac57-b14a-4237-828d-927e422c518b",
+ "metadata": {},
+ "source": [
+ "Add the first object (the left child's shirt) with a **positive click** at (x, y) = (200, 300) on frame 0.\n",
+ "\n",
+ "We assign it to object id `2` (it can be arbitrary integers, and only needs to be unique for each object to track), which is passed to the `add_new_points_or_box` API to distinguish the object we are clicking upon."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d13432fc-f467-44d8-adfe-3e0c488046b7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ann_frame_idx = 0 # the frame index we interact with\n",
+ "ann_obj_id = 2 # give a unique id to each object we interact with (it can be any integers)\n",
+ "\n",
+ "# Let's add a positive click at (x, y) = (200, 300) to get started on the first object\n",
+ "points = np.array([[200, 300]], dtype=np.float32)\n",
+ "# for labels, `1` means positive click and `0` means negative click\n",
+ "labels = np.array([1], np.int32)\n",
+ "prompts[ann_obj_id] = points, labels\n",
+ "\n",
+ "rel_points = [[x / width, y / height] for x, y in points]\n",
+ "points_tensor = torch.tensor(rel_points, dtype=torch.float32)\n",
+ "points_labels_tensor = torch.tensor(labels, dtype=torch.int32)\n",
+ "\n",
+ "_, out_obj_ids, low_res_masks, video_res_masks = predictor.add_new_points_or_box(\n",
+ " inference_state=inference_state,\n",
+ " frame_idx=ann_frame_idx,\n",
+ " obj_id=ann_obj_id,\n",
+ " points=points_tensor,\n",
+ " labels=points_labels_tensor,\n",
+ ")\n",
+ "\n",
+ "\n",
+ "# show the results on the current (interacted) frame\n",
+ "plt.figure(figsize=(9, 6))\n",
+ "plt.title(f\"frame {ann_frame_idx}\")\n",
+ "plt.imshow(video_frames_for_vis[ann_frame_idx])\n",
+ "for i, out_obj_id in enumerate(out_obj_ids):\n",
+ " show_points(points, labels, plt.gca())\n",
+ " show_points(*prompts[out_obj_id], plt.gca())\n",
+ " show_mask((video_res_masks[i][0] > 0.0).cpu().numpy(), plt.gca(), obj_id=out_obj_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1bbbd51b-e1e2-4c36-99ec-1d9a1b49b0cd",
+ "metadata": {},
+ "source": [
+ "Hmm, this time we just want to select the child's shirt, but the model predicts the mask for the entire child. Let's refine the prediction with a **negative click** at (x, y) = (275, 175)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "95ecf61d-662b-4f98-ae62-46557b219842",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add the first object\n",
+ "ann_frame_idx = 0 # the frame index we interact with\n",
+ "ann_obj_id = 2 # give a unique id to each object we interact with (it can be any integers)\n",
+ "\n",
+ "# Let's add a 2nd negative click at (x, y) = (275, 175) to refine the first object\n",
+ "# sending all clicks (and their labels) to `add_new_points_or_box`\n",
+ "points = np.array([[200, 300], [275, 175]], dtype=np.float32)\n",
+ "# for labels, `1` means positive click and `0` means negative click\n",
+ "labels = np.array([1, 0], np.int32)\n",
+ "prompts[ann_obj_id] = points, labels\n",
+ "\n",
+ "rel_points = [[x / width, y / height] for x, y in points]\n",
+ "points_tensor = torch.tensor(rel_points, dtype=torch.float32)\n",
+ "points_labels_tensor = torch.tensor(labels, dtype=torch.int32)\n",
+ "\n",
+ "\n",
+ "_, out_obj_ids, low_res_masks, video_res_masks = predictor.add_new_points_or_box(\n",
+ " inference_state=inference_state,\n",
+ " frame_idx=ann_frame_idx,\n",
+ " obj_id=ann_obj_id,\n",
+ " points=rel_points,\n",
+ " labels=points_labels_tensor,\n",
+ ")\n",
+ "\n",
+ "# show the results on the current (interacted) frame\n",
+ "plt.figure(figsize=(9, 6))\n",
+ "plt.title(f\"frame {ann_frame_idx}\")\n",
+ "plt.imshow(video_frames_for_vis[ann_frame_idx])\n",
+ "for i, out_obj_id in enumerate(out_obj_ids):\n",
+ " show_points(points, labels, plt.gca())\n",
+ " show_points(*prompts[out_obj_id], plt.gca())\n",
+ " show_mask((video_res_masks[i][0] > 0.0).cpu().numpy(), plt.gca(), obj_id=out_obj_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "194718c1-734d-446c-a3ef-361057de2f31",
+ "metadata": {},
+ "source": [
