#python #convolutional_neural_networks #cvpr #cvpr2020 #efficient_inference #fbnet #imagenet #mobilenet #mobilenetv3 #model_compression #tensorflow

https://github.com/huawei-noah/ghostnet

#javascript #annotate_images #annotation_tool #audio #classification #computer_vision #dataset #deep_learning #desktop #entity_recognition #image_labeling_tool #image_segmentation #imagenet #named_entity_recognition #semantic_segmentation #text_annotation #text_labeling #udt

https://github.com/UniversalDataTool/universal-data-tool

#python #convolutional_neural_networks #deep_learning #imagenet #jax #pytorch #tensorflow2 #transfer_learning

https://github.com/google-research/big_transfer

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

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

#typescript #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 #semantic_segmentation #tensorflow #video_annotation

https://github.com/openvinotoolkit/cvat

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

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

#python #damo_yolo #deep_learning #imagenet #nas #object_detection #onnx #pytorch #tensorrt #yolo #yolov5

https://github.com/tinyvision/DAMO-YOLO

#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

#python #deeplab_v3_plus #deeplabv3 #fpn #hacktoberfest #image_processing #image_segmentation #imagenet #linknet #models #pretrained_backbones #pretrained_models #pretrained_weights #pspnet #pytorch #segmentation #segmentation_models #semantic_segmentation #unet #unet_pytorch #unetplusplus

https://github.com/qubvel/segmentation_models.pytorch

#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 #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