pytorch-grad-cam
pytorch-tutorial
| pytorch-grad-cam | pytorch-tutorial | |
|---|---|---|
| 5 | 3 | |
| 9,456 | 29,128 | |
| - | - | |
| 5.4 | 0.0 | |
| about 2 months ago | 9 months ago | |
| Python | Python | |
| MIT License | MIT License |
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pytorch-grad-cam
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Exploring GradCam and More with FiftyOne
For the two examples we will be looking at, we will be using pytorch_grad_cam, an incredible open source package that makes working with GradCam very easy. There are excellent other tutorials to check out on the repo as well.
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Which layers are doing image segmentation on AutoEncoders/U-NET?
https://github.com/jacobgil/pytorch-grad-cam.
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[D] Algorithm for view prediction?
I know I would like to use grad-CAM https://github.com/jacobgil/pytorch-grad-cam
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[P] Adapting Class Activation Maps for Object Detection and Semantic Segmentation
https://github.com/jacobgil/pytorch-grad-cam is a project that has a comprehensive collection of Pixel Attribution Methods for PyTorch (like the package name grad-cam that was the original algorithm implemented).
- [Project] Recent Class Activation Map Methods for CNNs and Vision Transformers
pytorch-tutorial
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PyTorch - What does contiguous() do?
I was going through this example of a LSTM language model on github (link).What it does in general is pretty clear to me. But I'm still struggling to understand what calling contiguous() does, which occurs several times in the code.
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How to 'practice' pytorch after finishing its basic tutorial?
I tried to move straight to practicing implementing papers and trying to understand other people's codes but failed miserably. I feel like there was too much of a gap between the basic tutorial and being able to implement ideas into code....hence the question: Is there any resource/way to practice pytorch in general? I did find this and this, but I just wanted to hear what others have gone through to become better at PyTorch up to the point they can build stuff from their own ideas
- [P] Probabilistic Machine Learning: An Introduction, Kevin Murphy's 2021 e-textbook is out
What are some alternatives?
Transformer-Explainability - [CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
mixture-of-experts - PyTorch Re-Implementation of "The Sparsely-Gated Mixture-of-Experts Layer" by Noam Shazeer et al. https://arxiv.org/abs/1701.06538
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
InceptionTime - InceptionTime: Finding AlexNet for Time Series Classification
pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch
Conv-TasNet - A PyTorch implementation of Conv-TasNet described in "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" with Permutation Invariant Training (PIT).
tf-keras-vis - Neural network visualization toolkit for tf.keras
BigGAN-PyTorch - The author's officially unofficial PyTorch BigGAN implementation.
Transformer-MM-Explainability - [ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
bonito - A PyTorch Basecaller for Oxford Nanopore Reads
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
OpenNMT-py - Open Source Neural Machine Translation and (Large) Language Models in PyTorch