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README.md | 1 year ago |
This folder contains training recipes and model readme files for each model. The folder structure and naming rule of model configurations are as follows.
├── configs
├── model_a // model name in lower case with _ seperator
│ ├─ model_a_small_ascend.yaml // training recipe denated as {model_name}_{specification}_{hardware}.yaml
| ├─ model_a_large_gpu.yaml
│ ├─ README.md //readme file containing performance results and pretrained weight urls
│ └─ README_CN.md //readme file in Chinese
├── model_b
│ ├─ model_b_32_ascend.yaml
| ├─ model_l_16_ascend.yaml
│ ├─ README.md
│ └─ README_CN.md
├── README.md //this file
The model readme file in each sub-folder provides the introduction, reproduced results, and running guideline for each model.
Please follow the outline structure and table format shown in densenet/README.md when contributing your models :)
Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
---|---|---|---|---|---|---|
densenet_121 | D910x8-G | 75.64 | 92.84 | 8.06 | yaml | weights |
Illustration:
The checkpoint (i.e., model weight) name should follow this format: {model_name}_{specification}-{sha256sum}.ckpt, e.g., poolformer_s12-5be5c4e4.ckpt
.
You can run the following command and take the first 8 characters of the computing result as the sha256sum value in the checkpoint name.
sha256sum your_model.ckpt
For consistency, it is recommended to provide distributed training commands based on mpirun -n {num_devices} python train.py
, instead of using shell script such as distrubuted_train.sh
.
# standalone training on a gpu or ascend device
python train.py --config configs/densenet/densenet_121_gpu.yaml --data_dir /path/to/dataset --distribute False
# distributed training on gpu or ascend divices
mpirun -n 8 python train.py --config configs/densenet/densenet_121_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
.
Please use absolute path in the hyperlink or url for linking the target resource in the readme file and table.
MindCV是一个基于 MindSpore 开发的,致力于计算机视觉相关技术研发的开源工具箱。它提供大量的计算机视觉领域的经典模型和SoTA模型以及它们的预训练权重。同时,还提供了AutoAugment等SoTA算法来提高性能。通过解耦的模块设计,您可以轻松地将MindCV应用到您自己的CV任务中。
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For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》