#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

#python #coreml #deep_learning #ios #machine_learning #ml #object_detection #onnx #pytorch #tflite #ultralytics #yolo #yolov3 #yolov5

YOLOv5 is a powerful and easy-to-use AI model for object detection, image segmentation, and classification. It is designed to be fast, accurate, and simple to implement. Here are the key benefits YOLOv5 is straightforward to set up and use, with detailed documentation and tutorials available.
- **Performance** You can use YOLOv5 for object detection, image segmentation, and classification tasks.
- **Community Support** You can run YOLOv5 in various environments such as Google Colab, Paperspace, Kaggle, and Docker.

Overall, YOLOv5 simplifies the process of integrating advanced AI capabilities into your projects.

https://github.com/ultralytics/yolov5

#python #botsort #bytetrack #deep_learning #deepocsort #improvedassociation #mot #mots #multi_object_tracking #multi_object_tracking_segmentation #ocsort #osnet #segmentation #strongsort #tensorrt #tracking_by_detection #yolo

BoxMOT is a tool that helps track multiple objects in videos or images using advanced models. It offers various tracking methods that work well on different types of hardware, from CPUs to powerful GPUs. This means you can use it even if your computer is not very powerful. BoxMOT also saves time by allowing you to reuse pre-generated data, so you don't have to repeat calculations every time. You can easily install and use it with popular object detection models like YOLOv8, YOLOv9, and YOLOv10, and it supports tracking different types of data such as bounding boxes, segmentation masks, and pose estimations. This makes it very flexible and useful for various tasks involving object tracking.

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

#cplusplus #caffe #convolution #deep_learning #deep_neural_networks #diy #graph_algorithms #inference #inference_engine #maxpooling #ncnn #pnnx #pytorch #relu #resnet #sigmoid #yolo #yolov5

This course, "_动手自制大模型推理框架_" (Handcrafting Large Model Inference Framework), is a valuable resource for those interested in deep learning and model inference. It teaches you how to build a modern C++ project from scratch, focusing on designing and implementing a deep learning inference framework. The course supports latest models like LLama3.2 and Qwen2.5, and uses CUDA acceleration and Int8 quantization for better performance.

By taking this course, you will learn how to write efficient C++ code, manage projects with CMake and Git, design computational graphs, implement common operators like convolution and pooling, and optimize them for speed. This knowledge will be highly beneficial for job interviews and advancing your skills in deep learning. The course also includes practical demos on models like Unet and YoloV5, making it a hands-on learning experience.

https://github.com/zjhellofss/KuiperInfer