Datasets:
Tasks:
Object Detection
Formats:
webdataset
Languages:
English
Size:
< 1K
ArXiv:
Tags:
webdataset
License:
| license: cc-by-4.0 | |
| task_categories: | |
| - object-detection | |
| language: | |
| - en | |
| configs: | |
| - config_name: images | |
| data_files: | |
| - split: train | |
| path: | |
| - "data/amfeed.tar" | |
| - "data/pmfeed.tar" | |
| - config_name: videos | |
| data_files: | |
| - split: train | |
| path: "data/video.tar" | |
| tags: | |
| - webdataset | |
| # 8-Calves Dataset | |
| [](https://arxiv.org/abs/2503.13777) | |
| A benchmark dataset for occlusion-rich object detection, identity classification, and multi-object tracking. Features 8 Holstein Friesian calves with unique coat patterns in a 1-hour video with temporal annotations. | |
| --- | |
| ## Overview | |
| This dataset provides: | |
| - 🕒 **1-hour video** (67,760 frames @20 fps, 600x800 resolution) | |
| - 🎯 **537,908 verified bounding boxes** with calf identities (1-8) | |
| - 🖼️ **900 hand-labeled static frames** for detection tasks | |
| - Designed to evaluate robustness in occlusion handling, identity preservation, and temporal consistency. | |
| <img src="dataset_screenshot.png" alt="Dataset Example Frame" width="50%" /> | |
| *Example frame with bounding boxes (green) and calf identities. Challenges include occlusion, motion blur, and pose variation.* | |
| --- | |
| ## Key Features | |
| - **Temporal Richness**: 1-hour continuous recording (vs. 10-minute benchmarks like 3D-POP) | |
| - **High-Quality Labels**: | |
| - Generated via **ByteTrack + YOLOv8m** pipeline with manual correction | |
| - <0.56% annotation error rate | |
| - **Unique Challenges**: Motion blur, pose variation, and frequent occlusions | |
| - **Efficiency Testing**: Compare lightweight (e.g., YOLOv9t) vs. large models (e.g., ConvNextV2) | |
| --- | |
| ## Dataset Structure | |
| hand_labelled_frames/ # 900 manually annotated frames and labels in YOLO format, class=0 for cows | |
| pmfeed_4_3_16.avi # 1-hour video (4th March 2016) | |
| pmfeed_4_3_16_bboxes_and_labels.pkl # Temporal annotations | |
| ### Annotation Details | |
| **PKL File Columns**: | |
| | Column | Description | | |
| |--------|-------------| | |
| | `class` | Always `0` (cow detection) | | |
| | `x`, `y`, `w`, `h` | YOLO-format bounding boxes | | |
| | `conf` | Ignore (detections manually verified) | | |
| | `tracklet_id` | Calf identity (1-8) | | |
| | `frame_id` | Temporal index matching video | | |
| **Load annotations**: | |
| ```python | |
| import pandas as pd | |
| df = pd.read_pickle("pmfeed_4_3_16_bboxes_and_labels.pkl") | |
| ``` | |
| ## Usage | |
| ### Dataset Download: | |
| Step 1: install git-lfs: | |
| `git lfs install` | |
| Step 2: | |
| `git clone [email protected]:datasets/tonyFang04/8-calves` | |
| Step 3: create dataset: | |
| `./make_dataset.sh` | |
| Step 4: install conda and pip environments: | |
| ``` | |
| conda create --name new_env --file conda_requirements.txt | |
| pip install -r pip_requirements.txt | |
| ``` | |
| ### Object Detection | |
| - **Training/Validation**: Use the first 600 frames from `hand_labelled_frames/` (chronological split). | |
| - **Testing**: Evaluate on the full video (`pmfeed_4_3_16.avi`) using the provided PKL annotations. | |
| - ⚠️ **Avoid Data Leakage**: Do not use all 900 frames for training - they are temporally linked to the test video. | |
| **Recommended Split**: | |
| | Split | Frames | Purpose | | |
| |------------|--------|------------------| | |
| | Training | 500 | Model training | | |
| | Validation | 100 | Hyperparameter tuning | | |
| | Test | 67,760 | Final evaluation | | |
| ### Benchmarking YOLO Models: | |
| Step 1: | |
| `cd 8-calves/object_detector_benchmark`. Run | |
| `./create_yolo_dataset.sh` and | |
| `create_yolo_testset.py`. This creates a YOLO dataset with the 500/100/67760 train/val/test split recommended above. | |
