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group_vit

README.md

GroupViT: Semantic Segmentation Emerges from Text Supervision

Input

Input

(Image from https://github.com/NVlabs/GroupViT/blob/main/demo/examples/voc.jpg)

Output

Output

Usage

Automatically downloads the onnx and prototxt files on the first run. It is necessary to be connected to the Internet while downloading.

For the sample image,

$ python3 group_vit.py

If you want to specify the input image, put the image path after the --input option.
You can use --savepath option to change the name of the output file to save.

$ python3 group_vit.py --input IMAGE_PATH --savepath SAVE_IMAGE_PATH

By adding the --video option, you can input the video.
If you pass 0 as an argument to VIDEO_PATH, you can use the webcam input instead of the video file.

$ python3 group_vit.py --video VIDEO_PATH

You can specify the "model type" by specifying after the --model_type option. The model type is selected from "yfcc", "redcap".

$ python3 group_vit.py --model_type yfcc

To add an additional class label, specify it after the --additional-class option. This can be specified with list of multiple items.

$ python3 group_vit.py --additional-class bookshelf

Reference

Framework

Pytorch

Model Format

ONNX opset=11

Netron

group_vit_gcc_yfcc_30e-74d335e6.onnx.prototxt
group_vit_gcc_yfcc_mlc.onnx.prototxt
group_vit_gcc_redcap_30e-3dd09a76.onnx.prototxt
group_vit_gcc_redcap_mlc.onnx.prototxt