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awesome-machine-learning-interpretability

A curated list of awesome machine learning interpretability resources.

bagcams

bcam

Source code of the paper: Background-aware Classification Activation Map for Weakly Supervised Object Localization"

best-incremental-learning

An Incremental Learning, Continual Learning, and Life-Long Learning Repository

cct

:page_facing_up: Semi-Supervised Semantic Segmentation with Cross-Consistency Training (CVPR 2020).

clean-fid

PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]

clinitokenizer

Sentence tokenizer for clinical/medical text.

cunet

da-wsol_cvpr2022

Official implementation of the paper ``Weakly Supervised Object Localization as Domain Adaption"

dataset

医学影像数据集列表 『An Index for Medical Imaging Datasets』

deep-spectral-segmentation

[CVPR 2022] Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization

drw

This is the official code of our paper: Dynamic Random Walk for Superpixel Segmentation

encut_rwrt

The official code of the paper: Explored Normalized Cut with Random Walk Refining Term for Image Segmentation

enhancing-transformers

An unofficial implementation of both ViT-VQGAN and RQ-VAE in Pytorch

iqa-pytorch

👁️ 🖼️ 🔥PyTorch Toolbox for Image Quality Assessment, including LPIPS, FID, NIQE, NRQM(Ma), MUSIQ, NIMA, DBCNN, WaDIQaM, BRISQUE, PI and more...

kfac-pytorch

Pytorch implementation of KFAC and E-KFAC (Natural Gradient).

latent-diffusion

High-Resolution Image Synthesis with Latent Diffusion Models

lecoodtest

Leetcode_solutions

llama-recipes

Examples and recipes for Llama 2 model

llama2-lora-fine-tuning

llama2 finetuning with deepspeed and lora

llm-continual-learning-papers

Must-read Papers on Large Language Model (LLM) Continual Learning

lnsnet

Pytorch Implementation of LNSNet for Superpixel Segmentation

mib

Official code for Modeling the Background for Incremental Learning in Semantic Segmentation https://arxiv.org/abs/2002.00718

pytorch-grad-cam

Advanced AI Explainability for computer vision. Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Examples and applications for classification, object detection, segmentation, explaining image similarity and more.

pytorch-simple-maskrcnn

A PyTorch implementation of simple Mask R-CNN

snl_iccv2021

Official implementation of the paper ``Unifying Nonlocal Blocks for Neural Networks'' (ICCV'21)

spectral-nonlocal-block

v2l-tokenizer

vit-cifar

PyTorch implementation for Vision Transformer[Dosovitskiy, A.(ICLR'21)] modified to obtain over 90% accuracy FROM SCRATCH on CIFAR-10 with small number of parameters (= 6.3M, originally ViT-B has 86M).