simoswish
/

Person_Search_PRW

@@ -1,209 +1,227 @@
-# Detector Training, file: Detector_Training.ipynb
-## Overview
-This notebook presents a structured ablation study on the transferability of **YOLO26** to pedestrian detection on the **PRW** (Person Re-identification in the Wild) dataset.
-It constitutes the **detection stage** of a two-stage person search pipeline, where high recall is a primary design objective since any missed detection propagates as an unrecoverable error to the downstream Re-ID module.
-The study evaluates the impact of:
-- CrowdHuman intermediate pre-training vs. direct COCO initialisation
-- Full fine-tuning vs. partial fine-tuning (frozen backbone layers 0-9)
-- Model scale: YOLO26-Small vs. YOLO26-Large
-## Requirements
-### Platform
-The notebook was developed and executed on **Kaggle** with a **dual NVIDIA T4 GPU** configuration (16 GB VRAM each). Multi-GPU training is enabled by default via `cfg.use_multi_gpu = True`.
-To run on a single GPU or CPU, set this flag to `False` in the `Config` class.
-### Dependencies
-All required packages are installed in the first cell:
-- ultralytics, opencv-python-headless, scipy, pandas, tqdm
-### Input Datasets
-Before running the notebook, add the following two datasets as input sources in the Kaggle session (Notebook -> Input -> Add Input):
-| Dataset | Kaggle URL | Version |
-|---|---|---|
-| CrowdHuman | https://www.kaggle.com/datasets/leducnhuan/crowdhuman | 1 |
-| PRW -- Person Re-identification in the Wild | https://www.kaggle.com/datasets/edoardomerli/prw-person-re-identification-in-the-wild | 1 |
-The dataset paths are pre-configured in the `Config` class and match the default Kaggle mount locations. No manual path editing is required.
-## Notebook Structure
-| Phase | Description |
-|---|---|
-| 0 - Baseline | YOLO26-Small COCO zero-shot eval on PRW |
-| 1 - CrowdHuman Pre-training | Fine-tune Small on CrowdHuman, then eval on PRW |
-| 2 - Strategy Comparison | Full FT vs. Partial FT (freeze backbone layers 0-9) |
-| 3 - Scale-up | Best strategy applied to YOLO26-Large |
-| Final | Cross-model comparison: metrics, params, GFLOPs, speed |
-## Outputs
-All results are saved under `/kaggle/working/yolo_ablation/`:
-- `all_results.json` -- incremental results registry, persisted after each run
-- `all_results.csv` -- final summary table sorted by mAP@0.5
-- `plots/` -- all generated figures (bar charts, radar charts, training curves)
-Model checkpoints are saved under `/kaggle/working/yolo_runs/{run_name}/weights/`.
-## Key Results
-| Model | Strategy | mAP@0.5 (%) | Recall (%) | Params (M) | Latency (ms) |
-|---|---|---|---|---|---|
-| YOLO26-Large | Full FT | 96.24 | 91.38 | 26.2 | 27.88 |
-| YOLO26-Small | Full FT | 94.96 | 89.32 | 9.9 | 7.72 |
-| YOLO26-Small | Partial FT | 94.91 | 89.33 | 9.9 | 7.65 |
-| YOLO26-Small | Zero-shot (CH) | 88.23 | 83.41 | 9.9 | 6.85 |
-| YOLO26-Small | Zero-shot (COCO) | 85.82 | 79.42 | 10.0 | 6.29 |
-The Large model achieves the highest recall (91.38%) at the cost of 3.6x higher latency.
-The Small model with full or partial fine-tuning offers a competitive alternative for latency-constrained deployments, with recall above 89% at under 8 ms per image.
-## Reproducibility
-A global seed (42) is applied to Python, NumPy, PyTorch, and CUDA. All training cells are idempotent: if a valid checkpoint already exists, training is skipped and the existing weights are used.
