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CvPytorch

CvPytorch is an open source COMPUTER VISION toolbox based on PyTorch.

• Python 3.8
• PyTorch 1.6.0
• Torchvision 0.7.0
• tensorboardX 2.1

• (VGG) VGG: Very Deep Convolutional Networks for Large-Scale Image Recognition

• (ResNet) ResNet: Deep Residual Learning for Image Recognition

• (DenseNet) DenseNet: Densely Connected Convolutional Networks

• (ShuffleNet) ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

• (ShuffleNet V2) ShuffleNet V2: Practical Guidelines for Ecient CNN Architecture Design

• (SSD) SSD: Single Shot MultiBox Detector

• (Faster R-CNN) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

• (YOLOv3) YOLOv3: An Incremental Improvement

• (YOLOv5)

• (FPN) FPN: Feature Pyramid Networks for Object Detection

• (FCOS) FCOS: Fully Convolutional One-Stage Object Detection

• (FCN) Fully Convolutional Networks for Semantic Segmentation

• (Deeplab V3+) Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

• (PSPNet) Pyramid Scene Parsing Network

• (ENet) A Deep Neural Network Architecture for Real-Time Semantic Segmentation

• (U-Net) Convolutional Networks for Biomedical Image Segmentation

• (SegNet) A Deep ConvolutionalEncoder-Decoder Architecture for ImageSegmentation

• (Mask-RCNN) Mask-RCNN

• Pascal VOC
• Cityscapes
• ADE20K
• COCO
• PennPed
• CamVid

For this example, we will use COCO dataset with yolov5l.yaml . Feel free to use your own custom dataset and configurations.

$ python trainer.py --setting 'conf/hymenoptera.yml'

$ python -m torch.distributed.launch --nproc_per_node=2 trainer.py --setting 'conf/hymenoptera.yml'

• Train Custom Data

• Multi-GPU Training

• Mixed Precision Training

• Warm-Up

• Model Pruning/Sparsity

• Quantization

• TensorRT Deployment

• ONNX and TorchScript Export

• Class Activation Mapping (CAM)

• Test-Time Augmentation (TTA)

MIT License

Copyright (c) 2020 min liu