CvPytorch
CvPytorch is an open source COMPUTER VISION toolbox based on PyTorch.
- [2021.07.23] Release nanodet-repvgg models with RepVGG backbone on COCO (27.16mAP).
- [2021.07.23] Release nanodet-g models with cspnet backbone on COCO (23.54mAP).
- [2021.07.23] Release nanodet-efficientnet_lite models with efficientnet_lite backbone on COCO (25.65mAP).
- [2021.07.22] Release nanodet-t models with Transformer neck on COCO (21.97mAP).
- [2021.07.20] Release nanodet-416 models with shufflenetv2 backbone on COCO (23.30mAP).
- [2021.07.07] Release STDC models with stdc2 backbone on Cityscapes (73.36mIoU).
- [2021.07.06] Release STDC models with stdc1 backbone on Cityscapes (72.89mIoU).
- [2021.07.05] Release nanodet-320 models with shufflenetv2 backbone on COCO (20.54mAP).
- [2021.07.01] Release deeplabv3plus models with resnet50 backbone on Cityscapes (72.96mIoU).
- [2021.06.28] Release Unet models on Cityscapes (56.90mIoU).
- [2021.06.20] Release PSPNet models with resnet50 backbone on Cityscapes (72.59mIoU).
- [2021.06.15] Release deeplabv3 models with mobilenet_v2, resnet50 and resnet101 backbone on Cityscapes (68.06, 71.53 and 72.83mIoU).
- Python 3.8
- PyTorch 1.6.0
- Torchvision 0.7.0
- tensorboardX 2.1
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(VGG) VGG: Very Deep Convolutional Networks for Large-Scale Image Recognition
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(ResNet) ResNet: Deep Residual Learning for Image Recognition
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(DenseNet) DenseNet: Densely Connected Convolutional Networks
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(ShuffleNet) ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
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(ShuffleNet V2) ShuffleNet V2: Practical Guidelines for Ecient CNN Architecture Design
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(SSD) SSD: Single Shot MultiBox Detector
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(Faster R-CNN) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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(YOLOv3) YOLOv3: An Incremental Improvement
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(YOLOv5)
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(FPN) FPN: Feature Pyramid Networks for Object Detection
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(FCOS) FCOS: Fully Convolutional One-Stage Object Detection
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(FCN) Fully Convolutional Networks for Semantic Segmentation
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(Deeplab V3+) Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
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(PSPNet) Pyramid Scene Parsing Network
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(ENet) A Deep Neural Network Architecture for Real-Time Semantic Segmentation
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(U-Net) Convolutional Networks for Biomedical Image Segmentation
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(SegNet) A Deep ConvolutionalEncoder-Decoder Architecture for ImageSegmentation
- (Mask-RCNN) Mask-RCNN
For this example, we will use hymenoptera dataset with conf/hymenoptera.yml
. 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'
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Train Custom Data
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Multi-GPU Training
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Mixed Precision Training
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Warm-Up
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Model Pruning/Sparsity
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Quantization
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TensorRT Deployment
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ONNX and TorchScript Export
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Class Activation Mapping (CAM)
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Test-Time Augmentation (TTA)
MIT License
Copyright (c) 2020 min liu