-Results can be fully regenerated from saved checkpoints without re-running any training phase.
-## Estimated Runtime
-Full end-to-end execution (dataset conversion, all training phases, evaluation, and plotting) takes approximately **8-10 hours** on a Kaggle dual T4 session, depending on dataset I/O speed and early stopping behaviour.
-# Re-ID Training, file: ReIdentificator_Training.ipynb
-## Overview
-This notebook presents a structured ablation study on the fine-tuning of **PersonViT** for person re-identification on the **Person Re-identification in the Wild** (PRW) dataset.
-It constitutes the **Re-ID stage** of a two-stage person search pipeline, where the input is a set of pedestrian crops produced by the upstream YOLO26 detector and the output is an L2-normalised embedding used for cosine-similarity gallery retrieval.
-The study follows a **small-first, scale-up** design: the full ablation over fine-tuning strategies and loss functions is conducted on the lightweight **ViT-Small** (22 M parameters) to minimise GPU time, and only the winning configuration is then replicated on **ViT-Base** (86 M parameters). This reduces total compute by approximately 3x compared to running the ablation directly on ViT-Base, while preserving full causal interpretability of each experimental variable.
-The study evaluates the impact of:
-- Source domain selection (Duke, Market-1501, MSMT17, Occluded-Duke) in zero-shot evaluation
-- Fine-tuning strategy: Full FT vs. Partial FT vs. Freeze+Retrain
-- Metric learning loss function: TripletMarginLoss vs. ArcFaceLoss vs. NTXentLoss
-- Model scale: ViT-Small vs. ViT-Base
-## Requirements
-### Platform
-The notebook was developed and executed on **Kaggle** with a single **NVIDIA T4 GPU** (16 GB VRAM). Mixed-precision training (fp16) is enabled by default via `cfg.use_amp = True`.
-### Dependencies
-All required packages are installed in the first cell:
-- albumentations, opencv-python-headless, scipy
-- torchmetrics, timm, einops, yacs
-- pytorch-metric-learning
-- thop
-### Input Datasets and Models
-Before running the notebook, add the following dataset and model sources as inputs in the Kaggle session (Notebook -> Input -> Add Input):
-| Resource | Type | Kaggle URL | Version |
-|---|---|---|---|
-| PRW -- Person Re-identification in the Wild | Dataset | [https://www.kaggle.com/datasets/edoardomerli/prw-person-re-identification-in-the-wild](https://www.kaggle.com/datasets/edoardomerli/prw-person-re-identification-in-the-wild) | 1 |
-| PersonViT Pre-trained Weights | Model | [https://www.kaggle.com/models/simonerimondi/personvit](https://www.kaggle.com/models/simonerimondi/personvit) | 4 |
-The dataset and model paths are pre-configured in the `Config` class and match the default Kaggle mount locations. No manual path editing is required.
-The PersonViT model code is cloned at runtime from a personal GitHub fork ([github.com/simoswish02/PersonViT](https://github.com/simoswish02/PersonViT)) that fixes an import error present in the original upstream repository.
-## Notebook Structure
-| Phase | Description |
-|---|---|
-| 0 - Pretrained Baselines | Zero-shot evaluation of all four ViT-Small checkpoints on PRW |
-| 1 - Strategy Comparison | Full FT vs. Partial FT vs. Freeze+Retrain, ArcFace loss fixed (ViT-Small) |
-| 2 - Loss Comparison | TripletMarginLoss vs. ArcFaceLoss vs. NTXentLoss, best strategy fixed (ViT-Small) |
-| 3 - Scale-Up | Winning configuration replicated on ViT-Base (embedding dim 768) |
-| Final | Cross-model comparison: metrics, params, GFLOPs, latency |
-## Outputs
-All results are saved under `./evaluation_results/`:
-- `all_results.json` -- incremental results registry, persisted after each run
-- `all_results.csv` -- final summary table sorted by mAP
-- `plots/` -- all generated figures (bar charts, radar charts, training curves, Small vs. Base delta table)
-Model checkpoints are saved under `/kaggle/working/personvit_finetuning/`.
