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README.md | 1 year ago | |
resnet_18_ascend.yaml | 1 year ago | |
resnet_18_gpu.yaml | 1 year ago | |
resnet_34_ascend.yaml | 1 year ago | |
resnet_34_gpu.yaml | 1 year ago | |
resnet_50_ascend.yaml | 1 year ago | |
resnet_50_gpu.yaml | 1 year ago | |
resnet_101_ascend.yaml | 1 year ago | |
resnet_101_gpu.yaml | 1 year ago | |
resnet_152_ascend.yaml | 1 year ago | |
resnet_152_gpu.yaml | 1 year ago |
Resnet is a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which is explicitly reformulated that the layers are learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. Lots of comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
Figure 1. Architecture of ResNet [1]
Our reproduced model performance on ImageNet-1K is reported as follows.
Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
---|---|---|---|---|---|---|
ResNet18 | D910x8-G | 70.21 | 89.62 | 11.70 | yaml | weights |
ResNet34 | D910x8-G | 74.15 | 91.98 | 21.81 | yaml | weights |
ResNet50 | D910x8-G | 76.69 | 93.50 | 25.61 | yaml | weights |
ResNet101 | D910x8-G | 78.24 | 94.09 | 44.65 | yaml | weights |
ResNet152 | D910x8-G | 78.72 | 94.45 | 60.34 | 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/resnet/resnet_18_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/resnet/resnet_18_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/resnet/resnet_18_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] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.
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