Beast code in Giters

YOLOv5 Series Multi-backbone(TPH-YOLOv5, Ghostnet, ShuffleNetv2, Mobilenetv3Small, EfficientNetLite, PP-LCNet, SwinTransformer YOLO), Module(CBAM, DCN), Pruning (EagleEye, Network Slimming), Quantization (MQBench) and Deployment (TensorRT, ncnn) Compression Tool Box.

Language:Python97800

United-Perception

United Perception

Language:PythonApache-2.042600

mnn-yolov5

Imported from https://gitee.com/techshoww/mnn-yolov5.

Language:C++2600

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.

Language:C++GPL-3.0350500

mqbench-paper

Language:PythonApache-2.04400

simple-int8-pytorch-implement

int8 calibration implement and inference (help you understand int8 Q)

Language:Python1600

segmentation-learning-experiment-pytorch

基于Pytorch实现优秀的自然图像分割框架！(包括FCN、U-Net和Deeplab)

Language:Python5700

model-quantization

Collections of model quantization algorithms. Any issues, please contact Peng Chen (blueardour@gmail.com)

NOASSERTION4200

micronet

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

Language:PythonMIT220100

MNN-APPLICATIONS

MNN applications by MNN, JNI exec, RK3399. Support tflite\tensorflow\caffe\onnx models.

Language:C++49600

YOLOX

YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

Language:PythonApache-2.0915200