SENet
Squeeze-and-Excitation Networks
Introduction
In this work, the authors focus instead on the channel relationship and propose a novel architectural unit, which the
authors term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by
explicitly modelling interdependencies between channels. The results show that these blocks can be stacked together to
form SENet architectures that generalise extremely effectively across different datasets. The authors further
demonstrate that SE blocks bring significant improvements in performance for existing state-of-the-art CNNs at slight
additional computational cost.[1]
Figure 1. Architecture of SENet [1]
Results
Our reproduced model performance on ImageNet-1K is reported as follows.
Model |
Context |
Top-1 (%) |
Top-5 (%) |
Params (M) |
Recipe |
Download |
SEResNet18 |
D910x8-G |
71.81 |
90.49 |
11.80 |
yaml |
weights |
SEResNet34 |
D910x8-G |
75.38 |
92.50 |
21.98 |
yaml |
weights |
SEResNet50 |
D910x8-G |
78.32 |
94.07 |
28.14 |
yaml |
weights |
SEResNeXt26_32x4d |
D910x8-G |
77.17 |
93.42 |
16.83 |
yaml |
weights |
SEResNeXt50_32x4d |
D910x8-G |
78.71 |
94.36 |
27.63 |
yaml |
weights |
Notes
- 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.
Quick Start
Preparation
Installation
Please refer to the installation instruction in MindCV.
Dataset Preparation
Please download the ImageNet-1K dataset for model training and validation.
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
# distributed training on multiple GPU/Ascend devices
mpirun -n 8 python train.py --config configs/senet/seresnet50_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 to mpirun
.
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/senet/seresnet50_ascend.yaml --data_dir /path/to/dataset --distribute False
Validation
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/senet/seresnet50_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
Deployment
Please refer to the deployment tutorial in MindCV.
References
[1] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 7132-7141.