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muxiaojue 01604c5c76 | 1 year ago | |
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README.md | 1 year ago | |
rexnet_x09.yaml | 1 year ago | |
rexnet_x10.yaml | 1 year ago | |
rexnet_x13.yaml | 1 year ago | |
rexnet_x15.yaml | 1 year ago | |
rexnet_x20.yaml | 1 year ago |
ReXNet: Rethinking Channel Dimensions for Efficient Model Design
ReXNets is a new model achieved based on parameterization. It utilizes a new search method for a channel configuration via piece-wise linear functions of block index. The search space contains the conventions, and an effective channel configuration that can be parameterized by a linear function of the block index is used. ReXNets outperforms the recent lightweight models including NAS-based models and further showed remarkable fine-tuning performances on COCO object detection, instance segmentation, and fine-grained classifications.
Our reproduced model performance on ImageNet-1K is reported as follows.
Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
---|---|---|---|---|---|---|
rexnet_x09 | D910x8-G | 77.07 | 93.41 | 4.13 | yaml | weights |
rexnet_x10 | D910x8-G | 77.38 | 93.60 | 4.84 | yaml | weights |
rexnet_x13 | D910x8-G | 79.06 | 94.28 | 7.61 | yaml | weights |
rexnet_x15 | D910x8-G | 79.94 | 94.74 | 9.79 | yaml | weights |
rexnet_x20 | D910x8-G | 80.6 | 94.99 | 16.45 | 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/vit/vit_b32_224_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/vit/vit_b32_224_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/vit/vit_b32_224_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
To deploy online inference services with the trained model efficiently, please refer to the deployment tutorial.
[1] Han D, Yun S, Heo B, et al. Rethinking channel dimensions for efficient model design[C]//Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition. 2021: 732-741.
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