| Step 2: find the `Albumentations` class in the `data/augment.py` file in ultralytics source code. And replace the default transforms to: | |
| ``` | |
| # Transforms | |
| T = [ | |
| A.RandomRotate90(p=1.0), | |
| A.HorizontalFlip(p=0.5), | |
| A.RandomBrightnessContrast(p=0.4), | |
| A.ElasticTransform( | |
| alpha=100.0, | |
| sigma=5.0, | |
| p=0.5 | |
| ), | |
| ] | |
| ``` | |
| Step 3: | |
| run the yolo detectors following the following commands: | |
| ``` | |
| cd yolo_benchmark | |
| Model_Name=yolov9t | |
| yolo cfg=experiment.yaml model=$Model_Name.yaml name=$Model_Name | |
| ``` | |
| ### Benchmark Transformer Based Models: | |
| Step 1: run the following commands to load the data into yolo format, then into coco, then into arrow: | |
| ``` | |
| cd 8-calves/object_detector_benchmark | |
| ./create_yolo_dataset.sh | |
| python create_yolo_testset.py | |
| python yolo_to_coco.py | |
| python data_wrangling.py | |
| ``` | |
| Step 2: run the following commands to train: | |
| ``` | |
| cd transformer_benchmark | |
| python train.py --config Configs/conditional_detr.yaml | |
| ``` | |
| ### Temporal Classification | |
| - Use `tracklet_id` (1-8) from the PKL file as labels. | |
| - **Temporal Split**: 30% train / 30% val / 40% test (chronological order). | |
| ### Benchmark vision models for temporal classification: | |
| Step 1: cropping the bounding boxes from `pmfeed_4_3_16.mp4` using the correct labels in `pmfeed_4_3_16_bboxes_and_labels.pkl`. Then convert the folder of images cropped from `pmfeed_4_3_16.mp4` into lmdb dataset for fast loading: | |
| ``` | |
| cd identification_benchmark | |
| python crop_pmfeed_4_3_16.py | |
| python construct_lmdb.py | |
| ``` | |
| Step 2: get embeddings from vision model: | |
| ``` | |
| cd big_model_inference | |
| ``` | |
| Use `inference_resnet.py` to get embeddings from resnet and `inference_transformers.py` to get embeddings from transformer weights available on Huggingface: | |
| ``` | |
| python inference_resnet.py --resnet_type resnet18 | |
| python inference_transformers.py --model_name facebook/convnextv2-nano-1k-224 | |
| ``` | |
| Step 3: use the embeddings and labels obtained from step 2 to conduct knn evaluation and linear classification: | |
| ``` | |
| cd ../classification | |
| python train.py | |
| python knn_evaluation.py | |
| ``` | |
| ## Key Results | |
| ### Object Detection (YOLO Models) | |
| | Model | Parameters (M) | mAP50:95 (%) | Inference Speed (ms/sample) | | |
| |-------------|----------------|--------------|-----------------------------| | |
| | **YOLOv9c** | 25.6 | **68.4** | 2.8 | | |
| | YOLOv8x | 68.2 | 68.2 | 4.4 | | |
| | YOLOv10n | 2.8 | 64.6 | 0.7 | | |
| --- | |
| ### Identity Classification (Top Models) | |
| | Model | Accuracy (%) | KNN Top-1 (%) | Parameters (M) | | |
| |----------------|--------------|---------------|----------------| | |
| | **ConvNextV2-Nano** | 73.1 | 50.8 | 15.6 | | |
| | Swin-Tiny | 68.7 | 43.9 | 28.3 | | |
| | ResNet50 | 63.7 | 38.3 | 25.6 | | |
| --- | |
| **Notes**: | |
| - **mAP50:95**: Mean Average Precision at IoU thresholds 0.5–0.95. | |
| - **KNN Top-1**: Nearest-neighbor accuracy using embeddings. | |
| - Full results and methodology: [arXiv paper](https://arxiv.org/abs/2503.13777). | |
| ## License | |
| This dataset is released under [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). | |
| *Modifications/redistribution must include attribution.* | |
| ## Citation | |
| ```bibtex | |
| @article{fang20248calves, | |
| title={8-Calves: A Benchmark for Object Detection and Identity Classification in Occlusion-Rich Environments}, | |
| author={Fang, Xuyang and Hannuna, Sion and Campbell, Neill}, | |
| journal={arXiv preprint arXiv:2503.13777}, | |
| year={2024} | |
| } | |
| ``` | |
| ## Contact | |
| **Dataset Maintainer**: | |
| Xuyang Fang | |
| Email: [[email protected]](mailto:[email protected]) |