https://github.com/alyetama/ainatype
AiNaType is an open-source Python project for automated wildlife sign image classification, leveraging YOLO models to identify categories like live animal, dead specimen, scat, and tracks.
https://github.com/alyetama/ainatype
ai cameratrap ecology inaturalist wildlife
Last synced: 19 days ago
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AiNaType is an open-source Python project for automated wildlife sign image classification, leveraging YOLO models to identify categories like live animal, dead specimen, scat, and tracks.
- Host: GitHub
- URL: https://github.com/alyetama/ainatype
- Owner: Alyetama
- License: mit
- Created: 2024-04-11T16:56:11.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-08-28T20:48:39.000Z (about 2 months ago)
- Last Synced: 2025-08-29T01:35:06.012Z (about 1 month ago)
- Topics: ai, cameratrap, ecology, inaturalist, wildlife
- Language: Python
- Homepage:
- Size: 188 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# AiNaType
AiNaType is an open-source Python project for automated image classification, primarily focused on identifying wildlife signs such as live animals, dead specimens, scat, and tracks using deep learning models. The repository leverages the Ultralytics YOLO framework and provides both API and Gradio app interfaces for prediction and demonstration.
## Features
- **YOLO-based Classification**: Utilizes YOLO models to classify images into categories like dead, live animal, scat, and tracks.
- **API Interface**: FastAPI-based endpoint (`/predict`) for programmatic image classification, supporting custom model weights and versioning.
- **Gradio Demo App**: User-friendly web app for interactive image uploads and quick predictions.
- **Annotation Utilities**: Scripts for exporting and processing annotation tasks with Label Studio integration.
- **Batch Prediction**: SQLite-based tracking of image predictions for bulk processing.
## Model Weights
Pre-trained model weights for AiNaType can be downloaded from the [Releases page](https://github.com/Alyetama/AiNaType/releases). Download the appropriate weights files and specify their path in your API or batch prediction scripts.
## Installation
1. Clone the repository:
```bash
git clone https://github.com/Alyetama/AiNaType.git
cd AiNaType
```
2. Install requirements:
```bash
pip install -r requirements.txt
```
3. (Optional) Set up environment variables for annotation export:
```
LABEL_STUDIO_URL=
API_KEY=
```
## Usage
### Gradio Demo
Run the demo app for interactive classification:
```bash
python gradio-app-demo.py
```
Visit the displayed URL to upload images and view predictions.
### API Server
Start the FastAPI server:
```bash
python api-ainatype.py -w -m
```
Send a POST request to `/predict` with JSON payload containing the image URL and project information.
### Batch Prediction
Process a directory of images and store results in a SQLite database:
```bash
python predict.py -d -m -i
```
## Annotation Export
Export annotations from Label Studio via shell script:
```bash
bash misc/export_annotations.sh
```
## Project Structure
- `api-ainatype.py`: FastAPI server implementation.
- `gradio-app-demo.py`: Gradio demo web app.
- `predict.py`: Batch prediction script with SQLite tracking.
- `misc/get_annotations.py`: Python script to fetch and save annotations.
## License
This project is licensed under the MIT License. See [LICENSE](LICENSE) for details.
## Author
Developed by [Mohammad Alyetama](https://github.com/Alyetama).