+ "After the 2nd negative click, now we get the left child's shirt as our first object.\n",
+ "\n",
+ "Let's move on to the second object (the right child's shirt) with a positive click at (x, y) = (400, 150) on frame 0. Here we assign object id `3` to this second object (it can be arbitrary integers, and only needs to be unique for each object to track).\n",
+ "\n",
+ "Note: when there are multiple objects, the `add_new_points_or_box` API will return a list of masks for each object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "86ca1bde-62a4-40e6-98e4-15606441e52f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ann_frame_idx = 0 # the frame index we interact with\n",
+ "ann_obj_id = 3 # give a unique id to each object we interact with (it can be any integers)\n",
+ "\n",
+ "# Let's now move on to the second object we want to track (giving it object id `3`)\n",
+ "# with a positive click at (x, y) = (400, 150)\n",
+ "points = np.array([[400, 150]], dtype=np.float32)\n",
+ "# for labels, `1` means positive click and `0` means negative click\n",
+ "labels = np.array([1], np.int32)\n",
+ "prompts[ann_obj_id] = points, labels\n",
+ "\n",
+ "rel_points = [[x / width, y / height] for x, y in points]\n",
+ "points_tensor = torch.tensor(rel_points, dtype=torch.float32)\n",
+ "points_labels_tensor = torch.tensor(labels, dtype=torch.int32)\n",
+ "\n",
+ "\n",
+ "# `add_new_points_or_box` returns masks for all objects added so far on this interacted frame\n",
+ "_, out_obj_ids, low_res_masks, video_res_masks = predictor.add_new_points_or_box(\n",
+ " inference_state=inference_state,\n",
+ " frame_idx=ann_frame_idx,\n",
+ " obj_id=ann_obj_id,\n",
+ " points=points_tensor,\n",
+ " labels=points_labels_tensor,\n",
+ ")\n",
+ "\n",
+ "# show the results on the current (interacted) frame on all objects\n",
+ "plt.figure(figsize=(9, 6))\n",
+ "plt.title(f\"frame {ann_frame_idx}\")\n",
+ "plt.imshow(video_frames_for_vis[ann_frame_idx])\n",
+ "for i, out_obj_id in enumerate(out_obj_ids):\n",
+ " show_points(points, labels, plt.gca())\n",
+ " show_points(*prompts[out_obj_id], plt.gca())\n",
+ " show_mask((video_res_masks[i][0] > 0.0).cpu().numpy(), plt.gca(), obj_id=out_obj_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a1f7add8-d577-4597-ae2f-654b8c7b05e0",
+ "metadata": {},
+ "source": [
+ "This time the model predicts the mask of the shirt we want to track in just one click. Nice!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "448733b8-ea8b-4078-995f-b676c3b558ba",
+ "metadata": {},
+ "source": [
+ "#### Step 2: Propagate the prompts to get masklets across the video"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "60bd73de-d669-41c8-b6ba-943883f0caa2",
+ "metadata": {},
+ "source": [
+ "Now, we propagate the prompts for both objects to get their masklets throughout the video.\n",
+ "\n",
+ "Note: when there are multiple objects, the `propagate_in_video` API will return a list of masks for each object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "17737191-d62b-4611-b2c6-6d0418a9ab74",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# run propagation throughout the video and collect the results in a dict\n",
+ "video_segments = {} # video_segments contains the per-frame segmentation results\n",
+ "for frame_idx, obj_ids, low_res_masks, video_res_masks, obj_scores in predictor.propagate_in_video(inference_state, start_frame_idx=0, max_frame_num_to_track=300, reverse=False, propagate_preflight=True):\n",
+ " video_segments[frame_idx] = {\n",
+ " out_obj_id: (video_res_masks[i] > 0.0).cpu().numpy()\n",
+ " for i, out_obj_id in enumerate(out_obj_ids)\n",
+ " }\n",
+ "\n",
+ "# render the segmentation results every few frames\n",
+ "vis_frame_stride = 30\n",
+ "plt.close(\"all\")\n",
+ "for out_frame_idx in range(0, len(video_frames_for_vis), vis_frame_stride):\n",
+ " plt.figure(figsize=(6, 4))\n",
+ " plt.title(f\"frame {out_frame_idx}\")\n",
+ " plt.imshow(video_frames_for_vis[out_frame_idx])\n",
+ " for out_obj_id, out_mask in video_segments[out_frame_idx].items():\n",
