I have noted that while testing, images are being distorted. The colors are not important I know they are normalized. But the problem is for example this image is so cropped that it is almost impossible to detect it as a pants. I am not sure if the training data is trained on such samples because if it did, then you should reconsider retraining model with correct transformations. This distortion causes too much information loss IMO.
78 might be related to this fact.
Also only the vgg_16 global pooling model is working on attribute and category prediction. Other models are very poorly optimized. They are predicting very absurd results.
I have noted that while testing, images are being distorted. The colors are not important I know they are normalized. But the problem is for example this image is so cropped that it is almost impossible to detect it as a pants. I am not sure if the training data is trained on such samples because if it did, then you should reconsider retraining model with correct transformations. This distortion causes too much information loss IMO.
78 might be related to this fact.
Also only the vgg_16 global pooling model is working on attribute and category prediction. Other models are very poorly optimized. They are predicting very absurd results.