#jupyter_notebook #computer_vision #deep_learning #deep_neural_networks #image_classification #image_segmentation #object_detection #pytorch #tutorial #yolov5 #yolov6 #yolov7

https://github.com/roboflow-ai/notebooks

#python #deep_learning #image_classification #imagenet #mobilenet #pytorch #regnet #resnet #resnext #senet #shufflenet #swin_transformer

https://github.com/open-mmlab/mmclassification

#jupyter_notebook #computer_vision #deep_learning #image_classification #imagenet #neural_network #object_detection #pretrained_models #pretrained_weights #pytorch #semantic_segmentation #transfer_learning

https://github.com/Deci-AI/super-gradients

#cplusplus #ai #behaviour_analysis #cv #deep_learning #deepstream #face_recognition #feature_extraction #gstreamer #image_classification #image_enhancement #image_segmentation #license_plate_recognition #object_detection #opencv #reid #similarity_search #video_analysis #video_processing

https://github.com/sherlockchou86/VideoPipe

#python #deep_learning #hub #image_classification #instance_segmentation #machine_learning #obb #object_detection #pose #pytorch #tracking #ultralytics #yolo #yolo_world #yolo_world_v2 #yolo11 #yolov10 #yolov8 #yolov9

Ultralytics YOLO11 is a state-of-the-art model for object detection, segmentation, classification, and pose estimation. It is fast, accurate, and easy to use, making it suitable for various tasks. You can install it using pip (`pip install ultralytics`) and use it via the command line or Python scripts. The model comes with extensive documentation and community support through Discord, Reddit, and forums. Additionally, Ultralytics offers integrations with other AI platforms like Roboflow and ClearML to enhance your workflow. This tool benefits users by providing high-performance AI capabilities with minimal setup and robust community resources for assistance.

https://github.com/ultralytics/ultralytics

#javascript #annotation #annotation_tool #annotations #boundingbox #computer_vision #data_labeling #dataset #datasets #deep_learning #image_annotation #image_classification #image_labeling #image_labelling_tool #label_studio #labeling #labeling_tool #mlops #semantic_segmentation #text_annotation #yolo

Label Studio is a free, open-source tool that helps you label different types of data like images, audio, text, videos, and more. It has a simple and user-friendly interface that makes it easy to prepare or improve your data for machine learning models. You can customize it to fit your needs and export labeled data in various formats. It supports multi-user labeling, multiple projects, and integration with machine learning models for pre-labeling and active learning. You can install it locally using Docker, pip, or other methods, or deploy it in cloud services like Heroku or Google Cloud Platform. This tool streamlines your data labeling process and helps you create more accurate ML models.

https://github.com/HumanSignal/label-studio

#python #augmix #convnext #distributed_training #dual_path_networks #efficientnet #image_classification #imagenet #maxvit #mixnet #mobile_deep_learning #mobilenet_v2 #mobilenetv3 #nfnets #normalization_free_training #pretrained_models #pretrained_weights #pytorch #randaugment #resnet #vision_transformer_models

PyTorch Image Models (`timm`) is a comprehensive library that includes a wide range of state-of-the-art image models, layers, utilities, optimizers, and training scripts. Here are the key benefits `timm` offers over 300 pre-trained models from various families like Vision Transformers, ResNets, EfficientNets, and more, allowing you to choose the best model for your task.
- **Pre-trained Weights** You can easily extract features at different levels of the network using `features_only=True` and `out_indices`, making it versatile for various applications.
- **Optimizers and Schedulers** It provides several augmentation techniques like AutoAugment, RandAugment, and regularization methods like DropPath and DropBlock to enhance model performance.
- **Reference Training Scripts**: Included are high-performance training, validation, and inference scripts that support multiple GPUs and mixed-precision training.

Overall, `timm` simplifies the process of working with deep learning models for image tasks by providing a unified interface and extensive tools for training and evaluation.

https://github.com/huggingface/pytorch-image-models

#python #ade20k #image_classification #imagenet #mask_rcnn #mscoco #object_detection #semantic_segmentation #swin_transformer

The Swin Transformer is a powerful tool for computer vision tasks like image classification, object detection, semantic segmentation, and video recognition. It uses a hierarchical structure with shifted windows to efficiently process images, making it more efficient than other models. Here are the key benefits Swin Transformer achieves state-of-the-art results in various tasks such as COCO object detection, ADE20K semantic segmentation, and ImageNet classification.
- **Efficiency** The model supports multiple tasks including image classification, object detection, instance segmentation, semantic segmentation, and video action recognition.
- **Improved Speed** The model is integrated into popular frameworks like Hugging Face Spaces and PaddleClas, making it easy to use and deploy.

Overall, the Swin Transformer offers high accuracy, efficiency, and versatility, making it a valuable tool for various computer vision applications.

https://github.com/microsoft/Swin-Transformer

#python #artificial_intelligence #attention_mechanism #computer_vision #image_classification #transformers

This text describes a comprehensive implementation of Vision Transformers (ViT) in PyTorch, offering various models and techniques for image classification. Here’s the key information and benefits**
- The repository provides multiple ViT variants, including the original ViT, Simple ViT, NaViT, Deep ViT, CaiT, Token-to-Token ViT, CCT, Cross ViT, PiT, LeViT, CvT, Twins SVT, RegionViT, CrossFormer, ScalableViT, SepViT, MaxViT, NesT, MobileViT, XCiT, and others.
- Each variant introduces different architectural improvements such as efficient attention mechanisms, multi-scale processing, and innovative embedding techniques.
- The implementation includes pre-trained models and supports various tasks like masked image modeling, distillation, and self-supervised learning.

**Benefits** Users can choose from a wide range of ViT models tailored for different needs, such as efficiency, performance, or specific tasks.
- **Performance** Some models, like NaViT and ScalableViT, are designed to be more efficient in terms of computational resources and training time.
- **Ease of Use** The inclusion of various research ideas and techniques allows users to explore new approaches in vision transformer research.

Overall, this repository offers a powerful toolkit for anyone working with vision transformers, providing both practical solutions and cutting-edge research opportunities.

https://github.com/lucidrains/vit-pytorch

#python #annotation #annotation_tool #annotations #boundingbox #computer_vision #computer_vision_annotation #dataset #deep_learning #image_annotation #image_classification #image_labeling #image_labelling_tool #imagenet #labeling #labeling_tool #object_detection #pytorch #semantic_segmentation #tensorflow #video_annotation

CVAT is a powerful tool for annotating videos and images, especially useful for computer vision projects. It helps developers and companies annotate data quickly and efficiently. You can use CVAT online for free or subscribe for more features like unlimited data and integrations with other tools. It also offers a self-hosted option with enterprise support. CVAT supports many annotation formats and has automatic labeling options to speed up your work. It's widely used by many teams worldwide, making it a reliable choice for your data annotation needs.

https://github.com/cvat-ai/cvat