Comments (4)
@RaymondKirk if the abstractions around detectron2 makes it harder to use with Captum, you could try considering using the detection models in torchvision, which can be imported and used without a trainer / config system.
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Thank you for the questions, @RaymondKirk ! Do you want to understand the predictions or do you want to use attributions as an edge detector, how do you see it to be used with Detectron2 ?
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@NarineK Detectron2 provides a simple way to run popular detection and segmentation models, the forward pass is generally wrapped in a general trainer function, so isn't too trivially used with this package. I think it would help a lot of people understand how to use this package if an example was made with a Detecron2 model (or even a standard PyTorch model), and would really enhance this repo! So my question was mostly wondering if there's any plans for including more advanced deep detectors RCNNs etc in the examples?
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@fmassa Thank you for your input, I'll close this issue as it was merely a sugestion for enhancement.
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Related Issues (20)
- Setting Rule for LRP on different activation function.
- Example Reference
- Error in the implementation of the LRP Alpha1_Beta0_Rule
- Feature attribution for darts model HOT 2
- Layer Feature Attribution for RNN models HOT 2
- Example for LMGradientAttribution is missing. HOT 4
- AttributeError: 'NoneType' object has no attribute 'relevance_input' HOT 4
- Error using Integrated Gradients on MPS HOT 1
- ImportError for 'TextTemplateFeature' and 'TextTokenFeature' from 'captum.attr' HOT 2
- Addition of Important Direction Gradient Integration (IDGI) framework
- Explainability for embeddings
- Uninitialized target_tokens Variable in LLMGradientAttribution.attribute Method
- what is the meaning of contribution? HOT 1
- For sentence classification using BERT, PAD token is used in IG/Deeplift? HOT 1
- Integrated Gradients for Llama2
- BertForMultipleChoice works without captum, breaks with it?
- Possible to modify initial LRP score to compute CLRP(contrastive lrp)?
- Incorrect indexing for max pooling layers with DeepLIFT HOT 1
- Integrated gradient on a function of models
- NoiseTunnel on LayerIntegratedGradients Perturbs Input Instead of Attribution Target (RunetimeError)
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