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CITATION.bib

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+@dataset{zeredata_binpicking_2026,
+  author = {Umit Kavala},
+  title = {ZereData Bin Picking Dataset v1.0},
+  year = {2026},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/datasets/zeredata/bin-picking-v1}
+}

LICENSE

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+Attribution 4.0 International (CC BY 4.0)
+
+By exercising the Licensed Rights (defined below), You accept and agree to be
+bound by the terms and conditions of this Creative Commons Attribution 4.0
+International Public License ("Public License"). To the extent this Public
+License may be interpreted as a contract, You are granted the Licensed Rights
+in consideration of Your acceptance of these terms and conditions, and the
+Licensor grants You such rights in consideration of benefits the Licensor
+receives from making the Licensed Material available under these terms and
+conditions.
+
+The full legal text is available at:
+  https://creativecommons.org/licenses/by/4.0/legalcode
+
+Summary of rights:
+  - Share — copy and redistribute the material in any medium or format.
+  - Adapt — remix, transform, and build upon the material for any purpose,
+    even commercially.
+  - Attribution — You must give appropriate credit, provide a link to the
+    license, and indicate if changes were made.
+
+No warranties are given.

README.md

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+---
+pretty_name: ZereData Bin Picking Dataset v1.0
+license: cc-by-4.0
+task_categories:
+  - object-detection
+  - image-segmentation
+size_categories:
+  - 10K<n<100K
+tags:
+  - synthetic-data
+  - bin-picking
+  - robotics
+  - 6d-pose
+  - pose-estimation
+  - depth-estimation
+  - instance-segmentation
+  - warehouse
+  - coco
+  - yolo
+  - bop
+  - pbr
+  - computer-vision
+language:
+  - en
+---
+
+# ZereData Bin Picking Dataset v1.0
+
+![license](https://img.shields.io/badge/license-CC--BY--4.0-blue) ![scenes](https://img.shields.io/badge/scenes-10,000-green) ![size](https://img.shields.io/badge/size-14.8GB-orange)
+
+Synthetic training data for robotic bin picking — RGB, depth, instance masks, 6D pose, 2D bounding boxes, and per-instance visibility, in BOP/COCO/YOLO formats.
+
+![preview](preview/0001_scene_0002.png) ![preview](preview/0002_scene_4637.png) ![preview](preview/0003_scene_0013.png) ![preview](preview/0004_scene_0004.png)
+
+## Overview
+
+Generated via physically-based ray tracing in Blender Cycles, this dataset delivers dense, photorealistic scenes of cluttered bins at warehouse scale. Each scene includes RGB, 32-bit depth, instance segmentation, camera intrinsics/extrinsics, and per-instance 6D pose with visibility ratios.
+
+The dataset's value is simple: synthetic renders give perfect ground truth annotations impossible to obtain from real cameras, at a scale and cost real-world collection cannot match. Use it to train 6D pose estimators, bin-picking grasp predictors, and warehouse perception systems — then validate sim-to-real transfer on smaller real-world test sets.
+
+## Dataset Statistics
+
+| Metric | Value |
+|--------|-------|
+| Total scenes | 10,000 |
+| Train split | 8,000 |
+| Val split | 2,000 |
+| Resolution | 1280x720 |
+| Object instances | 345,404 |
+| Object categories | 7 |
+| Modalities | 6 (RGB, depth, mask, pose, bboxes, visibility) |
+| Total size on disk | 14.8 GB |
+
+## Modalities
+
+- **RGB** — 1280×720 PNG per scene. The primary input for detection, segmentation, and pose models.
+- **Depth** — 32-bit EXR in metres. Train depth-conditioned pose models or use as a second-channel input.
+- **Instance mask** — colour-coded PNG per scene, one colour per object instance. Drives instance segmentation and occlusion reasoning.
+- **6D pose** — per-instance rotation and translation in camera frame (BOP `cam_R_m2c`, `cam_t_m2c`). Supervises pose regression heads.
+- **2D bounding boxes** — derived from masks, included in COCO and YOLO formats.
+- **Visibility ratio** — BOP `visib_fract` per instance; lets you weight the training loss by occlusion severity.
+
+## Formats
+
+### BOP (primary)
+Canonical BOP directory layout under `data/train/` and `data/val/`. Each scene folder contains `scene_camera.json` (`cam_K`, `depth_scale`), `scene_gt.json` (per-object `cam_R_m2c`, `cam_t_m2c`, `obj_id`), and `scene_gt_info.json` (`bbox_obj`, `bbox_visib`, `visib_fract`). Load with the BOP toolkit. Object IDs are **ZereData-specific, not BOP canonical** — see Limitations.
+
+### COCO
+Merged `annotations/coco_train.json` and `annotations/coco_val.json` with `images`, `annotations` (bboxes + masks), and `categories`. Loads cleanly with pycocotools:
+
+```python
+from pycocotools.coco import COCO
+coco = COCO('annotations/coco_train.json')
+```
+
+### YOLO
+Per-image `.txt` label files under `annotations/yolo_train/` and `yolo_val/`, with normalized `class_id cx cy w h` entries. Class IDs are consistent across both splits; see `annotations/yolo_classes.txt` and `annotations/yolo_data.yaml`.
+
+## Data Format
+
+This dataset is packaged as per-format zip archives, mirroring the [bop-benchmark](https://huggingface.co/bop-benchmark) HF layout convention (one zip per logical split) adapted for multi-format shipping. Loose files — README, LICENSE, CITATION, metadata.json, preview images — remain at the repository root so the HF dataset page renders a preview.