+ " show_mask(out_mask, plt.gca(), obj_id=out_obj_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "18a0b9d7-c78f-432b-afb0-11f2ea5b652a",
+ "metadata": {},
+ "source": [
+ "Looks like both children's shirts are well segmented in this video.\n",
+ "\n",
+ "Now you can try SAM 3 on your own videos and use cases! "
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/examples/sam3_image_batched_inference.ipynb b/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/examples/sam3_image_batched_inference.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..dddbfd8390f6cdda75bdf71c6dca058ecd576754
--- /dev/null
+++ b/workspace/02_Code_Intelligence/task_2_sam3_debug/exec/sam3/examples/sam3_image_batched_inference.ipynb
@@ -0,0 +1,672 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Copyright (c) Meta Platforms, Inc. and affiliates."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# \n",
+ "# \n",
+ "# "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "using_colab = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if using_colab:\n",
+ " import torch\n",
+ " import torchvision\n",
+ " print(\"PyTorch version:\", torch.__version__)\n",
+ " print(\"Torchvision version:\", torchvision.__version__)\n",
+ " print(\"CUDA is available:\", torch.cuda.is_available())\n",
+ " import sys\n",
+ " !{sys.executable} -m pip install opencv-python matplotlib scikit-learn\n",
+ " !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/sam3.git'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "output": {
+ "id": 1373952277654085,
+ "loadingStatus": "loaded"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from PIL import Image\n",
+ "import requests\n",
+ "from io import BytesIO\n",
+ "import sam3\n",
+ "from sam3.train.data.collator import collate_fn_api as collate\n",
+ "from sam3.model.utils.misc import copy_data_to_device\n",
+ "import os\n",
+ "sam3_root = os.path.join(os.path.dirname(sam3.__file__), \"..\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "# turn on tfloat32 for Ampere GPUs\n",
+ "# https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n",
+ "torch.backends.cuda.matmul.allow_tf32 = True\n",
+ "torch.backends.cudnn.allow_tf32 = True\n",
+ "\n",
+ "# use bfloat16 for the entire notebook. If your card doesn't support it, try float16 instead\n",
+ "torch.autocast(\"cuda\", dtype=torch.bfloat16).__enter__()\n",
+ "\n",
+ "# inference mode for the whole notebook. Disable if you need gradients\n",
+ "torch.inference_mode().__enter__()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Utilities"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plotting"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This section contains simple utilities to plot masks and bounding masks on top of an image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "output": {
+ "id": 819341201047004,
+ "loadingStatus": "loaded"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "\n",
+ "sys.path.append(f\"{sam3_root}/examples\")\n",
+ "\n",
+ "from sam3.visualization_utils import plot_results"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Batching"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This section contains some utility functions to create datapoints. They are optional, but give some good indication on how they should be created"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sam3.train.data.sam3_image_dataset import InferenceMetadata, FindQueryLoaded, Image as SAMImage, Datapoint\n",
+ "from typing import List\n",
+ "\n",
+ "GLOBAL_COUNTER = 1\n",
+ "def create_empty_datapoint():\n",
+ " \"\"\" A datapoint is a single image on which we can apply several queries at once. \"\"\"\n",
+ " return Datapoint(find_queries=[], images=[])\n",
+ "\n",
+ "def set_image(datapoint, pil_image):\n",
+ " \"\"\" Add the image to be processed to the datapoint \"\"\"\n",
+ " w,h = pil_image.size\n",
+ " datapoint.images = [SAMImage(data=pil_image, objects=[], size=[h,w])]\n",
+ "\n",
+ "def add_text_prompt(datapoint, text_query):\n",
+ " \"\"\" Add a text query to the datapoint \"\"\"\n",
+ "\n",
+ " global GLOBAL_COUNTER\n",
