This repo contains gradio demo codes of:
- visualization of image preprocessing
- image classification with ResNet50
- semantic segmentation with DeepLabV3
- object detection with YOLOX
All applications can be accessible on localhost:7860
.
cd <PROJECT_ROOT>
# download yolox pre-trained weight
wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.onnx \
-O weights/yolox_s.onnx
# build docker image
sh scripts/build_docker.sh
# run docker container
sh scripts/run_docker.sh
in docker environment
# visualize preprocess
python src/visualize_preprocess.py
# image classification
python src/image_classification.py
# semantic segmentation
python src/semantic_segmentation.py
# object detection
python src/object_detection.py
- gradio-app/gradio: Create UIs for your machine learning model in Python in 3 minutes
- torchvision.models — Torchvision 0.11.0 documentation
- Megvii-BaseDetection/YOLOX: YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
- 【Gradio / PyTorch】Gradioで画像分類を行うデモアプリを簡単に作る
- 【Gradio / PyTorch】Gradioで画像の前処理を可視化するデモを作る
- 【Gradio / PyTorch】Gradioでセマンティックセグメンテーションを行うデモアプリを作る
- 【Gradio / PyTorch】YOLOXで物体検出を行うデモアプリを作る