PyTorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision

@misc{CV2018,
  author =       {Donny You},
  howpublished = {\url{https://github.com/CVBox/PyTorchCV}},
  year =         {2018}
}

This repository provides source code for some deep learning based cv problems. We'll do our best to keep this repository up to date. If you do find a problem about this repository, please raise it as an issue. We will fix it immediately.

Implemented Papers

Image Classification
- VGG: Very Deep Convolutional Networks for Large-Scale Image Recognition
- ResNet: Deep Residual Learning for Image Recognition
- DenseNet: Densely Connected Convolutional Networks
- MobileNetV2: Inverted Residuals and Linear Bottlenecks
- ResNeXt: Aggregated Residual Transformations for Deep Neural Networks
- SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
- ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
- ShuffleNet V2: Practical Guidelines for Ecient CNN Architecture Design
Pose Estimation
- CPM: Convolutional Pose Machines
- OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Object Detection
- SSD: Single Shot MultiBox Detector
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- YOLOv3: An Incremental Improvement
- FPN: Feature Pyramid Networks for Object Detection
Semantic Segmentation
- DeepLabV3: Rethinking Atrous Convolution for Semantic Image Segmentation
- PSPNet: Pyramid Scene Parsing Network
- DenseASPP: DenseASPP for Semantic Segmentation in Street Scenes
Instance Segmentation
- Mask R-CNN

Performances with PyTorchCV

Object Detection

SSD: Single Shot MultiBox Detector [SSD300, SSD512]

Model	Training data	Testing data	mAP	FPS
SSD-300 Origin	VOC07+12 trainval	VOC07 test	0.772	-
SSD-300 Ours	VOC07+12 trainval	VOC07 test	0.786	-
SSD-512 Origin	VOC07+12 trainval	VOC07 test	0.798	-
SSD-512 Ours	VOC07+12 trainval	VOC07 test	0.807	-

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks [Faster R-CNN]

Model	Training data	Testing data	mAP	FPS
Faster R-CNN Origin	VOC07 trainval	VOC07 test	0.699	-
Faster R-CNN Ours	VOC07 trainval	VOC07 test	0.706	-

YOLOv3: An Incremental Improvement

Commands with PyTorchCV

Take OpenPose as an example.

Train the openpose model

python main.py  --hypes hypes/pose/coco/op_coco_pose.json \
                --base_lr 0.001 \
                --phase train \
                --gpu 0 1

Finetune the openpose model

python main.py  --hypes hypes/pose/coco/op_coco_pose.json \
                --base_lr 0.001 \
                --phase train \
                --resume checkpoints/pose/coco/coco_open_pose_65000.pth \
                --gpu 0 1

Test the openpose model(test_img):

python main.py  --hypes hypes/pose/coco/op_coco_pose.json \
                --phase test \
                --resume checkpoints/pose/coco/coco_open_pose_65000.pth \
                --test_img val/samples/ski.jpg \
                --gpu 0

Test the openpose model(test_dir):

python main.py  --hypes hypes/pose/coco/op_coco_pose.json \
                --phase test \
                --resume checkpoints/pose/coco/coco_open_pose_65000.pth \
                --test_dir val/samples \
                --gpu 0

Examples with PyTorchCV