-## Key Results
-| Run | Strategy | Loss | mAP (%) | Rank-1 (%) | Params (M) | Latency (ms) |
-|---|---|---|---|---|---|---|
-| vit_base_full_triplet | Full FT | Triplet | 85.65 | 94.51 | 86.5 | 11.80 |
-| full_triplet | Full FT | Triplet | 81.50 | 93.44 | 22.0 | 7.02 |
-| full_ntxent | Full FT | NTXent | 80.77 | 93.15 | 22.0 | 7.47 |
-| full_arcface | Full FT | ArcFace | 78.10 | 93.39 | 22.0 | 7.34 |
-| freeze_arcface | Freeze+Retrain | ArcFace | 75.64 | 93.19 | 22.0 | 7.32 |
-| partial_arcface | Partial FT | ArcFace | 75.62 | 93.00 | 22.0 | 7.56 |
-| market1501 (zero-shot) | Pretrained | -- | 75.26 | 92.90 | 21.6 | 7.62 |
-ViT-Base with Full FT and TripletMarginLoss achieves the best mAP (85.65%) at the cost of 1.68x higher latency compared to the best ViT-Small run. ViT-Small with Full FT and TripletMarginLoss is the recommended alternative for throughput-constrained deployments, with mAP of 81.50% at 7.02 ms per image.
-## Reproducibility
-A global seed (42) is applied to Python, NumPy, PyTorch, and CUDA. All training cells are idempotent: if a run key is already present in `RESULTS`, training is skipped and the existing entry is used directly. Results can be fully restored from `all_results.json` after a kernel restart without re-running any training phase.
-## Estimated Runtime
-Full end-to-end execution (all four phases, evaluation, and plotting) takes approximately **15-20 hours** on a Kaggle single T4 session, depending on dataset I/O speed and Kaggle session availability.
-### References
-[1] Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., and Tian, Q.
-"Person Re-identification in the Wild."
-*IEEE Conference on Computer Vision and Pattern Recognition (CVPR)*, 2017.
-[https://arxiv.org/abs/1604.02531](https://arxiv.org/abs/1604.02531)
-[2] Hu, B., Wang, X., and Liu, W.
-"PersonViT: Large-scale Self-supervised Vision Transformer for Person Re-Identification."
-*Machine Vision and Applications*, 2025. DOI: 10.1007/s00138-025-01659-y
-[https://arxiv.org/abs/2408.05398](https://arxiv.org/abs/2408.05398)
-[3] hustvl.
-"PersonViT — Official GitHub Repository."
-[https://github.com/hustvl/PersonViT](https://github.com/hustvl/PersonViT)
-[4] lakeAGI.
-"PersonViT Pre-trained Weights (ViT-Base and ViT-Small)."
-*Hugging Face Model Hub*, 2024.
-[https://huggingface.co/lakeAGI/PersonViTReID/tree/main](https://huggingface.co/lakeAGI/PersonViTReID/tree/main)
-[5] He, S., Luo, H., Wang, P., Wang, F., Li, H., and Jiang, W.
-"TransReID: Transformer-based Object Re-Identification."
-*IEEE International Conference on Computer Vision (ICCV)*, 2021.
-[https://arxiv.org/abs/2102.04378](https://arxiv.org/abs/2102.04378)
-[6] Musgrave, K., Belongie, S., and Lim, S.
-"PyTorch Metric Learning."
-*arXiv preprint arXiv:2008.09164*, 2020.
