ultralytics / ultralytics

Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking

ultralytics / yolov5

Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.

huggingface / pytorch-image-models

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

lucidrains / vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

cvat-ai / cvat

CVAT is a leading data annotation platform for image, video, audio, and 3D datasets. It offers open-source, cloud, and enterprise products, as well as labeling services, with AI-assisted labeling, quality assurance, team collaboration, analytics, and developer APIs.

CVHub520 / X-AnyLabeling

X-AnyLabeling: A lightweight, efficient, and unified cross-platform desktop application for annotating text, image, video, point cloud and multimodal data, combining versatile built-in tools with state-of-the-art AI models and flexible multi-format export.

PaddlePaddle / PaddleClas

A treasure chest for visual classification and recognition powered by PaddlePaddle

autodistill / autodistill

Images to inference with no labeling (use foundation models to train supervised models).

ailia-ai / ailia-models

The collection of pre-trained, state-of-the-art AI models for ailia SDK

ufoym / imbalanced-dataset-sampler

A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.

yoshitomo-matsubara / torchdistill

A coding-free framework built on PyTorch for reproducible deep learning studies. PyTorch Ecosystem. 🏆26 knowledge distillation methods presented at TPAMI, CVPR, ICLR, ECCV, NeurIPS, ICCV, AAAI, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.

open-edge-platform / geti

Build, train, optimize, and run computer vision models locally, from raw images to live inference. Open source, optimized for Intel XPU (CPU-only and CUDA also supported).

PSRben / VisionHOPE

Official PyTorch implementation of VisionHOPE: Visual Backbones as Self-Modifying Learning Systems.

jasmcaus / caer

High-performance Vision library in Python. Scale your research, not boilerplate.

albumentations-team / AlbumentationsX

Image augmentation for computer vision. AGPL-3.0-only or commercial licensing.

oleost / VisionState

Turn your cameras into Home Assistant sensors with a local AI: garage door open/closed, people and cars, water, gas and power meters read by any camera or ESP32-CAM. 100% local, any CPU.

AhmadJeddi / Plant-Disease-Classification

A ResNet50-DCBAM deep learning framework for plant disease classification using the PlantVillage dataset with reproducible training and evaluation pipelines.

yoheinakajima / glance

Ask an open vision-language model typed questions about an image and get probabilities back, on your own machine.