HugsVision VS Transformer-Explainability

Compare HugsVision vs Transformer-Explainability and see what are their differences.

HugsVision

HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision (by qanastek)
huggingface Transformers Computer Vision pretrained-models image-classification semantic-segmentation object-detection image-generation pythorch pytorch-transformers Yolo vit deit detr Pytorch Deep Learning Machine Learning state-of-the-art Bert torchvision
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pypi.org
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Transformer-Explainability

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks. (by hila-chefer)
Deep Learning vision-transformer bert-model Bert explainability transformer-interpretability perturbation attention-visualization visualize-classifications vit attention-matrix cvpr2021
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HugsVision Transformer-Explainability
1 1
188 1,660
- -
0.0 0.0
9 months ago 3 months ago
Jupyter Notebook Jupyter Notebook
MIT License MIT License
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HugsVision

Posts with mentions or reviews of HugsVision. We have used some of these posts to build our list of alternatives and similar projects.

Transformer-Explainability

Posts with mentions or reviews of Transformer-Explainability. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-04-25.
  • [Project] Recent Class Activation Map Methods for CNNs and Vision Transformers
    2 projects | /r/MachineLearning | 25 Apr 2021
    Not exactly the same but since you mentioned using ViT's attention outputs as a 2D feature map for the CAM you can consider this paper (Transformer Interpretability Beyond Attention Visualization) where they study the question of how to choose/mix the attention scores in a way that can be visualized (so similar to the CAMs). Maybe it can lead to better results. https://arxiv.org/abs/2012.09838 https://github.com/hila-chefer/Transformer-Explainability

What are some alternatives?

When comparing HugsVision and Transformer-Explainability you can also consider the following projects:

poolformer - PoolFormer: MetaFormer Is Actually What You Need for Vision (CVPR 2022 Oral)

pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.

shap - A game theoretic approach to explain the output of any machine learning model.

fashionpedia-api - Python API for Fashionpedia Dataset

T2T-ViT - ICCV2021, Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

ganspace - Discovering Interpretable GAN Controls [NeurIPS 2020]

multi-label-sentiment-classifier - How to build a multi-label sentiment classifiers with Tez and PyTorch

CoordConv

tf-metal-experiments - TensorFlow Metal Backend on Apple Silicon Experiments (just for fun)

Vision-Project-Image-Segmentation

deep-text-recognition-benchmark - PyTorch code of my ICDAR 2021 paper Vision Transformer for Fast and Efficient Scene Text Recognition (ViTSTR)

HugsVision vs poolformer Transformer-Explainability vs pytorch-grad-cam HugsVision vs Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning Transformer-Explainability vs shap HugsVision vs fashionpedia-api Transformer-Explainability vs T2T-ViT HugsVision vs ganspace Transformer-Explainability vs multi-label-sentiment-classifier HugsVision vs CoordConv Transformer-Explainability vs tf-metal-experiments HugsVision vs Vision-Project-Image-Segmentation Transformer-Explainability vs deep-text-recognition-benchmark