+ " # in this function, we require that the image is already set.\n",
+ " # that's because we'll get its size to figure out what dimension to resize masks and boxes\n",
+ " # In practice you're free to set any size you want, just edit the rest of the function\n",
+ " assert len(datapoint.images) == 1, \"please set the image first\"\n",
+ "\n",
+ " w, h = datapoint.images[0].size\n",
+ " datapoint.find_queries.append(\n",
+ " FindQueryLoaded(\n",
+ " query_text=text_query,\n",
+ " image_id=0,\n",
+ " object_ids_output=[], # unused for inference\n",
+ " is_exhaustive=True, # unused for inference\n",
+ " query_processing_order=0,\n",
+ " inference_metadata=InferenceMetadata(\n",
+ " coco_image_id=GLOBAL_COUNTER,\n",
+ " original_image_id=GLOBAL_COUNTER,\n",
+ " original_category_id=1,\n",
+ " original_size=[w, h],\n",
+ " object_id=0,\n",
+ " frame_index=0,\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " GLOBAL_COUNTER += 1\n",
+ " return GLOBAL_COUNTER - 1\n",
+ "\n",
+ "def add_visual_prompt(datapoint, boxes:List[List[float]], labels:List[bool], text_prompt=\"visual\"):\n",
+ " \"\"\" Add a visual query to the datapoint.\n",
+ " The bboxes are expected in XYXY format (top left and bottom right corners)\n",
+ " For each bbox, we expect a label (true or false). The model tries to find boxes that ressemble the positive ones while avoiding the negative ones\n",
+ " We can also give a text_prompt as an additional hint. It's not mandatory, leave it to \"visual\" if you want the model to solely rely on the boxes.\n",
+ "\n",
+ " Note that the model expects the prompt to be consistent. If the text reads \"elephant\" but the provided boxe points to a dog, the results will be undefined.\n",
+ " \"\"\"\n",
+ "\n",
+ " global GLOBAL_COUNTER\n",
+ " # in this function, we require that the image is already set.\n",
+ " # that's because we'll get its size to figure out what dimension to resize masks and boxes\n",
+ " # In practice you're free to set any size you want, just edit the rest of the function\n",
+ " assert len(datapoint.images) == 1, \"please set the image first\"\n",
+ " assert len(boxes) > 0, \"please provide at least one box\"\n",
+ " assert len(boxes) == len(labels), f\"Expecting one label per box. Found {len(boxes)} boxes but {len(labels)} labels\"\n",
+ " for b in boxes:\n",
+ " assert len(b) == 4, f\"Boxes must have 4 coordinates, found {len(b)}\"\n",
+ "\n",
+ " labels = torch.tensor(labels, dtype=torch.bool).view(-1)\n",
+ " if not labels.any().item() and text_prompt==\"visual\":\n",
+ " print(\"Warning: you provided no positive box, nor any text prompt. The prompt is ambiguous and the results will be undefined\")\n",
+ " w, h = datapoint.images[0].size\n",
+ " datapoint.find_queries.append(\n",
+ " FindQueryLoaded(\n",
+ " query_text=text_prompt,\n",
+ " image_id=0,\n",
+ " object_ids_output=[], # unused for inference\n",
+ " is_exhaustive=True, # unused for inference\n",
+ " query_processing_order=0,\n",
+ " input_bbox=torch.tensor(boxes, dtype=torch.float).view(-1,4),\n",
+ " input_bbox_label=labels,\n",
+ " inference_metadata=InferenceMetadata(\n",
+ " coco_image_id=GLOBAL_COUNTER,\n",
+ " original_image_id=GLOBAL_COUNTER,\n",
+ " original_category_id=1,\n",
+ " original_size=[w, h],\n",
+ " object_id=0,\n",
+ " frame_index=0,\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " GLOBAL_COUNTER += 1\n",
+ " return GLOBAL_COUNTER - 1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Loading"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First we load our model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sam3 import build_sam3_image_model\n",
+ "\n",
+ "bpe_path = f\"{sam3_root}/assets/bpe_simple_vocab_16e6.txt.gz\"\n",
+ "model = build_sam3_image_model(bpe_path=bpe_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then our validation transforms"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sam3.train.transforms.basic_for_api import ComposeAPI, RandomResizeAPI, ToTensorAPI, NormalizeAPI\n",
+ "\n",
+ "from sam3.model.position_encoding import PositionEmbeddingSine\n",
+ "transform = ComposeAPI(\n",
+ " transforms=[\n",
+ " RandomResizeAPI(sizes=1008, max_size=1008, square=True, consistent_transform=False),\n",
+ " ToTensorAPI(),\n",
+ " NormalizeAPI(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),\n",
+ " ]\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And finally our postprocessor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sam3.eval.postprocessors import PostProcessImage\n",
+ "postprocessor = PostProcessImage(\n",
+ " max_dets_per_img=-1, # if this number is positive, the processor will return topk. For this demo we instead limit by confidence, see below\n",
+ " iou_type=\"segm\", # we want masks\n",
+ " use_original_sizes_box=True, # our boxes should be resized to the image size\n",
+ " use_original_sizes_mask=True, # our masks should be resized to the image size\n",
+ " convert_mask_to_rle=False, # the postprocessor supports efficient conversion to RLE format. In this demo we prefer the binary format for easy plotting\n",
+ " detection_threshold=0.5, # Only return confident detections\n",
+ " to_cpu=False,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Inference"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For inference, we proceed as follows:\n",
+ "- Create each datapoint one by one, using the functions above. Each query that we make will give us a unique id, which is then used after post-processing to retrieve the results\n",
+ "- Each datapoint must be transformed according to are pre-processing transforms (basically resize to 1008x1008, normalize)\n",
+ "- We then collate all datapoints into a batch and forward it to the model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Image 1, we'll use two text prompts\n",
+ "\n",
+ "img1 = Image.open(BytesIO(requests.get(\"http://images.cocodataset.org/val2017/000000077595.jpg\").content))\n",
+ "datapoint1 = create_empty_datapoint()\n",
+ "set_image(datapoint1, img1)\n",
+ "id1 = add_text_prompt(datapoint1, \"cat\")\n",
+ "id2 = add_text_prompt(datapoint1, \"laptop\")\n",
+ "\n",
+ "datapoint1 = transform(datapoint1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Image 2, one text prompt, some visual prompt\n",
+ "img2 = Image.open(BytesIO(requests.get(\"http://images.cocodataset.org/val2017/000000136466.jpg\").content))\n",
+ "\n",
+ "# img2 = Image.open(f\"{sam3_root}/assets/images/test_image.jpg\")\n",
+ "datapoint2 = create_empty_datapoint()\n",
+ "set_image(datapoint2, img2)\n",
+ "id3 = add_text_prompt(datapoint2, \"pot\")\n",
+ "# we trying to find the dials on the oven. Let's give a positive box\n",
+ "id4 = add_visual_prompt(datapoint2, boxes=[[ 59, 144, 76, 163]], labels=[True])\n",
+ "# Let's also get the oven start/stop button\n",
+ "id5 = add_visual_prompt(datapoint2, boxes=[[ 59, 144, 76, 163],[ 87, 148, 104, 159]], labels=[True, True])\n",
+ "# Next, let's try to find the pot handles. With the text prompt \"handle\" (vague on purpose), the model also finds the oven's handles\n",
+ "# We could make the text query more precise (try it!) but for this example, we instead want to leverage a negative prompt\n",
+ "# First, let's see what happens with just the text prompt\n",
+ "id6 = add_text_prompt(datapoint2, \"handle\")\n",
+ "# now the same but adding the negative prompt\n",
+ "id7 = add_visual_prompt(datapoint2, boxes=[[ 40, 183, 318, 204]], labels=[False], text_prompt=\"handle\")\n",
+ "\n",
+ "datapoint2 = transform(datapoint2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Collate then move to cuda\n",
+ "batch = collate([datapoint1, datapoint2], dict_key=\"dummy\")[\"dummy\"]\n",
+ "batch = copy_data_to_device(batch, torch.device(\"cuda\"), non_blocking=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/haithamkhedr/.conda/envs/sam3/lib/python3.12/site-packages/torch/_inductor/lowering.py:1917: UserWarning: Torchinductor does not support code generation for complex operators. Performance may be worse than eager.\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Forward. Note that the first forward will be very slow due to compilation\n",
