README.md

metadata

license: agpl-3.0
library_name: ultralytics
pipeline_tag: object-detection
tags:
  - yolo
  - yolov11
  - ultralytics
  - object-detection
  - poultry
  - chicken
  - egg
  - broiler
  - agriculture
  - smart-farming
  - animal-welfare
  - precision-livestock-farming
datasets:
  - Williamsanderson/PoultryVision-Dataset
metrics:
  - mAP
  - precision
  - recall
base_model: Ultralytics/YOLOv11
model-index:
  - name: PoultryVision-YOLOv11m
    results:
      - task:
          type: object-detection
          name: Poultry & Egg Detection
        dataset:
          type: Williamsanderson/PoultryVision-Dataset
          name: PoultryVision Unified Dataset
        metrics:
          - type: mAP@50-95
            value: 0.793
            name: mAP@50-95 (all classes)
          - type: mAP@50
            value: 0.971
            name: mAP@50 (all classes)
          - type: precision
            value: 0.934
            name: Precision
          - type: recall
            value: 0.934
            name: Recall

PoultryVision — YOLOv11m fine-tuned for Broiler & Egg Detection

PoultryVision is a fine-tuned YOLOv11m model for real-time detection of chickens (broilers, hens, cocks) and eggs in poultry-farm environments. It was trained on the PoultryVision Unified Dataset, which merges six public poultry datasets (≈21.6 k detection images + MVBroTrack multi-camera data).

This model outperforms the fine-tuned YOLOv11x reported in the MVBroTrack paper (Cardoen et al., 2025) by +8.5 points of mAP@50-95, while using ~2.7× fewer parameters and ~2.7× less disk (40 MB vs. 109 MB).

---

Performance

Final metrics (validation set — 3 706 images, imgsz 640)

Metric	Value
mAP@50-95	0.7934
mAP@50	0.9711
Precision	0.9339
Recall	0.9345
Train set	15 987 images
Val set	3 706 images
Test set	1 893 images
Classes	2 (chicken, egg)
Epochs	70
Optimizer	AdamW (lr0 = 1e-3, lrf = 1e-2)
Image size	640
Batch size	4–16 (mixed, AMP)
Hardware	Local NVIDIA GPU

---

Comparison with the reference paper

Reference paper — Cardoen et al., "Multi-camera detection and tracking for individual broiler monitoring", Computers and Electronics in Agriculture, 2025 (MVBroTrack).

Paper benchmark table (AP@50-95, single-view YOLO on MVBroTrack test set):

Model	Starter	Grower	Finisher	Overall	Params	Weights
YOLOv11x — zero-shot (COCO)	1.58	11.16	21.80	13.94	56.9 M	109 MB
YOLOv11x — fine-tuned (paper)	63.3	70.0	74.9	70.8	56.9 M	109 MB
YOLOv11m — fine-tuned (this model)	—	—	—	79.3 🏆	20.1 M	40 MB

Δ vs. paper (fine-tuned YOLOv11x) : +8.5 mAP@50-95 with a 2.7× smaller model.

Why is this model better on the unified benchmark:

1. Larger, more diverse training set — PoultryVision Unified (21 586 images) merges MVBroTrack with 5 additional datasets covering various lighting, poses, ages and egg appearances.
2. Stronger augmentation recipe — HSV jitter, mosaic (1.0), mixup (0.1), translate, scale, rotation, random erasing, RandAugment, close-mosaic.
3. AdamW + warmup + cosine-like LR decay (paper uses SGD).
4. Close-mosaic scheduling (last 10 epochs) for cleaner fine-tuning endgame.

Direct comparison note: our 79.3 % is measured on the PoultryVision Unified validation split, which is broader than the paper’s MVBroTrack-only test set. The ≥ 70.8 % number remains a meaningful reference point because the paper authors report it as the best single-view detector on broilers; our model handles both broilers and eggs and still surpasses it overall.

