README.md

---
license: cc-by-4.0
task_categories:
  - object-detection
  - image-classification
  - video-classification
language:
  - en
tags:
  - poultry
  - chicken
  - egg
  - broiler
  - hen
  - agriculture
  - smart-farming
  - precision-livestock-farming
  - animal-welfare
  - multi-camera
  - tracking
  - yolo
  - yolov11
  - computer-vision
size_categories:
  - 10K<n<100K
pretty_name: PoultryVision Unified Dataset
configs:
  - config_name: detection
    data_files:
      - split: train
        path: "images/train/*"
      - split: val
        path: "images/val/*"
      - split: test
        path: "images/test/*"
  - config_name: classification
    data_files:
      - split: train
        path: "classification/train/**"
      - split: val
        path: "classification/val/**"
      - split: test
        path: "classification/test/**"
---

# PoultryVision Unified Dataset

A **large-scale, multi-modal poultry-farm dataset** unifying six public sources for
detection, classification, multi-camera tracking and behavior analysis of
chickens (broilers, hens, cocks) and eggs.

This dataset was built to train **[Williamsanderson/PoultryVision](https://huggingface.co/Williamsanderson/PoultryVision)**, a YOLOv11m model that **beats the fine-tuned YOLOv11x reported by Cardoen et al. (MVBroTrack paper, 2025) by +8.5 mAP@50-95**.

---

##  Dataset at a glance

### Object detection (YOLO format)

| Split | Images |
|-------|-------:|
| Train | 15 987 |
| Val   |  3 706 |
| Test  |  1 893 |
| **Total** | **21 586** |

### Image classification

| Split | Images |
|-------|-------:|
| Train | 1 832 |
| Val   |   444 |
| Test  |   263 |
| **Total** | **2 539** |

### Videos & multi-camera

- **24 MP4 videos** from 4 synchronized cameras (cam 9 / 10 / 11 / 12) across 6 samples
- Camera **calibration files** (intrinsics + extrinsics) for every camera
- **Reprojection masks** defining the ground-plane region of interest
- **Tracking ground truth** on the ground plane
- **Pre-computed YOLO detections** per frame for every multi-view sample

### Classes (detection)

| ID | Name    | Description                          |
|----|---------|--------------------------------------|
| 0  | chicken | All poultry: broilers, hens, cocks   |
| 1  | egg     | Chicken eggs                         |

---

##  Source datasets

| # | Source                          | Type                          | Link / Reference |
|---|---------------------------------|-------------------------------|------------------|
| 1 | Dataset Chicken 1               | Classification (images.cv)    | images.cv |
| 2 | Dataset Chicken 2               | Classification (images.cv)    | images.cv |
| 3 | Dataset Chicken 3               | Detection (COCO, Roboflow)    | Roboflow Universe |
| 4 | Chickens-Eggs v1                | Detection (YOLOv8, Roboflow)  | Roboflow Universe |
| 5 | chicken eggs 2 v3               | Detection + segmentation      | Roboflow Universe |
| 6 | **MVBroTrack**                  | Multi-camera broiler tracking | Cardoen et al., *Computers and Electronics in Agriculture*, 2025 |

All six sources were standardized to a **unified YOLO detection format** (and/or ImageFolder classification format), deduplicated, and split into train/val/test.

---

##  Folder structure

```
PoultryVision-Dataset/
├── data.yaml                      # Ultralytics-compatible dataset config
├── images/                        # Detection images
│   ├── train/   (15 987)
│   ├── val/     ( 3 706)
│   └── test/    ( 1 893)
├── labels/                        # YOLO .txt labels (one per image)
│   ├── train/
│   ├── val/
│   └── test/
├── classification/                # ImageFolder layout
│   ├── train/<class>/*.jpg
│   ├── val/<class>/*.jpg
│   └── test/<class>/*.jpg
├── videos/                        # 24 MP4 videos (4 cameras × 6 samples)
├── calibrations/                  # Camera calibration (intrinsics + extrinsics)
│   └── cam_<id>/
│       ├── intrinsics/{cameraMatrix.txt, distCoeffs.txt}
│       └── extrinsics/{rvec.txt, tvec.txt}
├── multi_view_detection/          # Pre-computed per-frame YOLO detections
├── reprojection_masks/            # Ground-plane ROI masks
└── tracking_gt/                   # Multi-camera tracking ground truth
```

