#jupyter_notebook #asr #asr_benchmark #colab #english #enterprise_grade_stt #german #pretrained_models #pytorch #silero_models #spanish #speech_recognition #speech_to_text #stt #stt_benchmark

https://github.com/snakers4/silero-models

#python #image_classification #image_recognition #pretrained_models #knowledge_distillation #product_recognition #autoaugment #cutmix #randaugment #gridmask #deit #repvgg #swin_transformer #image_retrieval_system

https://github.com/PaddlePaddle/PaddleClas

#python #bert #deep_learning #knowledge_pretrain #nlp #pretrained_models #pytorch #transformers

https://github.com/alibaba/EasyNLP

#python #pretrained_models #pytorch #text_to_image #transformer

https://github.com/THUDM/CogView2

#python #caffe #computer_vision #coreml #edgetpu #keras #mediapipe #model #model_zoo #models #onnx #openvino #pretrained_models #pytorch #tensorflow #tensorflow_lite #tensorflowjs #tf_trt #tfjs #tflite #tflite_models

https://github.com/PINTO0309/PINTO_model_zoo

#python #bert #embedding #ernie #information_extraction #neural_search #nlp #paddlenlp #pretrained_models #question_answering #search_engine #semantic_analysis #sentiment_analysis #seq2seq #transformer #transformers #uie

https://github.com/PaddlePaddle/PaddleNLP

#python #computer_vision #contrastive_loss #deep_learning #language_model #multi_modal_learning #pretrained_models #pytorch #zero_shot_classification

https://github.com/mlfoundations/open_clip

#jupyter_notebook #overlapped_speech_detection #pretrained_models #pytorch #speaker_change_detection #speaker_diarization #speaker_embedding #speaker_recognition #speaker_verification #speech_activity_detection #speech_processing #voice_activity_detection

https://github.com/pyannote/pyannote-audio

#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 #chinese #clip #computer_vision #contrastive_loss #coreml_models #deep_learning #image_text_retrieval #multi_modal #multi_modal_learning #nlp #pretrained_models #pytorch #transformers #vision_and_language_pre_training #vision_language

This project is about a Chinese version of the CLIP (Contrastive Language-Image Pretraining) model, trained on a large dataset of Chinese text and images. Here’s what you need to know This model helps you quickly perform tasks like calculating text and image features, cross-modal retrieval (finding images based on text or vice versa), and zero-shot image classification (classifying images without any labeled examples).
- **Ease of Use** The model has been tested on various datasets and shows strong performance in zero-shot image classification and cross-modal retrieval tasks.
- **Resources**: The project includes pre-trained models, training and testing codes, and detailed tutorials on how to use the model for different tasks.

Overall, this project makes it easy to work with Chinese text and images using advanced AI techniques, saving you time and effort.

https://github.com/OFA-Sys/Chinese-CLIP