Layer choice about yolo-v8-cam HOT 2 CLOSED

Hey thanks for your kind words. This is a good question, it made me realise one extra thing about this project. I shall add an update for it and let you know more about this soon. For starters you can also try using print(model.model) for understanding the structure.

from yolo-v8-cam.

To extract the specific layer within the model object, you will have to loop across its Sequential Block and extract the exact layer you need. You can then use it to generate the heatmap for it.

For example to extract the last Conv layer, you can pass it like this:

from torch.nn import Conv2d

c2f_module = model.model.model[-1]

tgt_layer = []

# # Iterate through the sub-modules of C2f
for sub_module in c2f_module.modules():
    if isinstance(sub_module, Conv2d):
        if sub_module.in_channels == 80 and sub_module.out_channels == 80 and sub_module.kernel_size == (1, 1) and sub_module.stride == (1, 1):
            tgt_layer = sub_module

print(tgt_layer)

You can then pass this extracted layer stored in tgt_layer as a list:

target_layers = [tgt_layer]

cam = EigenCAM(model, target_layers, use_cuda=False)
grayscale_cam = cam(rgb_img)[0, :, :]
cam_image = show_cam_on_image(img, grayscale_cam, use_rgb=True)
Image.fromarray(cam_image)

Rest of the steps remain same. The results may not be as you expect though with regards to the generated heatmaps. You can try experimenting with different layers and see the outputs.

from yolo-v8-cam.