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 | 71.86 | 90.50 | 132.86 | yaml | weights |
| vgg13 | D910x8-G | 72.87 | 91.02 | 133.04 | yaml | weights |
| vgg16 | D910x8-G | 74.61 | 91.87 | 138.35 | yaml | weights |
| vgg19 | D910x8-G | 75.21 | 92.56 | 143.66 | yaml | weights |
- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode.
- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K.
Please refer to the installation instruction in MindCV.
Please download the ImageNet-1K dataset for model training and validation.
- Distributed Training
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/imagenetIf the script is executed by the root user, the
--allow-run-as-rootparameter 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.
- Standalone Training
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 FalseTo 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.