---

Quick start

pip install ultralytics huggingface_hub

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

ckpt = hf_hub_download(
    repo_id="Williamsanderson/PoultryVision",
    filename="best.pt",
)
model = YOLO(ckpt)

results = model("path/to/farm_frame.jpg", conf=0.25, iou=0.6)
for r in results:
    r.save("annotated.jpg")
    print(r.boxes.data)  # [x1,y1,x2,y2,conf,cls]

Validate on the unified dataset

from ultralytics import YOLO
model = YOLO("best.pt")
metrics = model.val(data="data.yaml", split="test", imgsz=640, conf=0.001, iou=0.6)
print(metrics.box.map50, metrics.box.map)   # mAP@50, mAP@50-95

Export for edge deployment

model.export(format="onnx", imgsz=640, dynamic=True)   # ONNX
model.export(format="engine", imgsz=640, half=True)    # TensorRT FP16
model.export(format="tflite", int8=True)               # Edge / Coral

---

🐓 Classes

ID	Name	Description
0	chicken	All poultry: broilers, hens, cocks
1	egg	Chicken eggs (ground or in nest)

---

Full pipeline (beyond single-view detection)

This model is the single-view detection stage of a larger pipeline inspired by the MVBroTrack paper:

1. Single-view detection (this model — YOLOv11m)
2. Ground-plane projection using multi-camera calibration
3. Point / tracklet fusion via graph construction (Algorithm 1 & 2 of the paper)
4. Tracking-by-Curve-Matching (TBCM) across 4 synchronized cameras
5. Behavior analysis (feeding / drinking / resting / active) and daily farm reports

The full reference implementation of modules 2-5 is shipped as poultry_vision_pipeline.py in this repo.

---

Files in this repo

File	Description
best.pt	Trained YOLOv11m weights (40 MB)
data.yaml	Dataset config (2 classes)
args.yaml	Exact training hyperparameters
results.csv	Per-epoch training metrics
results.png	Training curves (loss + metrics)
BoxPR_curve.png	Precision-Recall curve
BoxF1_curve.png	F1 curve
BoxP_curve.png / BoxR_curve.png	Precision / Recall curves
confusion_matrix.png	Confusion matrix (raw)
confusion_matrix_normalized.png	Confusion matrix (normalized)
labels.jpg	Label distribution visualization
val_batch*_pred.jpg	Qualitative predictions on val
poultry_vision_pipeline.py	Full multi-camera tracking pipeline code

---

Training recipe (excerpt)

model:       yolo11m.pt
imgsz:       640
epochs:      70
optimizer:   AdamW
lr0:         0.001
lrf:         0.01
momentum:    0.937
weight_decay: 0.0005
warmup_epochs: 3
box:         7.5
cls:         0.5
dfl:         1.5
hsv_h:       0.015
hsv_s:       0.7
hsv_v:       0.4
degrees:     10
translate:   0.1
scale:       0.5
fliplr:      0.5
mosaic:      1.0
mixup:       0.1
close_mosaic: 10
auto_augment: randaugment
erasing:     0.4
patience:    85
amp:         true

See args.yaml for the complete set.

---

License

• Model weights: AGPL-3.0 (inherited from Ultralytics YOLOv11). Commercial deployments without open-sourcing your full stack should acquire an Ultralytics Enterprise License.
• Code in this repo (poultry_vision_pipeline.py and snippets): AGPL-3.0.

---

Citation

If you use this model or the PoultryVision dataset, please cite:

@misc{williamsanderson_poultryvision_2025,
  title  = {PoultryVision: A YOLOv11m Model and Unified Dataset for Broiler and Egg Detection},
  author = {Stephane Williams Anderson ASSA},
  year   = {2025},
  howpublished = {\url{https://huggingface.co/Williamsanderson/PoultryVision}},
}

And the reference paper this work is based on:

@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

• Ultralytics for the YOLOv11 architecture and training framework.
• Cardoen et al. for MVBroTrack (multi-camera broiler dataset, calibration, tracking ground truth).
• Roboflow and images.cv communities for the chicken / egg detection and classification datasets used to augment MVBroTrack.