Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking
- ultralytics
- yolov8
- yolo
- deep-learning
Repository Index
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Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
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.
We write your reusable computer vision tools. 💜
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
OpenMMLab Detection Toolbox and Benchmark
- object-detection
- instance-segmentation
- fast-rcnn
- faster-rcnn
Label Studio is a multi-type data labeling and annotation tool with standardized output format
- computer-vision
- deep-learning
- image-annotation
- annotation-tool
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
- mask-rcnn
- tensorflow
- object-detection
- instance-segmentation
The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
- open-vocabulary-detection
- open-vocabulary-segmentation
- data-generation
- automatic-labeling-system
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.
- video-annotation
- computer-vision
- computer-vision-annotation
- image-annotation
Image annotation with Python. Supports polygon, rectangle, circle, line, point, and AI-assisted annotation.
- image-annotation
- python
- computer-vision
- annotations
Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
- object-detection
- instance-segmentation
- faster-rcnn
- mask-rcnn
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
- deep-learning
- pytorch
- grad-cam
- visualizations
Translated100+ Chinese Word Vectors
- chinese
- chinese-word-segmentation
- embeddings
- word-embeddings
The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Unsupervised text tokenizer for Neural Network-based text generation.
- neural-machine-translation
- natural-language-processing
- word-segmentation
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
- segmentation
- image-processing
- pspnet
- unet
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
- pytorch
- unet
- pytorch-unet
- semantic-segmentation
Translated[CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4o. An open-source multimodal dialogue model with performance close to GPT-4o
- image-classification
- image-text-retrieval
- llm
- semantic-segmentation
CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc.
- natural-language-processing
- nlp
- nlp-parsing
- named-entity-recognition
Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
A simple, fully convolutional model for real-time instance segmentation.
- realtime
- real-time
- instance-segmentation
- yolact
pySLAM is a hybrid Python/C++ Visual SLAM pipeline supporting monocular, stereo, and RGB-D cameras. It provides a broad set of modern local and global feature extractors, multiple loop-closure strategies, a volumetric reconstruction module, integrated depth-prediction models, and semantic segmentation capabilities for enhanced scene understanding.
- 3d-reconstruction
- depth-prediction
- feature-matching
- global-features