#python #ai #artificial_intelligence #computer_vision #coreml #deep_learning #high_resolution #machine_learning #matting #onnx #pytorch #real_time #tensorflow #tfjs #video_matting

https://github.com/PeterL1n/RobustVideoMatting

#cplusplus #audio_processing #audio_production #audio_research #audio_unit #juce #pybind11 #python #tensorflow #vst3 #vst3_host

https://github.com/spotify/pedalboard

#python #contrastive_learning #deep_learning #metric_learning #nearest_neighbor_search #nearest_neighbors #similarity_learning #similarity_search #tensorflow

https://github.com/tensorflow/similarity

#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

#other #classification #convolutional_neural_networks #dataset #datasets #deep_learning #deep_neural_networks #image_classification #keras #machine_learning #python #pytorch #remote_sensing #satellite_data #satellite_imagery #satellite_images #sentinel #tensorflow

https://github.com/robmarkcole/satellite-image-deep-learning

#python #awesome #awesome_list #best_of #data_science #data_visualization #data_visualizations #deep_learning #keras #machine_learning #nlp #python_library #pytorch #scikit_learn #tensorflow

https://github.com/ml-tooling/best-of-ml-python

#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 #federated_analytics #federated_learning #federated_learning_framework #fleet_intelligence #fleet_learning #flower #keras_federated_learning #pytorch_federated_learning #tensorflow_federated_learning

https://github.com/adap/flower

#python #bumble #efficientnet #image_classification #tensorflow

https://github.com/bumble-tech/private-detector

#python #deep_learning #neural_ode #pytorch #recurrent_neural_networks #sequence_models #tensorflow

https://github.com/raminmh/CfC

#python #document_ai #document_image_analysis #document_layout_analysis #document_parser #document_understanding #layoutlm #nlp #ocr #publaynet #pubtabnet #pytorch #table_detection #table_recognition #tensorflow

https://github.com/deepdoctection/deepdoctection

#javascript #ai #camera #computer_vision #deep_learning #edge #face_detection #face_recognition #home_assistant #machine_learning #nvidia_jetson_nano #object_detection #python #raspberry_pi #tensorflow #tf_lite #video_surveillance

https://github.com/SharpAI/DeepCamera

#python #ai #automl #data_science #deep_learning #devops_tools #hacktoberfest #llm #llmops #machine_learning #metadata_tracking #ml #mlops #pipelines #production_ready #pytorch #tensorflow #workflow #zenml

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

#go #data_science #deep_learning #distributed_training #hyperparameter_optimization #hyperparameter_search #hyperparameter_tuning #kubernetes #machine_learning #ml_infrastructure #ml_platform #mlops #pytorch #tensorflow

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

#python #classification #coco #computer_vision #deep_learning #hacktoberfest #image_processing #instance_segmentation #low_code #machine_learning #metrics #object_detection #oriented_bounding_box #pascal_voc #python #pytorch #tensorflow #tracking #video_processing #yolo

Supervision is a powerful tool for building computer vision applications. It allows you to easily load datasets, draw detections on images or videos, and count detections in specific zones. You can use any classification, detection, or segmentation model with it, and it has connectors for popular libraries like Ultralytics and Transformers. Supervision also offers customizable annotators to visualize your data and utilities to manage datasets in various formats. By using Supervision, you can streamline your computer vision projects and make them more reliable and efficient. Additionally, there are extensive tutorials and documentation available to help you get started quickly.

https://github.com/roboflow/supervision

#python #bert #deep_learning #flax #hacktoberfest #jax #language_model #language_models #machine_learning #model_hub #natural_language_processing #nlp #nlp_library #pretrained_models #python #pytorch #pytorch_transformers #seq2seq #speech_recognition #tensorflow #transformer

The Hugging Face Transformers library provides thousands of pretrained models for various tasks like text, image, and audio processing. These models can be used for tasks such as text classification, image detection, speech recognition, and more. The library supports popular deep learning frameworks like JAX, PyTorch, and TensorFlow, making it easy to switch between them.

The benefit to the user is that you can quickly download and use these pretrained models with just a few lines of code, saving time and computational resources. You can also fine-tune these models on your own datasets and share them with the community. Additionally, the library offers a simple `pipeline` API for immediate use on different inputs, making it user-friendly for both researchers and practitioners. This helps in reducing compute costs and carbon footprint while enabling high-performance results across various machine learning tasks.

https://github.com/huggingface/transformers

#jupyter_notebook #computer_vision #deep_learning #drug_discovery #forecasting #large_language_models #mxnet #nlp #paddlepaddle #pytorch #recommender_systems #speech_recognition #speech_synthesis #tensorflow #tensorflow2 #translation

This repository provides top-quality deep learning examples that are easy to train and deploy on NVIDIA GPUs. It includes a wide range of models for computer vision, natural language processing, recommender systems, speech to text, and more. These examples are updated monthly and come in Docker containers with the latest NVIDIA software, ensuring the best performance. The models support multiple GPUs and nodes, and some are optimized for Tensor Cores, which can significantly speed up training. This makes it easier for users to achieve high accuracy and performance in their deep learning projects.

https://github.com/NVIDIA/DeepLearningExamples