-[https://arxiv.org/abs/2008.09164](https://arxiv.org/abs/2008.09164)

+---
+license: cc
+datasets:
+- sshao0516/CrowdHuman
+- aveocr/Market-1501-v15.09.15.zip
+language:
+- en
+base_model:
+- lakeAGI/PersonViT
+- Ultralytics/YOLO26
+tags:
+- person_search
+- PRW
+- Tranformer
+- PersonViT
+- YOLO26
+- ablation
+---
+# Detector Training, file: Detector_Training.ipynb
+## Overview
+This notebook presents a structured ablation study on the transferability of **YOLO26** to pedestrian detection on the **PRW** (Person Re-identification in the Wild) dataset.
+It constitutes the **detection stage** of a two-stage person search pipeline, where high recall is a primary design objective since any missed detection propagates as an unrecoverable error to the downstream Re-ID module.
+The study evaluates the impact of:
+- CrowdHuman intermediate pre-training vs. direct COCO initialisation
+- Full fine-tuning vs. partial fine-tuning (frozen backbone layers 0-9)
+- Model scale: YOLO26-Small vs. YOLO26-Large
+## Requirements
+### Platform
+The notebook was developed and executed on **Kaggle** with a **dual NVIDIA T4 GPU** configuration (16 GB VRAM each). Multi-GPU training is enabled by default via `cfg.use_multi_gpu = True`.
+To run on a single GPU or CPU, set this flag to `False` in the `Config` class.
+### Dependencies
+All required packages are installed in the first cell:
+- ultralytics, opencv-python-headless, scipy, pandas, tqdm
+### Input Datasets
+Before running the notebook, add the following two datasets as input sources in the Kaggle session (Notebook -> Input -> Add Input):
+| Dataset | Kaggle URL | Version |
+|---|---|---|
+| CrowdHuman | https://www.kaggle.com/datasets/leducnhuan/crowdhuman | 1 |
+| PRW -- Person Re-identification in the Wild | https://www.kaggle.com/datasets/edoardomerli/prw-person-re-identification-in-the-wild | 1 |
+The dataset paths are pre-configured in the `Config` class and match the default Kaggle mount locations. No manual path editing is required.
+## Notebook Structure
+| Phase | Description |
+|---|---|
+| 0 - Baseline | YOLO26-Small COCO zero-shot eval on PRW |
+| 1 - CrowdHuman Pre-training | Fine-tune Small on CrowdHuman, then eval on PRW |
+| 2 - Strategy Comparison | Full FT vs. Partial FT (freeze backbone layers 0-9) |
+| 3 - Scale-up | Best strategy applied to YOLO26-Large |
+| Final | Cross-model comparison: metrics, params, GFLOPs, speed |
+## Outputs
+All results are saved under `/kaggle/working/yolo_ablation/`:
+- `all_results.json` -- incremental results registry, persisted after each run
+- `all_results.csv` -- final summary table sorted by mAP@0.5
+- `plots/` -- all generated figures (bar charts, radar charts, training curves)
+Model checkpoints are saved under `/kaggle/working/yolo_runs/{run_name}/weights/`.
+## Key Results
+| Model | Strategy | mAP@0.5 (%) | Recall (%) | Params (M) | Latency (ms) |
+|---|---|---|---|---|---|
+| YOLO26-Large | Full FT | 96.24 | 91.38 | 26.2 | 27.88 |
+| YOLO26-Small | Full FT | 94.96 | 89.32 | 9.9 | 7.72 |
+| YOLO26-Small | Partial FT | 94.91 | 89.33 | 9.9 | 7.65 |
+| YOLO26-Small | Zero-shot (CH) | 88.23 | 83.41 | 9.9 | 6.85 |
+| YOLO26-Small | Zero-shot (COCO) | 85.82 | 79.42 | 10.0 | 6.29 |
+The Large model achieves the highest recall (91.38%) at the cost of 3.6x higher latency.
+The Small model with full or partial fine-tuning offers a competitive alternative for latency-constrained deployments, with recall above 89% at under 8 ms per image.
