Chaoran Wei d387e889ff | 1 year ago | |
---|---|---|
.. | ||
README.md | 1 year ago | |
vgg11_ascend.yaml | 1 year ago | |
vgg13_ascend.yaml | 1 year ago | |
vgg16_ascend.yaml | 1 year ago | |
vgg19_ascend.yaml | 1 year ago |
Very Deep Convolutional Networks for Large-Scale Image Recognition
Figure 1 shows the model architecture of VGGNet. VGGNet is a key milestone on image classification task. It expands the model to 16-19 layers for the first time. The key motivation of this model is
that it shows usage of 3x3 kernels is efficient and by adding 3x3 kernels, it could have the same effect as 5x5 or 7x7 kernels. VGGNet could achieve better model performance compared with previous
methods such as GoogleLeNet and AlexNet on ImageNet-1K dataset.[1]
Figure 1. Architecture of VGG [1]
Our reproduced model performance on ImageNet-1K is reported as follows.
Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
---|---|---|---|---|---|---|
vgg11 | D910x8-G | 72.00 | 90.50 | 132.86 | yaml | weights |
vgg13 | D910x8-G | 72.75 | 91.03 | 133.04 | yaml | weights |
vgg16 | D910x8-G | 74.53 | 92.05 | 138.35 | yaml | weights |
vgg19 | D910x8-G | 75.20 | 92.52 | 143.66 | yaml | weights |
Please refer to the installation instruction in MindCV.
Please download the ImageNet-1K dataset for model training and validation.
It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run
# distrubted training on multiple GPU/Ascend devices
mpirun -n 8 python train.py --config configs/vgg/vgg16_ascend.yaml --data_dir /path/to/imagenet
If the script is executed by the root user, the
--allow-run-as-root
parameter must be added tompirun
.
Similarly, you can train the model on multiple GPU devices with the above mpirun
command.
For detailed illustration of all hyper-parameters, please refer to config.py.
Note: As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size.
If you want to train or finetune the model on a smaller dataset without distributed training, please run:
# standalone training on a CPU/GPU/Ascend device
python train.py --config configs/vgg/vgg16_ascend.yaml --data_dir /path/to/dataset --distribute False
To validate the accuracy of the trained model, you can use validate.py
and parse the checkpoint path with --ckpt_path
.
python validate.py -c configs/vgg/vgg16_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
Please refer to the deployment tutorial in MindCV.
[1] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014.
No Description
Python Markdown Text other
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》