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sy_liang2021@163.com 975ca25d0e | 1 year ago | |
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README.md | 1 year ago | |
README_CN.md | 1 year ago | |
res2net.png | 1 year ago | |
res2net_50-v1b_ascend.yaml | 1 year ago | |
res2net_50-v1b_gpu.yaml | 1 year ago | |
res2net_50_ascend.yaml | 1 year ago | |
res2net_50_gpu.yaml | 1 year ago | |
res2net_101-v1b_ascend.yaml | 1 year ago | |
res2net_101-v1b_gpu.yaml | 1 year ago | |
res2net_101_ascend.yaml | 1 year ago | |
res2net_101_gpu.yaml | 1 year ago |
We propose a novel building block for CNNs, namely Res2Net, 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. The proposed Res2Net block can be plugged into the state-of-the-art
backbone CNN models, e.g. , ResNet, ResNeXt, BigLittleNet, and DLA. We evaluate the Res2Net block on all these models
and demonstrate consistent performance gains over baseline models.
Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Train T. | Infer T. | Download | Config | Log |
---|---|---|---|---|---|---|---|---|---|
Res2Net50 | D910x8-G | 79.35 | 94.64 | 25.76 | 246s/epoch | 28.5ms/step | model | cfg | log |
Res2Net101 | D910x8-G | 79.56 | 94.70 | 45.33 | 467s/epoch | 46.0ms/step | model | cfg | log |
Res2Net50 | D910x8-G | 80.32 | 95.09 | 25.77 | 250s/epoch | 29.6ms/step | model | cfg | log |
Res2Net101-v1b | D910x8-G | 81.26 | 95.41 | 45.35 | 435s/epoch | 42.4ms/step | model | cfg | log |
The yaml config files that yield competitive results on ImageNet for different models are listed in
the configs
folder. To trigger training using preset yaml config.
mpirun -n 8 python train.py --config configs/res2net/res2net_50_gpu.yaml --data_dir /path/to/imagenet
Detailed adjustable parameters and their default value can be seen in config.py.
To validate the model, you can use validate.py
. Here is an example for res2net50 to verify the accuracy of your
training.
python validate.py --config configs/res2net/res2net_50_gpu.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/res2net50.ckpt
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