#python #compression_artifact_reduction #image_deblocking #image_denoising #image_restoration #image_super_resolution #lightweight_image_super_resolution #low_level_vision #real_world_image_super_resolution #transformer #vision_transformer

https://github.com/JingyunLiang/SwinIR

#python #dataset #deep_learning #im2latex #im2markup #image_processing #image2text #latex #latex_ocr #machine_learning #math_ocr #ocr #pytorch #transformer #vision_transformer #vit

https://github.com/lukas-blecher/LaTeX-OCR

#other #attention_mechanism #attention_mechanisms #awesome_list #computer_vision #deep_learning #detr #papers #self_attention #transformer #transformer_architecture #transformer_awesome #transformer_cv #transformer_models #transformer_with_cv #transformers #vision_transformer #visual_transformer #vit

https://github.com/cmhungsteve/Awesome-Transformer-Attention

#python #classification #computer_vision #object_detection #pytorch #self_supervised_learning #transformers #vision_transformer

https://github.com/alibaba/EasyCV

#python #computer_vision #convolutional_networks #embedding_vectors #embeddings #feature_extraction #feature_vector #image_processing #image_retrieval #machine_learning #milvus #pipeline #towhee #transformer #unstructured_data #video_processing #vision_transformer #vit

https://github.com/towhee-io/towhee

#python #foundation_models #representation_learning #vision_transformer

https://github.com/baaivision/EVA

#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 #auto_regressive_model #autoregressive_models #diffusion_models #generative_ai #generative_model #gpt #gpt_2 #image_generation #large_language_models #neurips #transformers #vision_transformer

VAR (Visual Autoregressive Modeling) is a new way to generate images that improves upon existing methods. It uses a "next-scale prediction" approach, which means it generates images from coarse to fine details, unlike the traditional method of predicting pixel by pixel. This makes VAR models better than diffusion models for the first time. You can try VAR on a demo website and generate images interactively, which is fun and easy. VAR also follows power-law scaling laws, making it efficient and scalable. The benefit to you is that you can create high-quality images quickly and easily, and even explore technical details through provided scripts and models.

https://github.com/FoundationVision/VAR