+
+| Archive | Contents | On-extract layout |
+|---|---|---|
+| `bin_picking_train_bop.zip` | BOP-format train split (rgb/depth/mask + `scene_camera.json` / `scene_gt.json` / `scene_gt_info.json` per scene) | `data/train/{000000..007999}/...` |
+| `bin_picking_val_bop.zip` | BOP-format val split | `data/val/{000000..001999}/...` |
+| `bin_picking_coco.zip` | `coco_train.json`, `coco_val.json` (merged, BOP obj IDs remapped to COCO categories) | `annotations/coco_*.json` |
+| `bin_picking_yolo.zip` | YOLO labels per split + `yolo_classes.txt` + `yolo_data.yaml` | `annotations/yolo_{train,val}/*.txt`, `annotations/yolo_*.{txt,yaml}` |
+| `bin_picking_native.zip` | Per-scene native annotations (full pre-export ZereData scene graph) | `annotations/scene_NNNN.json` |
+| `bin_picking_models.zip` | 27 GLB object models | `models/*.glb` |
+
+### Download and extract only what you need
+
+```python
+from huggingface_hub import hf_hub_download
+import zipfile
+
+REPO = 'zeredata/bin-picking-v1'
+# BOP train split
+p = hf_hub_download(repo_id=REPO, filename='bin_picking_train_bop.zip', repo_type='dataset')
+with zipfile.ZipFile(p) as z:
+    z.extractall('./zd_bp')   # rehydrates ./zd_bp/data/train/...
+```
+
+Or the whole dataset in one shot:
+
+```bash
+huggingface-cli download --repo-type dataset zeredata/bin-picking-v1 --local-dir ./zd_bp
+cd ./zd_bp && for z in bin_picking_*.zip; do unzip -q "$z"; done
+```
+
+All zip extractions share the same root-relative layout, so unzipping all six archives into one directory rehydrates the canonical flat tree.
+
+## Loading the Dataset
+
+These snippets assume you have already extracted the relevant zip(s) into a working directory (see **Data Format** above). Paths are relative to that root.
+
+### PyTorch Dataset over BOP structure
+```python
+from pathlib import Path
+from torch.utils.data import Dataset
+from PIL import Image
+import json
+
+class BopBinPicking(Dataset):
+    def __init__(self, root, split='train'):
+        # root must contain data/<split>/... (extract bin_picking_<split>_bop.zip there first)
+        self.scene_dirs = sorted((Path(root) / 'data' / split).iterdir())
+    def __len__(self):
+        return len(self.scene_dirs)
+    def __getitem__(self, idx):
+        sd = self.scene_dirs[idx]
+        rgb = Image.open(sd / 'rgb' / '000000.png')
+        gt = json.loads((sd / 'scene_gt.json').read_text())
+        cam = json.loads((sd / 'scene_camera.json').read_text())
+        return rgb, gt, cam
+```
+
+### COCO via pycocotools
+```python
+# After extracting bin_picking_coco.zip:
+from pycocotools.coco import COCO
+coco = COCO('annotations/coco_train.json')
+img_ids = coco.getImgIds()
+for ann in coco.loadAnns(coco.getAnnIds(imgIds=img_ids[0])):
+    print(ann['bbox'], ann['category_id'])
+```
+
+_A `datasets.load_dataset()` loader is planned for v1.1._
+
+## Intended Use
+
+Training 6D pose estimation models, bin-picking grasp models, and warehouse robotics perception systems. Synthetic data for sim-to-real transfer research.
+
+## Limitations and Known Issues
+
+- **Non-canonical BOP object IDs.** This release uses ZereData-specific object IDs. It is BOP-format-compatible but **not** a drop-in replacement for evaluation against BOP test sets (LM-O, YCB-V, T-LESS). A BOP-dataset-compatible release with canonical CAD models is forthcoming.
+- **Warehouse-specific lighting.** The three lighting profiles model warehouse conditions and may not transfer directly to outdoor, medical, or agricultural domains:
+    - `bin_picking_overhead` — bright fluorescent overhead panels, typical of distribution-center shelving aisles.
+    - `bin_picking_mixed` — mixed overhead + rim lighting with warmer colour temperature, mimicking older facilities with partial skylights.
+    - `studio` — lower-energy three-point studio setup, producing darker scenes useful as a poor-lighting proxy.
+  Each scene's `variety.lighting_profile` annotation tag records which profile was used.
+- **Procedural materials.** Material variation uses procedural textures, not photoscanned assets. High-frequency surface detail may look synthetic under close inspection.
+- **Geometric occlusion only.** No category-level occlusion modelling — occlusion is derived from geometry alone.
+- **Simulated camera intrinsics.** The intrinsic matrix is synthetic, not drawn from real sensor calibration.
+
+## Evaluation
+
+Benchmark evaluation on LM-O is forthcoming; see [ZereData](https://zeredata.com) for updates.
+
+## Comparison to Related Datasets
+
+HOPE, T-LESS, and YCB-Video are excellent real-world datasets with limited scale and fixed object sets. This dataset is synthetic-only, scales without bound, and supports customer-specific object libraries. Treat the two as complementary: real data for evaluation, synthetic data for training.
+
+## Citation
+
+```bibtex
+@dataset{zeredata_binpicking_2026,
+  author = {Umit Kavala},
+  title = {ZereData Bin Picking Dataset v1.0},
+  year = {2026},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/datasets/zeredata/bin-picking-v1}
+}
+```
+
+## License
+
+Released under [CC BY 4.0](LICENSE). Attribution required. Commercial use permitted.
+
+## Contact and Links
+
+- Website: [https://zeredata.com](https://zeredata.com)
+- Contact: [engineering@zeredata.com](mailto:engineering@zeredata.com)

