This project leverages the power of YOLOv5 for object detection and classification to build a system that identifies animal species in real-world images. From collecting datasets to model training and deployment, the system is designed to provide robust and accurate predictions. 🦁🐘🐒
The Animal Species Prediction System uses the YOLOv5 deep learning framework to detect and classify multiple animal species in images. This project demonstrates the application of computer vision techniques for wildlife conservation, research, and monitoring.
✨ Highlights:
- Object detection and classification with YOLOv5.
- Integration with a user-friendly interface.
- Real-world image predictions with high accuracy.
The dataset includes images of 12 different animal species, such as:
- 🐅 Tiger
- 🦒 Giraffe
- 🐻 Bear
- 🦁 Lion
- 🐘 Elephant
- 🦌 Deer
- 🐺 Wolf
- 🐂 Bull
- 🐒 Monkey
- 🐆 Leopard
- 🦏 Rhinoceros
- 🦛 Hippo
Example Predictions:
| Elephant | Cattle | Monkey |
|---|---|---|
 |
 |
 |
- Data Collection: Images of various animal species were gathered and labeled with bounding boxes.
- Data Splitting: The dataset was split into 80% training and 20% validation sets.
- Storage: Prepared datasets were uploaded to Google Drive for seamless integration with the training pipeline.
The YOLOv5 model was trained with the following parameters:
- Image size: 415
- Batch size: 31
- Epochs: 100
- Pre-trained weights:
yolov5s
Inference:
The model uses trained weights (best.pt) to detect multiple animal species in an image and outputs bounding boxes with class labels.
Elephant.Prediction.mp4
The system achieved high accuracy in detecting and classifying animal species in real-world images. The predictions include bounding boxes and species labels for each detected animal, supporting multiple detections in a single image.
Detected images include bounding boxes with labels:
- Input Image: Wildlife photograph
- Output: Annotated image with detected species
This project showcases the potential of YOLOv5 for wildlife monitoring and research. Applications include:
- Wildlife conservation
- Animal behavior studies
- Automated zoo monitoring systems
Future Work:
- Expand to more species for increased biodiversity coverage.
- Improve model efficiency and deploy as a real-time system.
Contributions are welcome! 🎉
- Report issues or suggest features via issues.
- Fork the repository, implement your ideas, and submit a pull request.
⭐ If you find this project helpful, give it a star!
Let’s build smarter tools for wildlife conservation together! 🌍🐾