Transformer-MM-Explainability VS clip-italian

Compare Transformer-MM-Explainability vs clip-italian and see what are their differences.

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. (by hila-chefer)
Transformers Transformer vqa detr Visualization explainability explainable-ai Interpretability lxmert visualbert clip
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clip-italian

CLIP (Contrastive Language–Image Pre-training) for Italian (by clip-italian)
clip Transformers transformers-models contrastive-loss huggingface Dataset
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Transformer-MM-Explainability clip-italian
3 1
709 172
- 1.2%
0.0 2.0
8 months ago 12 months ago
Jupyter Notebook Jupyter Notebook
MIT License -
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Transformer-MM-Explainability

Posts with mentions or reviews of Transformer-MM-Explainability. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-28.

clip-italian

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

What are some alternatives?

When comparing Transformer-MM-Explainability and clip-italian you can also consider the following projects:

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

Basic-UI-for-GPT-J-6B-with-low-vram - A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.

TorchDrift - Drift Detection for your PyTorch Models

clip-retrieval - Easily compute clip embeddings and build a clip retrieval system with them

explainerdashboard - Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

TargetCLIP - [ECCV 2022] Official PyTorch implementation of the paper Image-Based CLIP-Guided Essence Transfer.

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

browser-ml-inference - Edge Inference in Browser with Transformer NLP model

pytea - PyTea: PyTorch Tensor shape error analyzer

WeightWatcher - The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both

univec - Multilingual CLIP

Transformer-MM-Explainability vs pytorch-grad-cam clip-italian vs Basic-UI-for-GPT-J-6B-with-low-vram Transformer-MM-Explainability vs TorchDrift clip-italian vs clip-retrieval Transformer-MM-Explainability vs explainerdashboard clip-italian vs TargetCLIP Transformer-MM-Explainability vs shap clip-italian vs browser-ml-inference Transformer-MM-Explainability vs pytea Transformer-MM-Explainability vs WeightWatcher Transformer-MM-Explainability vs cleverhans Transformer-MM-Explainability vs univec