#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