Spaces:
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title: Yolo V3 | |
emoji: π | |
colorFrom: gray | |
colorTo: blue | |
sdk: gradio | |
sdk_version: 3.40.1 | |
app_file: app.py | |
pinned: false | |
license: mit | |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
# YoloV3 object detection model- Interactive Interface | |
This project Impliments a simple Gradio interface to perform inference on YoloV3 object detection. | |
## Task : | |
The task involves performing detection on the Pascal voc dataset using the YoloV3 model built with PyTorch and PyTorch Lightning. | |
## Files : | |
1. `requirements.txt`: Contains the necessary packages required for installation. | |
2. `model.py`: Contains the YoloV3 model architecture. | |
3. `YoloV3.pth`: Trained model checkpoint file containing model weights. | |
4. `examples/`: Folder containing example images (e.g., car.jpg, home.jpg, etc.). | |
5. `app.py`: Contains the Gradio code for the interactive interface. Users can select input images or examples of the model that detects objects. | |
## Implementation | |
The following features are implemented using Gradio: | |
1. **Upload and Select Images:** Users can upload new images or select from a set of example images. | |
## Usage | |
1. Run the `app.py` script to launch the interactive Gradio interface. | |