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bert-pytorch

Google AI 2018 BERT pytorch implementation

bert_wjf

TensorFlow code and pre-trained models for BERT

clip-retrieval

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

clip_wjf

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

d2l-zh

《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被60多个国家的400多所大学用于教学。

deit_wjf

Official DeiT repository

diffengine

Diffusers training with mmengine

diffusers

🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch

eva_wjf

EVA Series: Visual Representation Fantasies from BAAI

fastertransformer

Transformer related optimization, including BERT, GPT

get3d

grounded-segment-anything

Grounded-SAM: Marrying Grounding-DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

img2dataset

Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine.

insightface_wjf

State-of-the-art 2D and 3D Face Analysis Project

llama

Inference code for LLaMA models

llava_wjf

Large Language-and-Vision Assistant built towards multimodal GPT-4 level capabilities.

minigpt-4

MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models

mmselfsup

OpenMMLab Self-Supervised Learning Toolbox and Benchmark

nanogpt_wjf

The simplest, fastest repository for training/finetuning medium-sized GPTs.

nerf

Code release for NeRF (Neural Radiance Fields)

open_clip

An open source implementation of CLIP.

paper-reading

深度学习经典、新论文逐段精读

pytorch-grad-cam_wjf

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

pytorch-image-models

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

segment-everything-everywhere-all-at-once

Official implementation of the paper "Segment Everything Everywhere All at Once"

stable-diffusion

A latent text-to-image diffusion model

stable-diffusion-webui

Stable Diffusion web UI

syn-rep-learn

Learning from synthetic data - code and models

transfg_wjf

This is the official PyTorch implementation of the paper "TransFG: A Transformer Architecture for Fine-grained Recognition" (Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, Alan Yuille).