Pinned Repositories
algorithm-cpp
algorithm-cpp projects
awesome-semantic-segmentation
:metal: awesome-semantic-segmentation
chineseocr_lite
超轻量级中文ocr,支持竖排文字识别, 支持ncnn、mnn、tnn推理 ( dbnet(1.8M) + crnn(2.5M) + anglenet(378KB)) 总模型仅4.7M
lite.ai.toolkit
🛠 A lite C++ toolkit of awesome AI models, support ONNXRuntime, MNN. Contains YOLOv5, YOLOv6, YOLOX, YOLOR, FaceDet, HeadSeg, HeadPose, Matting etc. Engine: ONNXRuntime, MNN.
MedivhXZ
Config files for my GitHub profile.
onnxruntime-inference-examples
Examples for using ONNX Runtime for machine learning inferencing.
segmentation_models.pytorch
Segmentation models with pretrained backbones. PyTorch.
sssegmentation
SSSegmentation: An Open Source Supervised Semantic Segmentation Toolbox Based on PyTorch.
TensorRT-Alpha
🔥🔥🔥TensorRT for YOLOv8、YOLOv8-Pose、YOLOv8-Seg、YOLOv8-Cls、YOLOv7、YOLOv6、YOLOv5、YOLONAS......🚀🚀🚀CUDA IS ALL YOU NEED.🍎🍎🍎
tensorRT_Pro
C++ library based on tensorrt integration
MedivhXZ's Repositories
MedivhXZ/algorithm-cpp
algorithm-cpp projects
MedivhXZ/awesome-semantic-segmentation
:metal: awesome-semantic-segmentation
MedivhXZ/chineseocr_lite
超轻量级中文ocr,支持竖排文字识别, 支持ncnn、mnn、tnn推理 ( dbnet(1.8M) + crnn(2.5M) + anglenet(378KB)) 总模型仅4.7M
MedivhXZ/lite.ai.toolkit
🛠 A lite C++ toolkit of awesome AI models, support ONNXRuntime, MNN. Contains YOLOv5, YOLOv6, YOLOX, YOLOR, FaceDet, HeadSeg, HeadPose, Matting etc. Engine: ONNXRuntime, MNN.
MedivhXZ/MedivhXZ
Config files for my GitHub profile.
MedivhXZ/onnxruntime-inference-examples
Examples for using ONNX Runtime for machine learning inferencing.
MedivhXZ/segmentation_models.pytorch
Segmentation models with pretrained backbones. PyTorch.
MedivhXZ/sssegmentation
SSSegmentation: An Open Source Supervised Semantic Segmentation Toolbox Based on PyTorch.
MedivhXZ/TensorRT-Alpha
🔥🔥🔥TensorRT for YOLOv8、YOLOv8-Pose、YOLOv8-Seg、YOLOv8-Cls、YOLOv7、YOLOv6、YOLOv5、YOLONAS......🚀🚀🚀CUDA IS ALL YOU NEED.🍎🍎🍎
MedivhXZ/tensorRT_Pro
C++ library based on tensorrt integration
MedivhXZ/YoloAll
YoloAll is a collection of yolo all versions. you you use YoloAll to test yolov3/yolov5/yolox/yolo_fastest
MedivhXZ/yolov7
Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors