#python #bounding_boxes #computer_vision #data_augmentation #data_visualization #deep_learning #deep_neural_networks #image_processing #k_means #mean_average_precision #numpy #object_detection #pandas #performance_visualization #pretrained_weights #python3 #random_weights #tensorflow2 #train #yolo #yolov3

https://github.com/emadboctorx/yolov3-keras-tf2

#javascript #annotation #annotation_tool #coco #computer_vision #create #image #labelling #machine_learning #ml #model #object_detection #tensorflow #training_data #vgg #vision #yolo

https://github.com/OvidijusParsiunas/myvision

#python #pytorch #yolo #implicit #representation #darknet #explicit #yolov4 #scaled_yolov4 #yolov4_csp #yolor #unified_network

https://github.com/WongKinYiu/yolor

#python #real_time #video #pytorch #computer_camera #rtsp_stream #web_camera #you_only_look_once #pedestrian_tracking #deep_sort #http_stream #multple_object_tracking #deep_association_metric #yolov5 #simple_online_and_realtime_tracking #pytorch_yolov5 #yolo_v5

https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch

#python #detection #detextron2 #detr #face #instance_segmentation #object_detection #onnx #tensorrt #transformers #yolo #yolov6 #yolov7 #yolox

https://github.com/jinfagang/yolov7

#python #object_detection #pytorch #yolo

https://github.com/meituan/YOLOv6

#python #object_detection #pytorch #rtmdet #yolo #yolov5 #yolov6 #yolox

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

#python #anchor_free #computer_vision #deep_learning #edge_computing #object_detection #object_detector #onnx #pytorch #tensorrt #yolo

https://github.com/LSH9832/edgeyolo

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

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

#python #deep_learning #hub #image_classification #instance_segmentation #machine_learning #obb #object_detection #pose #pytorch #tracking #ultralytics #yolo #yolo_world #yolo_world_v2 #yolo11 #yolov10 #yolov8 #yolov9

Ultralytics YOLO11 is a state-of-the-art model for object detection, segmentation, classification, and pose estimation. It is fast, accurate, and easy to use, making it suitable for various tasks. You can install it using pip (`pip install ultralytics`) and use it via the command line or Python scripts. The model comes with extensive documentation and community support through Discord, Reddit, and forums. Additionally, Ultralytics offers integrations with other AI platforms like Roboflow and ClearML to enhance your workflow. This tool benefits users by providing high-performance AI capabilities with minimal setup and robust community resources for assistance.

https://github.com/ultralytics/ultralytics

#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

#javascript #annotation #annotation_tool #annotations #boundingbox #computer_vision #data_labeling #dataset #datasets #deep_learning #image_annotation #image_classification #image_labeling #image_labelling_tool #label_studio #labeling #labeling_tool #mlops #semantic_segmentation #text_annotation #yolo

Label Studio is a free, open-source tool that helps you label different types of data like images, audio, text, videos, and more. It has a simple and user-friendly interface that makes it easy to prepare or improve your data for machine learning models. You can customize it to fit your needs and export labeled data in various formats. It supports multi-user labeling, multiple projects, and integration with machine learning models for pre-labeling and active learning. You can install it locally using Docker, pip, or other methods, or deploy it in cloud services like Heroku or Google Cloud Platform. This tool streamlines your data labeling process and helps you create more accurate ML models.

https://github.com/HumanSignal/label-studio