+## Reproducibility
+A global seed (42) is applied to Python, NumPy, PyTorch, and CUDA. All training cells are idempotent: if a valid checkpoint already exists, training is skipped and the existing weights are used.
+Results can be fully regenerated from saved checkpoints without re-running any training phase.
+## Estimated Runtime
+Full end-to-end execution (dataset conversion, all training phases, evaluation, and plotting) takes approximately **8-10 hours** on a Kaggle dual T4 session, depending on dataset I/O speed and early stopping behaviour.
+# Re-ID Training, file: ReIdentificator_Training.ipynb
+## Overview
+This notebook presents a structured ablation study on the fine-tuning of **PersonViT** for person re-identification on the **Person Re-identification in the Wild** (PRW) dataset.
+It constitutes the **Re-ID stage** of a two-stage person search pipeline, where the input is a set of pedestrian crops produced by the upstream YOLO26 detector and the output is an L2-normalised embedding used for cosine-similarity gallery retrieval.
+The study follows a **small-first, scale-up** design: the full ablation over fine-tuning strategies and loss functions is conducted on the lightweight **ViT-Small** (22 M parameters) to minimise GPU time, and only the winning configuration is then replicated on **ViT-Base** (86 M parameters). This reduces total compute by approximately 3x compared to running the ablation directly on ViT-Base, while preserving full causal interpretability of each experimental variable.
+The study evaluates the impact of:
+- Source domain selection (Duke, Market-1501, MSMT17, Occluded-Duke) in zero-shot evaluation
+- Fine-tuning strategy: Full FT vs. Partial FT vs. Freeze+Retrain
+- Metric learning loss function: TripletMarginLoss vs. ArcFaceLoss vs. NTXentLoss
+- Model scale: ViT-Small vs. ViT-Base
+## Requirements
+### Platform
+The notebook was developed and executed on **Kaggle** with a single **NVIDIA T4 GPU** (16 GB VRAM). Mixed-precision training (fp16) is enabled by default via `cfg.use_amp = True`.
+### Dependencies
+All required packages are installed in the first cell:
+- albumentations, opencv-python-headless, scipy
+- torchmetrics, timm, einops, yacs
+- pytorch-metric-learning
+- thop
+### Input Datasets and Models
+Before running the notebook, add the following dataset and model sources as inputs in the Kaggle session (Notebook -> Input -> Add Input):
+| Resource | Type | Kaggle URL | Version |
+|---|---|---|---|
+| PRW -- Person Re-identification in the Wild | Dataset | [https://www.kaggle.com/datasets/edoardomerli/prw-person-re-identification-in-the-wild](https://www.kaggle.com/datasets/edoardomerli/prw-person-re-identification-in-the-wild) | 1 |
+| PersonViT Pre-trained Weights | Model | [https://www.kaggle.com/models/simonerimondi/personvit](https://www.kaggle.com/models/simonerimondi/personvit) | 4 |
+The dataset and model paths are pre-configured in the `Config` class and match the default Kaggle mount locations. No manual path editing is required.
+The PersonViT model code is cloned at runtime from a personal GitHub fork ([github.com/simoswish02/PersonViT](https://github.com/simoswish02/PersonViT)) that fixes an import error present in the original upstream repository.
+## Notebook Structure
+| Phase | Description |
+|---|---|
+| 0 - Pretrained Baselines | Zero-shot evaluation of all four ViT-Small checkpoints on PRW |
+| 1 - Strategy Comparison | Full FT vs. Partial FT vs. Freeze+Retrain, ArcFace loss fixed (ViT-Small) |
+| 2 - Loss Comparison | TripletMarginLoss vs. ArcFaceLoss vs. NTXentLoss, best strategy fixed (ViT-Small) |
+| 3 - Scale-Up | Winning configuration replicated on ViT-Base (embedding dim 768) |
+| Final | Cross-model comparison: metrics, params, GFLOPs, latency |
+## Outputs
+All results are saved under `./evaluation_results/`:
+- `all_results.json` -- incremental results registry, persisted after each run
+- `all_results.csv` -- final summary table sorted by mAP
+- `plots/` -- all generated figures (bar charts, radar charts, training curves, Small vs. Base delta table)
+Model checkpoints are saved under `/kaggle/working/personvit_finetuning/`.
