deepchecks VS Transformer-MM-Explainability

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

deepchecks

Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production. (by deepchecks)

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)
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deepchecks Transformer-MM-Explainability
15 3
3,350 704
1.6% -
8.2 0.0
13 days ago 8 months ago
Python Jupyter Notebook
GNU General Public License v3.0 or later MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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deepchecks

Posts with mentions or reviews of deepchecks. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-06-18.

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.

What are some alternatives?

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

great_expectations - Always know what to expect from your data.

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

evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b

TorchDrift - Drift Detection for your PyTorch Models

model-validation-toolkit - Model Validation Toolkit is a collection of tools to assist with validating machine learning models prior to deploying them to production and monitoring them after deployment to production.

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

feast - Feature Store for Machine Learning

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

postgresml - The GPU-powered AI application database. Get your app to market faster using the simplicity of SQL and the latest NLP, ML + LLM models.

clip-italian - CLIP (Contrastive Languageā€“Image Pre-training) for Italian

giskard - šŸ¢ Open-Source Evaluation & Testing framework for LLMs and ML models

pytea - PyTea: PyTorch Tensor shape error analyzer