| --- |
| task_categories: |
| - object-detection |
| language: |
| - id |
| - en |
| tags: |
| - poultry |
| - chicken |
| - animal-health |
| - computer-vision |
| - yolov11s |
| - early-disease-detection |
| - Indonesia |
| base_model: |
| - Ultralytics/YOLO11 |
| datasets: |
| - IceKhoffi/chicken-health-behavior-multimodal |
| --- |
| |
| # `chbd-yolov11s-chicken-detector` |
|
|
| This model is a YOLOv11s-based object detector specifically trained to identify individual chickens in farm environments. It is a core component of the "Chicken Health & Behavior Detection" multimodal project, aiming to provide visual insights for early disease detection and behavioral analysis in poultry farming. |
|
|
| ## Model Description |
|
|
| The `chbd-yolov11s-chicken-detector` is an object detection model trained using the Ultralytics YOLOv11s architecture. It is designed to accurately locate and classify chickens within images and video frames, serving as a foundational step for downstream tasks such as chicken counting, density estimation, tracking, and the analysis of anomalous visual behaviors related to health. |
|
|
|
|
|  |
|
|
|
|
| ## Training Data |
|
|
| This model was trained on the `vision-object-detection` datasets from [chicken-health-behavior-multimodal](https://huggingface.co/datasets/IceKhoffi/chicken-health-behavior-multimodal). |
|
|
| The visual data within this dataset was curated from publicly available videos, specifically from the **Kipster Farm YouTube channel**. While Kipster Farm is located in the US, this data provides high-quality depictions of chicken behavior and general farm environments suitable for developing robust foundational detection models. |
|
|
| * **Dataset Size :** Total images used for training and validation: 24 (21 training, 3 validation) |
| * **Annotations :** Bounding boxes in YOLO format (`class_id x_center y_center width height`). |
| * **Classes :** Currently trained for a single class: `0`. |
|
|
| ## Training Procedure |
|
|
| The model was trained using the Ultralytics YOLO framework (version 8.3.162) |
|
|
| * **Model :** `yolov11s.pt` |
| * **Epochs :** 100 |
| * **Image Size :** 640x640 pixels |
| * **Hardware :** Training was performed on a Tesla T4 GPU with 15095MiB memory |
| * **Optimizer :** AdamW |
| * **Python/Pytorch :** Python 3.11.13, PyTorch 2.6.0+cu124 |
|
|
| ## How to Use |
|
|
| You can load and use this model for inference with the Ultralytics YOLO library: |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| from ultralytics import YOLO |
| import os |
| |
| # Define each Hugging Face details |
| repo_id = "IceKhoffi/chicken-object-detection-yolov11s" |
| filename = "yolov11s.pt" |
| model_path = hf_hub_download(repo_id=repo_id, filename=filename) |
| |
| # Load the trained model weights |
| model = YOLO(model_path) |
| |
| # Perform inference on an image |
| results = model('path/to/your/image.jpg') |
| |
| # Or on a video stream |
| # results = model('path/to/your/video.mp4', stream=True, save=True) |
| |
| # Process results |
| for r in results: |
| boxes = r.boxes # Bounding boxes |
| masks = r.masks # Segmentation masks |
| probs = r.probs # Probabilities |
| |
| # Save inference results (if save=True in model call) |
| # Results are saved to runs/detect/predict by default. |
| ``` |