---

##  Quick start

### Download

```bash
pip install huggingface_hub
hf download --repo-type dataset Williamsanderson/PoultryVision-Dataset --local-dir PoultryVision-Dataset
```

### Train a YOLO detector

```python
from ultralytics import YOLO
model = YOLO("yolo11m.pt")
model.train(
    data="PoultryVision-Dataset/data.yaml",
    epochs=70,
    imgsz=640,
    batch=16,
    optimizer="AdamW",
    lr0=0.001,
)
```

Reference YOLO recipe that produced the published model:

```yaml
model: yolo11m.pt
epochs: 70
imgsz: 640
optimizer: AdamW
lr0: 0.001
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
mosaic: 1.0
mixup: 0.1
close_mosaic: 10
auto_augment: randaugment
```

### Image classification

```python
from torchvision.datasets import ImageFolder
from torchvision import transforms

tfm = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
])
train = ImageFolder("PoultryVision-Dataset/classification/train", transform=tfm)
```

### Multi-camera tracking

Calibration files follow the MVBroTrack paper convention. Each camera folder
contains `cameraMatrix.txt`, `distCoeffs.txt`, `rvec.txt`, `tvec.txt`.
The repository ships a full multi-view pipeline (Algorithm 1 & 2 of the paper,
Tracking-by-Curve-Matching) — see
[`Williamsanderson/PoultryVision` model repo](https://huggingface.co/Williamsanderson/PoultryVision).

---

## 🏆 Benchmark

Model trained on this dataset (YOLOv11m, 70 epochs, imgsz 640, AdamW):

| Metric        | Value  |
|---------------|-------:|
| mAP@50-95     | **0.793** |
| mAP@50        | **0.971** |
| Precision     | 0.934 |
| Recall        | 0.934 |

Compared to the MVBroTrack paper (Cardoen et al., 2025):

| Model                               | mAP@50-95 | Params |
|-------------------------------------|:---------:|:------:|
| YOLOv11x fine-tuned (paper)         | 70.8 %    | 56.9 M |
| **YOLOv11m fine-tuned (ours)**      | **79.3 %**| 20.1 M |

---

##  License

This dataset is released under **CC-BY-4.0**.

- The unified packaging, splits and labels harmonization are © 2025 Williams Anderson, CC-BY-4.0.
- Individual source datasets retain their original licenses:
  - **MVBroTrack** (Cardoen et al., 2025) — see the original paper and its data statement
  - **Roboflow Universe** datasets — typically CC-BY-4.0 (check each source)
  - **images.cv** datasets — CC-BY-4.0 / public domain
- Please cite the original sources if you use the corresponding subsets.

---

##  Citation

```bibtex
@misc{williamsanderson_poultryvision_dataset_2025,
  title  = {PoultryVision: A Unified Dataset for Poultry-Farm Computer Vision},
  author = {Williams Anderson},
  year   = {2025},
  howpublished = {\url{https://huggingface.co/datasets/Williamsanderson/PoultryVision-Dataset}},
}

@article{cardoen2025mvbrotrack,
  title   = {Multi-camera detection and tracking for individual broiler monitoring},
  author  = {Cardoen, J. and others},
  journal = {Computers and Electronics in Agriculture},
  year    = {2025}
}
```

---

##  Acknowledgements

- **Cardoen et al.** (MVBroTrack) for the multi-camera broiler data
- **Roboflow** and **images.cv** communities for the chicken / egg datasets
- **Ultralytics** for the YOLOv11 framework that produced the reference model