Comments (2)
okay, thank you for your patient answer.
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Hey. You can use Grad-CAM to get an "abnormal class heatmap"; it's another gradient-based explanation method. However, you lose some advantages of FCDD, as FCDD's explanations are directly tied to the anomaly score. Feel free to try it, though.
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Related Issues (20)
- Installing and running with CUDA in Windows 10 raises Pickle error HOT 2
- Can I run it on a cpu? HOT 2
- Providing ground truth maps to prevent purely black or purely white maps HOT 3
- FCDD model without bias raises AssertionError: VGG net is only supported with bias atm! HOT 2
- how to generate ground truth map HOT 1
- Change the OE dataset for training on custom data HOT 11
- When I run this program every time, the result on the same category is different, and I can't make further improvement, can you recommend a random seed? Which position should be appropriate? Can you tell me, thanks! HOT 1
- The output of the model is often nan, what can be done to avoid the output of nan. Also because of the output of nan, the roc indicator cannot be calculated, can you tell me?thank you. HOT 1
- Multiple classes with one-vs-rest training raises AttributeError HOT 1
- Image level AUC-ROC for MV-Tech dataset HOT 2
- Error running custom.py using semi-supervised mode with multiple classes HOT 2
- Kill the runners/run_custom.py
- Training on grayscale images HOT 2
- noise_mode for custom data HOT 1
- Train fcdd with rectangular image HOT 1
- Save the model as torchscript HOT 1
- Normal GT maps also contains the red spots HOT 1
- Training with 0 as the anomalous label HOT 2
- MVTec-AD with oe-limit HOT 2
- Confused when trying to run inference with trained model HOT 2
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