bin_picking_coco.zip

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bin_picking_models.zip

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bin_picking_native.zip

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bin_picking_train_bop.zip

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bin_picking_val_bop.zip

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bin_picking_yolo.zip

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dataset_stats.json

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+{
+  "num_scenes": 10000,
+  "num_rgb": 10000,
+  "num_depth": 10000,
+  "num_segmentation": 10000,
+  "num_annotations": 10000,
+  "total_objects": 345404,
+  "categories": [
+    "bin",
+    "bottle",
+    "box",
+    "can",
+    "floor",
+    "pouch",
+    "shelf"
+  ],
+  "resolutions": [
+    [
+      1280,
+      720
+    ]
+  ],
+  "avg_quality_score": 0.0,
+  "source_dirs": [
+    "C:\\Projects\\zere-synth\\blender\\output\\bin_picking_v1_release_10k\\20260419_154937"
+  ]
+}

metadata.json

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+{
+  "name": "bin-picking-v1",
+  "version": "1.0.0",
+  "author": "Umit Kavala",
+  "release_date": "2026-04-25T19:45:01.458880+00:00",
+  "num_scenes_total": 10000,
+  "num_scenes_train": 8000,
+  "num_scenes_val": 2000,
+  "resolution": [
+    1280,
+    720
+  ],
+  "generation": {
+    "engine": "Blender Cycles",
+    "blender_version": "5.0.1",
+    "cycles_samples": 128,
+    "render_device": "GPU (CUDA)",
+    "avg_render_time_per_scene_seconds": 12.63
+  },
+  "objects": {
+    "count": 27,
+    "categories": {
+      "bottles": [
+        "beer_bottle_low_poly",
+        "bottle",
+        "bottle (1)",
+        "bottle (2)",
+        "coca_cola_bottle",
+        "glass_soda_bottle",
+        "liquid_detergent_bottle",
+        "scooberts_beer_bottle",
+        "simple_plastic_bottle",
+        "water_bottle",
+        "water_bottle_low_poly",
+        "water_bottle_prop"
+      ],
+      "boxes": [
+        "Box",
+        "Cardboard Boxes",
+        "Crate",
+        "Fruit Crate",
+        "cc0_free_cardboard_box",
+        "low_poly_wooden_crate",
+        "the_brown_cardboard_box"
+      ],
+      "cans": [
+        "Gas Can",
+        "metal_can",
+        "soda_can",
+        "soda_can (1)",
+        "tin_can"
+      ],
+      "pouches": [
+        "Bags",
+        "Coin Pouch",
+        "Sack Trench Small"
+      ]
+    },
+    "object_id_scheme": "zeredata_v1",
+    "bop_canonical_compatible": false
+  },
+  "category_map": {
+    "bottle": 1,
+    "box": 2,
+    "can": 3,
+    "pouch": 4
+  },
+  "scene_parameters": {
+    "objects_per_scene": [
+      20,
+      40
+    ],
+    "bin_size_range_meters": [
+      [
+        0.3,
+        0.3,
+        0.2
+      ],
+      [
+        0.6,
+        0.6,
+        0.4
+      ]
+    ],
+    "lighting_profiles": [
+      "bin_picking_overhead",
+      "bin_picking_mixed",
+      "studio"
+    ],
+    "bin_conditions": [
+      "new",
+      "scratched",
+      "damaged"
+    ]
+  },
+  "modalities": [
+    "rgb",
+    "depth",
+    "instance_mask",
+    "camera_intrinsics",
+    "camera_extrinsics",
+    "6d_pose",
+    "bbox_2d",
+    "visibility"
+  ],
+  "annotation_formats": [
+    "bop",
+    "coco",
+    "yolo"
+  ],
+  "random_seed": 42,
+  "generation_manifest_hash": "e9bfd71ea60a18222f0284b2612968185149b36b227c5c4e4840979bbc0134d0"
+}