+ "output = model(batch)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "processed_results = postprocessor.process_results(output, batch.find_metadatas)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Plotting"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "output": {
+ "id": 3372746802877191,
+ "loadingStatus": "loaded"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "found 1 object(s)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "\n",
+ "# "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "8517f5f6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "using_colab = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "2540e376",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if using_colab:\n",
+ " import torch\n",
+ " import torchvision\n",
+ " print(\"PyTorch version:\", torch.__version__)\n",
+ " print(\"Torchvision version:\", torchvision.__version__)\n",
+ " print(\"CUDA is available:\", torch.cuda.is_available())\n",
+ " import sys\n",
+ " !{sys.executable} -m pip install opencv-python matplotlib scikit-learn\n",
+ " !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/sam3.git'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "90073483-58f6-404e-90ac-c22efcd76216",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib widget"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "13325376-658b-48d6-8528-2a006f223d44",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "# turn on tfloat32 for Ampere GPUs\n",
+ "# https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n",
+ "torch.backends.cuda.matmul.allow_tf32 = True\n",
+ "torch.backends.cudnn.allow_tf32 = True\n",
+ "\n",
+ "# use bfloat16 for the entire notebook. If your card doesn't support it, try float16 instead\n",
+ "torch.autocast(\"cuda\", dtype=torch.bfloat16).__enter__()\n",
+ "\n",
+ "# inference mode for the whole notebook. Disable if you need gradients\n",
+ "torch.inference_mode().__enter__()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fb863772-56a9-4ee2-be52-5d8933066519",
+ "metadata": {},
+ "source": [
+ "# Load the model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "f84b4ccc-9db2-4d88-ac8f-4c272694d25a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sam3\n",
+ "from sam3 import build_sam3_image_model\n",
+ "import os\n",
+ "sam3_root = os.path.join(os.path.dirname(sam3.__file__), \"..\")\n",
+ "bpe_path = f\"{sam3_root}/assets/bpe_simple_vocab_16e6.txt.gz\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "de01a36e-1221-4497-a5ab-e6c796689480",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = build_sam3_image_model(bpe_path=bpe_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "b01ec8a9-d9f6-4baf-96ac-1e5d21fd90b8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sam3.model.sam3_image_processor import Sam3Processor\n",
+ "processor = Sam3Processor(model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e6172a69-35ca-487c-bd67-6f1f1ecb20d5",
+ "metadata": {},
+ "source": [
+ "# Jupyter widget"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "2a4ac22f-5d5c-4272-a5a1-dfe0c04253a7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import io\n",
+ "\n",
+ "import ipywidgets as widgets\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import PIL.Image\n",
+ "import requests\n",
+ "from IPython.display import clear_output, display, HTML\n",
+ "from matplotlib.patches import Rectangle\n",
+ "\n",
+ "\n",
+ "class Sam3SegmentationWidget:\n",
+ " \"\"\"Interactive Jupyter widget for SAM3 segmentation with text and box prompts.\"\"\"\n",
+ "\n",
+ " def __init__(self, processor):\n",
+ " \"\"\"\n",
+ " Initialize the segmentation widget.\n",
+ "\n",
+ " Args:\n",
+ " processor: Sam3Processor instance\n",
+ " \"\"\"\n",
+ " self.processor = processor\n",
+ " self.state = None\n",
+ " self.current_image = None\n",
+ " self.current_image_array = None\n",
+ " self.box_mode = \"positive\"\n",
+ " self.drawing_box = False\n",
+ " self.box_start = None\n",
+ " self.current_rect = None\n",
+ "\n",
+ " self._setup_ui()\n",
+ " self._setup_plot()\n",
+ "\n",
+ " def _setup_ui(self):\n",
