#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

#cplusplus #compiler #cuda #jax #machine_learning #mlir #pytorch #runtime #spirv #tensorflow #vulkan

IREE is a tool that helps run Machine Learning (ML) models on different devices, from big data centers to small mobile and edge devices. It uses a special way to convert ML models into a uniform format, making it easier to deploy them anywhere. This tool is still in the early stages but is being actively improved. Using IREE can help you scale your ML models efficiently across various platforms, making it beneficial for developers who need to deploy models in different environments.

https://github.com/iree-org/iree

#cplusplus #ai_framework #deep_learning #hardware_acceleration #machine_learning #neural_networks #onnx #pytorch #scikit_learn #tensorflow

ONNX Runtime is a tool that makes machine learning faster and cheaper. It works on many different devices and operating systems, like Windows, Linux, and Mac, and supports popular machine learning frameworks like PyTorch and TensorFlow. This means you can use it to speed up your machine learning models, making your applications run faster and more efficiently. It also helps in training models quickly, especially on powerful NVIDIA GPUs. This benefits you by providing faster customer experiences and lower costs for your machine learning projects.

https://github.com/microsoft/onnxruntime

#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

#cplusplus #deep_learning #deep_neural_networks #distributed #machine_learning #ml #neural_network #python #tensorflow

TensorFlow is a powerful tool for machine learning that helps you build and deploy AI applications easily. It was developed by Google and is now open source, meaning anyone can use and contribute to it. TensorFlow provides tools, libraries, and a strong community to support your work. You can install it using Python with a simple command like `pip install tensorflow`, and it supports various devices including GPUs. This makes it versatile for researchers and developers alike, allowing you to push the boundaries of machine learning and create innovative applications.

https://github.com/tensorflow/tensorflow

#typescript #ai #camera #google_coral #home_assistant #home_automation #homeautomation #mqtt #nvr #object_detection #realtime #rtsp #tensorflow

Frigate is a powerful tool for your home security cameras that uses AI to detect objects in real-time. It works well with Home Assistant and can use a Google Coral Accelerator to make it very fast. Frigate saves resources by only looking for objects when necessary and can record video based on what it detects. It also supports low-latency live viewing and can re-stream video to reduce connections. This helps you monitor your home efficiently and effectively, making it easier to keep your space secure.

https://github.com/blakeblackshear/frigate

#other #automl #chatgpt #data_analysis #data_science #data_visualization #data_visualizations #deep_learning #gpt #gpt_3 #jax #keras #machine_learning #ml #nlp #python #pytorch #scikit_learn #tensorflow #transformer

This is a comprehensive, regularly updated list of 920 top open-source Python machine learning libraries, organized into 34 categories like frameworks, data visualization, NLP, image processing, and more. Each project is ranked by quality using GitHub and package manager metrics, helping you find the best tools for your needs. Popular libraries like TensorFlow, PyTorch, scikit-learn, and Hugging Face transformers are included, along with specialized ones for time series, reinforcement learning, and model interpretability. This resource saves you time by guiding you to high-quality, actively maintained libraries for building, optimizing, and deploying machine learning models efficiently.

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