clothing-detection

clothing-detection

Input

(Image from https://github.com/simaiden/Clothing-Detection/blob/master/tests/0000003.jpg)

Shape : (1, 3, 416, 416)
Range : [0.0, 1.0]

Output

modanet

df2

Category

DATASETS_CATEGORY = {
    'modanet': [
        "bag", "belt", "boots", "footwear", "outer", "dress", "sunglasses",
        "pants", "top", "shorts", "skirt", "headwear", "scarf/tie"
    ],
    'df2': [
        "short sleeve top", "long sleeve top", "short sleeve outwear", "long sleeve outwear",
        "vest", "sling", "shorts", "trousers", "skirt", "short sleeve dress",
        "long sleeve dress", "vest dress", "sling dress"
    ]
}

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 clothing-detection.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 clothing-detection.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 clothing-detection.py --video VIDEO_PATH

By adding the -d df2 option, you can use df2 model.

Reference

• Clothing-Detection

Framework

ONNX Runtime

Model Format

ONNX opset=10

Netron

yolov3-modanet.opt.onnx.prototxt

yolov3-df2.opt.onnx.prototxt