Ailia input shape : (1,3,224,224)
Range : [-1.0, 1.0]
If specified model is base_1k, small_1k or tiny_1k, it predicts image class from label_table.txt.
predicted class = 981(ballplayer, baseball player)
If specified model is cifar10, it predicts image class from ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'].
predicted class = 1(automobile)
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 convnext.py
If you want to specify the input image, put the image path after the --input option.
$ python3 convnext.py --input 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 convnext.py --video VIDEO_PATH
By adding the --model option, you can choose model.
$ python3 convnext.py --input IMAGE_PATH --model MODEL_TYPE
(ex)$ python3 convnext.py --input IMAGE_PATH --model base_1k
(ex)$ python3 convnext.py --input IMAGE_PATH --model small_1k
(ex)$ python3 convnext.py --input IMAGE_PATH --model tiny_1k
(ex)$ python3 convnext.py --input IMAGE_PATH --model cifar10
A PyTorch implementation of ConvNeXt
ONNX opset = 10
Pytorch 1.7.1
convnext_base_1k_224_ema.onnx.prototxt
convnext_small_1k_224_ema.onnx.prototxt