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| title: Object Detection - YOLOV3 | |
| emoji: 🚀 | |
| colorFrom: green | |
| colorTo: purple | |
| 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 | |
| Demonstration of Object Detection using YOLOv3. This model is trained using Pytorch Lightning from scratch on PASCAL VOC dataset with 20 classes. | |
| The input image resolution used for the training was 416 x 416. | |
| The app also shows the saliency maps of the input images generated using EigenCAM explainability method. | |
| The model supports following classes: | |
| * aeroplane ✈️ | |
| * bicycle 🚲 | |
| * bird 🐦 | |
| * boat 🛥️ | |
| * bottle 🍾 | |
| * bus 🚌 | |
| * car 🚗 | |
| * cat 🐈 | |
| * chair 🪑 | |
| * cow 🐄 | |
| * diningtable | |
| * dog 🐕 | |
| * horse 🐎 | |
| * motorbike 🏍️ | |
| * person 👱 | |
| * pottedplant | |
| * sheep 🐑 | |
| * sofa 🛋️ | |
| * train 🚊 | |
| * tvmonitor 📺 | |
| ### Steps to Use: | |
| * Upload an image and click submit to generate the detections, the corresponding bounding boxes, and the saliency maps. | |
| * Control the degree of detection threshold and the IOU threshold to filter the predictions | |