#python #object_detection

https://github.com/IDEACVR/DINO

#python #classification #computer_vision #object_detection #pytorch #self_supervised_learning #transformers #vision_transformer

https://github.com/alibaba/EasyCV

#python #detr #dino #object_detection #pytorch #state_of_the_art

https://github.com/IDEA-Research/detrex

#cplusplus #android #deep_learning #deployment #graphcore #intel #ios #jetson #kunlun #object_detection #onnxruntime #openvino #picodet #rockchip #sdk #serving #tensorrt #uie #yolov5

https://github.com/PaddlePaddle/FastDeploy

#jupyter_notebook #computer_vision #deep_learning #deep_neural_networks #image_classification #image_segmentation #object_detection #pytorch #tutorial #yolov5 #yolov6 #yolov7

https://github.com/roboflow-ai/notebooks

#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

#jupyter_notebook #computer_vision #deep_learning #image_classification #imagenet #neural_network #object_detection #pretrained_models #pretrained_weights #pytorch #semantic_segmentation #transfer_learning

https://github.com/Deci-AI/super-gradients

#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 #3d_perception #camera #lidar #object_detection #pytorch #semantic_segmentation #sensor_fusion

https://github.com/mit-han-lab/bevfusion

#cplusplus #ai #behaviour_analysis #cv #deep_learning #deepstream #face_recognition #feature_extraction #gstreamer #image_classification #image_enhancement #image_segmentation #license_plate_recognition #object_detection #opencv #reid #similarity_search #video_analysis #video_processing

https://github.com/sherlockchou86/VideoPipe

#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

#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 #ade20k #image_classification #imagenet #mask_rcnn #mscoco #object_detection #semantic_segmentation #swin_transformer

The Swin Transformer is a powerful tool for computer vision tasks like image classification, object detection, semantic segmentation, and video recognition. It uses a hierarchical structure with shifted windows to efficiently process images, making it more efficient than other models. Here are the key benefits Swin Transformer achieves state-of-the-art results in various tasks such as COCO object detection, ADE20K semantic segmentation, and ImageNet classification.
- **Efficiency** The model supports multiple tasks including image classification, object detection, instance segmentation, semantic segmentation, and video action recognition.
- **Improved Speed** The model is integrated into popular frameworks like Hugging Face Spaces and PaddleClas, making it easy to use and deploy.

Overall, the Swin Transformer offers high accuracy, efficiency, and versatility, making it a valuable tool for various computer vision applications.

https://github.com/microsoft/Swin-Transformer

#python #autogluon #automated_machine_learning #automl #computer_vision #data_science #deep_learning #ensemble_learning #forecasting #gluon #hyperparameter_optimization #machine_learning #natural_language_processing #object_detection #python #pytorch #scikit_learn #structured_data #tabular_data #time_series #transfer_learning

AutoGluon makes machine learning easy and fast. With just a few lines of code, you can train and use high-accuracy models for images, text, time series, and tabular data. This means you can quickly build and deploy powerful machine learning models without needing to write a lot of code. It supports Python 3.8 to 3.11 and works on Linux, MacOS, and Windows, making it convenient for various users. This saves time and effort, allowing you to focus on other parts of your project.

https://github.com/autogluon/autogluon

#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

#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

#python #d_fine #detr #object_detection

D-FINE is a fast and accurate real-time object detection model that improves how bounding boxes are predicted by refining detailed probability distributions for each box edge, making localization more precise. It uses two main techniques: Fine-grained Distribution Refinement (FDR), which iteratively improves box predictions by focusing on uncertainties, and Global Optimal Localization Self-Distillation (GO-LSD), which helps earlier layers learn from later, more accurate predictions. This approach boosts detection accuracy without extra training or inference costs, making it efficient and effective for detecting objects even in complex scenes. You benefit by getting better, faster object detection with less computational effort.

https://github.com/Peterande/D-FINE