Please provide the following information when requesting support.
• Hardware (Quadro GV100)
• Network Type Image Classification
• How to reproduce the issue ? Run the inference on the pre-trained image classification models.
I want to use the pretrained image classification models as it is and see their performances on the embedded platforms. The models that I used are present here. Before deployment I want to test that the models are working well in Python, so I download these models and try to run simple inference. Upon some investigations I found that these models are trained on a subset of the open images dataset (only 176 classes are used). I load a jpg image, I perform the imagenet normalization. I.e. scale the image by dividing the pixel values by 255, and then subtract the mean and divide by the variance. Standard PyTorch way. When I run the inference the results are almost always wrong, although the provided table suggest that the accuracies are +70% on almost all models. The class labels that I am using are
labels.txt (1.5 KB). Could you please help me to understand or fix this? Thank you.
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