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README.md | 9 months ago | |
swinv2_tiny_window8_ascend.yaml | 3 months ago |
This paper aims to explore large-scale models in computer vision. The authors tackle three major issues in training and
application of large vision models, including training instability, resolution gaps between pre-training and
fine-tuning, and hunger on labelled data. Three main techniques are proposed: 1) a residual-post-norm method combined
with cosine attention to improve training stability; 2) A log-spaced continuous position bias method to effectively
transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) A
self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images. This model set new performance
records on 4 representative vision tasks, including ImageNet-V2 image classification, COCO object detection, ADE20K
semantic segmentation, and Kinetics-400 video action classification.[1]
Figure 1. Architecture of Swin Transformer V2 [1]
Our reproduced model performance on ImageNet-1K is reported as follows.
Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
---|---|---|---|---|---|---|
swinv2_tiny_window8 | D910x8-G | 81.42 | 95.43 | 28.78 | 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
# distributed training on multiple GPU/Ascend devices
mpirun -n 8 python train.py --config configs/swintransformerv2/swinv2_tiny_window8_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/swintransformerv2/swinv2_tiny_window8_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/swintransformerv2/swinv2_tiny_window8_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
Please refer to the deployment tutorial in MindCV.
[1] Liu Z, Hu H, Lin Y, et al. Swin transformer v2: Scaling up capacity and resolution[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 12009-12019.
A toolbox of vision models and algorithms based on MindSpore
https://github.com/mindspore-lab/mindcv
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