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XixinYang 97143b8e55 | 1 year ago | |
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README.md | 1 year ago | |
res2net_50_ascend.yaml | 1 year ago | |
res2net_50_gpu.yaml | 1 year ago | |
res2net_50_v1b_ascend.yaml | 1 year ago | |
res2net_50_v1b_gpu.yaml | 1 year ago | |
res2net_101_ascend.yaml | 1 year ago | |
res2net_101_gpu.yaml | 1 year ago | |
res2net_101_v1b_ascend.yaml | 1 year ago | |
res2net_101_v1b_gpu.yaml | 1 year ago |
Res2Net is a novel building block for CNNs proposed by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. Ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, verify the superiority of the Res2Net over the state-of-the-art baseline methods such as ResNet-50, DLA-60 and etc.
Figure 1. Architecture of Res2Net [1]
Our reproduced model performance on ImageNet-1K is reported as follows.
Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
---|---|---|---|---|---|---|
Res2Net50 | D910x8-G | 79.35 | 94.64 | 25.76 | yaml | weights |
Res2Net101 | D910x8-G | 79.56 | 94.70 | 45.33 | yaml | weights |
Res2Net50-v1b | D910x8-G | 80.32 | 95.09 | 25.77 | yaml | weights |
Res2Net101-v1b | D910x8-G | 81.26 | 95.41 | 45.35 | 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/res2net/res2net_50_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/res2net/res2net_50_ascend.yaml --data_dir /path/to/imagenet --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/res2net/res2net_50_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] Gao S H, Cheng M M, Zhao K, et al. Res2net: A new multi-scale backbone architecture[J]. IEEE transactions on pattern analysis and machine intelligence, 2019, 43(2): 652-662.
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