{"id":20316097,"url":"https://github.com/pushtogithub23/wildlife-yolo-detector","last_synced_at":"2026-06-25T13:31:12.465Z","repository":{"id":259484814,"uuid":"877991282","full_name":"Pushtogithub23/wildlife-yolo-detector","owner":"Pushtogithub23","description":"This project implements a wild animal detection system using YOLOv8, Roboflow, and Python, allowing real-time tracking in images and videos.","archived":false,"fork":false,"pushed_at":"2024-10-27T11:38:54.000Z","size":78974,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-11-09T23:26:57.162Z","etag":null,"topics":["computer-vision","object-detection","roboflow","yolov8"],"latest_commit_sha":null,"homepage":"https://universe.roboflow.com/puspendu-ai-vision-workspace/wild-animals-detection-fspct","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Pushtogithub23.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-10-24T15:30:43.000Z","updated_at":"2024-10-27T11:38:57.000Z","dependencies_parsed_at":"2024-11-14T18:39:46.275Z","dependency_job_id":null,"html_url":"https://github.com/Pushtogithub23/wildlife-yolo-detector","commit_stats":null,"previous_names":["pushtogithub23/wildlife-yolo-detector"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Pushtogithub23/wildlife-yolo-detector","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pushtogithub23%2Fwildlife-yolo-detector","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pushtogithub23%2Fwildlife-yolo-detector/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pushtogithub23%2Fwildlife-yolo-detector/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pushtogithub23%2Fwildlife-yolo-detector/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Pushtogithub23","download_url":"https://codeload.github.com/Pushtogithub23/wildlife-yolo-detector/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pushtogithub23%2Fwildlife-yolo-detector/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34778079,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-25T02:00:05.521Z","response_time":101,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-vision","object-detection","roboflow","yolov8"],"created_at":"2024-11-14T18:24:19.387Z","updated_at":"2026-06-25T13:31:12.445Z","avatar_url":"https://github.com/Pushtogithub23.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🦁 Wild Animals Detection using YOLOv8\n\nA comprehensive implementation of a wild animals detection system using YOLOv8, Roboflow, and Python. This project enables real-time detection and tracking of wild animals in images and videos. \n\n![Python](https://img.shields.io/badge/Python-3.7+-blue.svg)\n![YOLOv8](https://img.shields.io/badge/YOLO-v8-brightgreen.svg)\n![OpenCV](https://img.shields.io/badge/OpenCV-4.x-red.svg)\n\n![lions_detected](https://github.com/user-attachments/assets/1fb43c97-a643-4a4c-a0ae-e61e97767bd8)\n\n![tiger_detected](https://github.com/user-attachments/assets/ca734f18-4deb-4efa-ac65-0eb46e6e791e)\n\n\n\n## 🚀 Features\n\n- Real-time wild animal detection in images and videos\n- Support for both local files and URL-based images\n- Automatic tracking of animals in video streams\n- Confidence-based filtering of detections\n- Custom visualization with optimized annotations\n- Easy-to-use interface for both image and video processing\n\n## 📋 Prerequisites\n\nBefore running this project, make sure you have the following dependencies installed:\n\n```bash\npip install roboflow ultralytics supervision opencv-python matplotlib numpy requests\n```\n\n## 🛠️ Installation\n\n1. Clone the repository:\n```bash\ngit clone https://github.com/Pushtogithub23/wildlife-yolo-detector.git\ncd wildlife-yolo-detector\n```\n\n2. Install required packages:\n```bash\npip install -r requirements.txt\n```\n\n3. Set up your Roboflow API key:\n- Create an account on [Roboflow](https://roboflow.com)\n- Replace `YOUR_API_KEY` in the notebook with your actual API key\n\n## 💻 Usage\n\n### Image Detection\n\n```python\n\n# For local images\ndisplay_prediction(\n    \"DATA/IMAGES/test_images/zebra_1.jpg\",\n    save_fig=True,\n    filename='zebras_detected_1.jpg'\n)\n\n# For images from URL\ndisplay_prediction(\n    \"https://example.com/image.jpg\",\n    save_fig=True,\n    filename='detected_image.jpg'\n)\n```\n\nI have attached a few image detections below:\n\n![elephants_detected](https://github.com/user-attachments/assets/7c715b0a-daa0-4046-bd7d-3d2d4dfdec11)\n\n![giraffes_detected](https://github.com/user-attachments/assets/c41bdc75-5a0b-48b2-9092-689aa1c82a0b)\n\n![zebras_detected](https://github.com/user-attachments/assets/8130cc73-b461-4cda-b2c7-ad3e1a22d26c)\n\n### Video Detection\n\n```python\n\npredict_in_videos(\n    \"DATA/VIDEOS/test_videos/zebras.mp4\",\n    save_video=True,\n    filename='zebras_detected.mp4'\n)\n```\nI have attached a few video detections below in gif format:\n\n![zebras_detected](https://github.com/user-attachments/assets/03f0d723-4d36-46b8-9d8a-d41cf616ca74)\n\n![giraffe_detected](https://github.com/user-attachments/assets/f8bf1c33-cbf8-449d-b5fe-1d135c2cef6f)\n\n## 📦 Project Structure\n\n```\nwild-animals-detection/\n├── DATA/\n│   ├── IMAGES/\n│   │   ├── test_images/\n│   │   └── detected_images/\n│   └── VIDEOS/\n│       ├── test_videos/\n│       └── captured_videos/\n|── yolo-wild-animals-detection.ipynb\n├── requirements.txt\n└── README.md\n```\n\n## 🔧 Model Training\n\nThe project uses YOLOv8 large model (`yolov8l.pt`) as the base model for transfer learning. To train the model on your own dataset:\n\n1. Prepare your dataset using Roboflow\n2. Update the data.yaml file with correct paths\n3. Run the training script:\n\n```python\nmodel = YOLO('yolov8l.pt')\nmodel.train(\n    data=\"WILD-ANIMALS-DETECTION-1/data.yaml\",\n    epochs=100,\n    imgsz=640\n)\n```\n\n## 📝 Key Parameters\n\n- Detection confidence threshold: 0.5\n- Video processing can be stopped by pressing 'p'\n- Image size for training: 640x640\n- Training epochs: 100\n\n## 🤝 Contributing\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/AmazingFeature`)\n3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)\n4. Push to the branch (`git push origin feature/AmazingFeature`)\n5. Open a Pull Request\n\n\n## 🙏 References\n\n- [Ultralytics](https://github.com/ultralytics/ultralytics) for YOLOv8\n- [Roboflow](https://roboflow.com) for dataset management\n- [Supervision](https://github.com/roboflow/supervision) for annotation tools\n\nYou can find the project on Roboflow by clicking [here](https://universe.roboflow.com/puspendu-ai-vision-workspace/wild-animals-detection-fspct)\n\nYou can view the training results on wandb(Weights \u0026 Biases) by clicking [here](https://wandb.ai/ranapuspendu24-iit-madras-foundation/Ultralytics/runs/o2ze0pai/workspace?nw=nwuserranapuspendu24)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpushtogithub23%2Fwildlife-yolo-detector","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpushtogithub23%2Fwildlife-yolo-detector","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpushtogithub23%2Fwildlife-yolo-detector/lists"}