Comments (1)
Hi @forest-su, we have used the code in here: https://github.com/acheketa/pytorch-CAM
In order to create "Activation Maps" for 2D and 3D backbones, we have modified the code a lot. That's why we have not shared code here. However, here's what we did: Given an input clip of 16-frames, output features of 2D bacbone is weighted with 1 (since we want to see what activates 2D backbone regardless of which class it is) and created activation map is overelapped with the key-frame. For 3D backbone, we have used the output feature of layer 3 of ResNeXt-101 (where the depth dimension is 4) weight with again 1 and mapped to the corresponding frames on the input clip.
It is not too much effort, you just need to modify the forward pass of the YOWO model to return the desired output feature, set the weight to 1 and feed it to the CAM module in the above repo.
from yowo.
Related Issues (20)
- Working on dataset other than humans HOT 3
- Can you provide each type of map in ava
- About the envs setup HOT 2
- KeyError: 'exp_avg' HOT 1
- YOWO not using GPUs? HOT 1
- model is None
- how is the yolo.weights trained?
- Training YOWO on a custom dataset HOT 1
- Dropbox links for pre-trained models for J-HMDB, UCF and their annotations give 404 error HOT 1
- The [email protected] results reported in the paper are for validation or test splits of the AVA dataset?
- A stronger YOWO achieved by us. HOT 4
- not find yowo_jhmdb21_32f_best.pth
- plz,how to make and train my own dataset?
- test_video_ava.py error
- ava_detection_val_boxes_and_labels.csv is missing
- ava_classnames.json is missing
- /usr/home/sut path HOT 1
- animal action reconginition
- Training YOWO on a customized dataset HOT 2
- This code lacks any Conda environment or usage instructions.
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from yowo.