ML_GCN_目标检测_标签检测_基于多标签的目标检测_planning7ai_GCN_

# ML_GCN.pytorch PyTorch implementation of [Multi-Label Image Recognition with Graph Convolutional Networks](https://arxiv.org/abs/1904.03582), CVPR 2019. ### Requirements Please, install the following packages - numpy - torch-0.3.1 - torchnet - torchvision-0.2.0 - tqdm ### Download pretrain models checkpoint/coco ([GoogleDrive](https://drive.google.com/open?id=1ivLi1Rc-dCUmN1ProcMk76zxF1DSvlIk)) checkpoint/voc ([GoogleDrive](https://drive.google.com/open?id=1lhbmW5g-Mo9KgI07nmc1kwSbEnb6t-YA)) or [Baidu](https://pan.baidu.com/s/17j3lTjMRmXvWHT86zhaaVA) ### Options - `lr`: learning rate - `lrp`: factor for learning rate of pretrained layers. The learning rate of the pretrained layers is `lr * lrp` - `batch-size`: number of images per batch - `image-size`: size of the image - `epochs`: number of training epochs - `evaluate`: evaluate model on validation set - `resume`: path to checkpoint ### Demo VOC 2007 ```sh python3 demo_voc2007_gcn.py data/voc --image-size 448 --batch-size 32 -e --resume checkpoint/voc/voc_checkpoint.pth.tar ``` ### Demo COCO 2014 ```sh python3 demo_coco_gcn.py data/coco --image-size 448 --batch-size 32 -e --resume checkpoint/coco/coco_checkpoint.pth.tar ``` ## Citing this repository If you find this code useful in your research, please consider citing us: ``` @inproceedings{ML_GCN_CVPR_2019, author = {Zhao-Min, Chen and Xiu-Shen, Wei and Peng, Wang and Yanwen, Guo}, title = {{Multi-Label Image Recognition with Graph Convolutional Networks}}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2019} } ``` ## Reference This project is based on https://github.com/durandtibo/wildcat.pytorch ## Tips If you have any questions about our work, please do not hesitate to contact us by emails.