---
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.


![image/png](https://cdn-uploads.huggingface.co/production/uploads/67524d7300134bb0ad1503a7/3zLD-pE8Trk1Zo8sf0i1F.png)


## 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.
```