+ " \"\"\"Set up the UI components.\"\"\"\n",
+ " self.upload_widget = widgets.FileUpload(\n",
+ " accept=\"image/*\", multiple=False, description=\"Upload Image\"\n",
+ " )\n",
+ " self.upload_widget.observe(self._on_image_upload, names=\"value\")\n",
+ "\n",
+ " self.url_input = widgets.Text(\n",
+ " placeholder=\"Or enter image URL\",\n",
+ " )\n",
+ " self.url_button = widgets.Button(description=\"Load URL\", button_style=\"info\")\n",
+ " self.url_button.on_click(self._on_load_url)\n",
+ " url_box = widgets.HBox(\n",
+ " [self.url_input, self.url_button],\n",
+ " layout=widgets.Layout(width=\"100%\", justify_content=\"space-between\"),\n",
+ " )\n",
+ "\n",
+ " self.text_input = widgets.Text(\n",
+ " placeholder='Enter segmentation prompt (e.g., \"person\", \"dog\")',\n",
+ " continuous_update=False,\n",
+ " )\n",
+ " self.text_input.observe(self._on_text_submit, names=\"value\")\n",
+ " self.text_button = widgets.Button(description=\"Segment\", button_style=\"success\")\n",
+ " self.text_button.on_click(self._on_text_prompt)\n",
+ " text_box = widgets.HBox(\n",
+ " [self.text_input, self.text_button],\n",
+ " layout=widgets.Layout(width=\"100%\", justify_content=\"space-between\"),\n",
+ " )\n",
+ "\n",
+ " self.box_mode_buttons = widgets.ToggleButtons(\n",
+ " options=[\"Positive Boxes\", \"Negative Boxes\"],\n",
+ " description=\"Box Mode:\",\n",
+ " button_style=\"\",\n",
+ " tooltips=[\n",
+ " \"Draw boxes around objects to include\",\n",
+ " \"Draw boxes around objects to exclude\",\n",
+ " ],\n",
+ " )\n",
+ " self.box_mode_buttons.observe(self._on_box_mode_change, names=\"value\")\n",
+ "\n",
+ " self.clear_button = widgets.Button(\n",
+ " description=\"Clear All Prompts\", button_style=\"warning\"\n",
+ " )\n",
+ " self.clear_button.on_click(self._on_clear_prompts)\n",
+ "\n",
+ " self.confidence_slider = widgets.FloatSlider(\n",
+ " value=0.5,\n",
+ " min=0.0,\n",
+ " max=1.0,\n",
+ " step=0.01,\n",
+ " description=\"Confidence:\",\n",
+ " continuous_update=False,\n",
+ " style={\"description_width\": \"initial\"},\n",
+ " )\n",
+ " self.confidence_slider.observe(self._on_confidence_change, names=\"value\")\n",
+ "\n",
+ " self.size_slider = widgets.IntSlider(\n",
+ " value=960,\n",
+ " min=300,\n",
+ " max=2000,\n",
+ " step=10,\n",
+ " description=\"Image Size:\",\n",
+ " continuous_update=False,\n",
+ " style={\"description_width\": \"initial\"},\n",
+ " )\n",
+ " self.size_slider.observe(self._on_size_change, names=\"value\")\n",
+ "\n",
+ " slider_box = widgets.HBox(\n",
+ " [self.confidence_slider, self.size_slider],\n",
+ " layout=widgets.Layout(justify_content=\"space-between\"),\n",
+ " )\n",
+ "\n",
+ " self.output = widgets.Output()\n",
+ " self.status_label = widgets.Label(value=\"Upload an image to begin\")\n",
+ "\n",
+ " # This box will hold our matplotlib output and we can target it with CSS.\n",
+ " self.plot_container = widgets.Box([self.output])\n",
+ " self.plot_container.add_class(\"no-drag\")\n",
+ "\n",
+ " # CSS to make the cursor a crosshair over the matplotlib canvas\n",
+ " css_style = widgets.HTML(\n",
+ " \"\"\"\n",
+ " \n",
+ " \"\"\"\n",
+ " )\n",
+ " # Create VBoxes for each accordion pane\n",
+ " source_pane = widgets.VBox([self.upload_widget, url_box])\n",
+ " prompt_pane = widgets.VBox(\n",
+ " [\n",
+ " widgets.Label(\"Text Prompt:\"),\n",
+ " text_box,\n",
+ " self.box_mode_buttons,\n",
+ " self.confidence_slider,\n",
+ " self.clear_button,\n",
+ " ]\n",
+ " )\n",
+ " display_pane = widgets.VBox([self.size_slider])\n",
+ "\n",
+ " # Create the Accordion to hold the control panes\n",
+ " self.accordion = widgets.Accordion(\n",
+ " children=[source_pane, prompt_pane, display_pane]\n",
+ " )\n",
+ " self.accordion.set_title(0, \"Image Source\")\n",
+ " self.accordion.set_title(1, \"Segmentation Prompts\")\n",
+ " self.accordion.set_title(2, \"Display Settings\")\n",
+ " self.accordion.selected_index = 0 # Start with the first pane open\n",
+ "\n",
+ " # Create the left sidebar for controls\n",
+ " sidebar = widgets.VBox(\n",
+ " [self.status_label, widgets.HTML(\"