image-classification

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.

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

Detect and classify product defects in real-time using AI and computer vision to improve manufacturing quality control processes.

🩻 Classify chest X-ray images as NORMAL or PNEUMONIA using a custom Hybrid CNN with Residual Blocks and explore results with an interactive Streamlit app.

🔍 Audit source code with Dev Audit Tool (DAT) for security, compliance, and quality, ensuring robust development workflows across multiple platforms.

🩻 Build a deep learning model for pneumonia detection in chest X-rays with a user-friendly web interface using TensorFlow and Streamlit.

Deep learning pipeline for multi-type ISIC 2018 skin lesion classification with CNNs, preprocessing, augmentation, and training and inference support 🐙.

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).

Local-first AI photo culling for professional photographers — 6-axis rubric, XMP/IPTC export, Lightroom & Capture One ready.

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

Teach a local AI what your camera sees and turn it into Home Assistant sensors — garage door open/closed, gate shut, car parked. 100% local, any CPU.

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

Educational TensorFlow/Keras CNN for multi-class skin-image classification and model evaluation.

A computer vision pipeline for plant disease classification from leaf images using deep learning.

FreshLens — Modular EfficientNet-B0 fruit freshness classification with open-set rejection, reproducible training/evaluation, and Streamlit inference.

Histology tissue classification using EfficientNet and PyTorch

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

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

Supervision is your essential toolkit for computer vision. From data loading to real-time zone counting, it provides the building blocks so you can focus on building applications around your models. ml-pipes-supervision provides Supervision capabilities as composable operators in ml-pipes.

BacteriaCV - Dataset preparation pipeline for DIBaS bacterial microscopy images with species mapping, leakage-safe 70/20/10 splits, and five-fold training folds.