+## Key Results
+| Run | Strategy | Loss | mAP (%) | Rank-1 (%) | Params (M) | Latency (ms) |
+|---|---|---|---|---|---|---|
+| vit_base_full_triplet | Full FT | Triplet | 85.65 | 94.51 | 86.5 | 11.80 |
+| full_triplet | Full FT | Triplet | 81.50 | 93.44 | 22.0 | 7.02 |
+| full_ntxent | Full FT | NTXent | 80.77 | 93.15 | 22.0 | 7.47 |
+| full_arcface | Full FT | ArcFace | 78.10 | 93.39 | 22.0 | 7.34 |
+| freeze_arcface | Freeze+Retrain | ArcFace | 75.64 | 93.19 | 22.0 | 7.32 |
+| partial_arcface | Partial FT | ArcFace | 75.62 | 93.00 | 22.0 | 7.56 |
+| market1501 (zero-shot) | Pretrained | -- | 75.26 | 92.90 | 21.6 | 7.62 |
+ViT-Base with Full FT and TripletMarginLoss achieves the best mAP (85.65%) at the cost of 1.68x higher latency compared to the best ViT-Small run. ViT-Small with Full FT and TripletMarginLoss is the recommended alternative for throughput-constrained deployments, with mAP of 81.50% at 7.02 ms per image.
+## Reproducibility
+A global seed (42) is applied to Python, NumPy, PyTorch, and CUDA. All training cells are idempotent: if a run key is already present in `RESULTS`, training is skipped and the existing entry is used directly. Results can be fully restored from `all_results.json` after a kernel restart without re-running any training phase.
+## Estimated Runtime
+Full end-to-end execution (all four phases, evaluation, and plotting) takes approximately **15-20 hours** on a Kaggle single T4 session, depending on dataset I/O speed and Kaggle session availability.
+# References
+[1] Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., and Tian, Q.
+"Person Re-identification in the Wild."
+*IEEE Conference on Computer Vision and Pattern Recognition (CVPR)*, 2017.
+[https://arxiv.org/abs/1604.02531](https://arxiv.org/abs/1604.02531)
+[2] Hu, B., Wang, X., and Liu, W.
+"PersonViT: Large-scale Self-supervised Vision Transformer for Person Re-Identification."
+*Machine Vision and Applications*, 2025. DOI: 10.1007/s00138-025-01659-y
+[https://arxiv.org/abs/2408.05398](https://arxiv.org/abs/2408.05398)
+[3] hustvl.
+"PersonViT — Official GitHub Repository."
+[https://github.com/hustvl/PersonViT](https://github.com/hustvl/PersonViT)
+[4] lakeAGI.
+"PersonViT Pre-trained Weights (ViT-Base and ViT-Small)."
+*Hugging Face Model Hub*, 2024.
+[https://huggingface.co/lakeAGI/PersonViTReID/tree/main](https://huggingface.co/lakeAGI/PersonViTReID/tree/main)
+[5] He, S., Luo, H., Wang, P., Wang, F., Li, H., and Jiang, W.
+"TransReID: Transformer-based Object Re-Identification."
+*IEEE International Conference on Computer Vision (ICCV)*, 2021.
+[https://arxiv.org/abs/2102.04378](https://arxiv.org/abs/2102.04378)
+[6] Musgrave, K., Belongie, S., and Lim, S.
+"PyTorch Metric Learning."
+*arXiv preprint arXiv:2008.09164*, 2020.
+[https://arxiv.org/abs/2008.09164](https://arxiv.org/abs/2008.09164)