Feature visualization

You can use the tool to visualize the feature map of ResNet-50 through Grad-CAM algorithm.

Usage

For examples, if your input images are stored on ./images directory, and the output directory is ./results.

Visualize of ImageNet pretrained weights

python feat_visualization.py --image-dir images --output-dir results --weights-type imagenet

Visualize of custom pretrained weights

For example, if we try to visualize the VCLR pretrained weights, assume the weight is stored at ./vclr_torch.pth.

Note, you must transfer the custom weights to torchvision style firstly.

You can refer the README to do it, or download our pretrained model (Download Link).
```
python feat_visualization.py --image-dir images --output-dir results --weights-type custom --weights-path vclr_torch.pth
```

Visual example

Arch	Image	ImageNet	SeCo	VCLR
ResNet-50

Reference

Grad-Cam Implementation

https://arxiv.org/abs/1610.02391
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

other nice visualization tools:
- pytorch-grad-cam