mirror of https://github.com/alibaba/EasyCV.git
111 lines
3.0 KiB
Markdown
111 lines
3.0 KiB
Markdown
# Quick Start
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## Linear Evaluation
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### Method 1: Extract features（recommend）
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This method is highly efficient.
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It first extracts data features with pretrained models, then performs linear eval on the features.
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This means there is no data augments and all feature layers are frozen.
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**Step1: Extract data features with pretrained model.**
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```shell
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sh benchmarks/tools/dist_extract.sh \
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${EXTRACT_DATASET_CONFIG} \
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${NUM_GPUS} \
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${FEATURE_DIR} \
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--checkpoint ${CHECKPOINT}
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```
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<details>
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<summary>Arguments</summary>
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- `EXTRACT_DATASET_CONFIG`:the config path of extract data features, refer to [extract_dataset_configs](benchmarks/extract_dataset_configs).
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- `NUM_GPUS`: number of gpus
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- `FEATURE_DIR`:your path to save output features
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- `CHECKPOINT`: the export checkpoint file of a selfsup model named as epoch\_\*\_export.pt.
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</details>
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**Examples:**
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Export model please reference to [ssl.md](../docs/source/tutorials/ssl.md), or you can test with our default export model [swav_restnet50](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/selfsup/swav_r50/epoch_200_export.pt -O ./pretrained_models/swav_rn50/epoch_200_export.pt).
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```shell
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sh benchmarks/tools/dist_extract.sh \
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benchmarks/extract_dataset_configs/imagenet.py \
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8 \
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./linear_eval/imagenet_features \
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--checkpoint ./pretrained_models/swav_rn50/epoch_200_export.pt
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```
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**Step2: Do linear evaluation with features.**
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```shell
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sh tools/dist_train.sh \
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${FEATURE_LINEAR_EVAL_CONFIG_PATH} \
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${NUM_GPUS} \
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--work_dir ${WORK_DIR}
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```
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<details>
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<summary>Arguments</summary>
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- `FEATURE_LINEAR_EVAL_CONFIG_PATH`:the config path of linear eval with features.
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Reference to `benchmarks/selfsup/classification/imagenet`,edit feature path to your local or oss path.
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- `NUM_GPUS`: number of gpus
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- `WORK_DIR`:your path to save models and logs
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</details>
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**Examples:**
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Edit `data_root` in `${FEATURE_LINEAR_EVAL_CONFIG_PATH}` to your own `${FEATURE_DIR}` path.
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```shell
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sh tools/dist_train.sh \
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benchmarks/selfsup/imagenet/swav_r50_feature.py \
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8 \
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--work_dir ./linear_eval
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```
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### Method 2: Finetune with fc layer
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```shell
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sh tools/dist_train.sh \
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${LINEAR_EVAL_CONFIG_PATH} \
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${NUM_GPUS} \
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--work_dir ${WORK_DIR}
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--load_from ${LOAD_FROM}
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```
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<details>
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<summary>Arguments</summary>
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- `LINEAR_EVAL_CONFIG_PATH`: the config path of linear eval
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- `NUM_GPUS`: number of gpus
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- `WORK_DIR`: your path to save models and logs
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- `LOAD_FROM`: the pretrained checkpoint file of a selfsup model named as epoch\_\*.pth.
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</details>
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**Examples:**
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Edit the `data_root` in the `${LINEAR_EVAL_CONFIG_PATH}` to your own data path.
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`${LOAD_FROM}` can use your own pretrained model, or you can test with our default pretrained model above.
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```shell
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sh tools/dist_train.sh \
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benchmarks/selfsup/classification/imagenet/resnet50_8xb32_100e_finetune.py \
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8 \
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--work_dir ./linear_eval
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--load_from ./pretrained_models/swav_rn50/epoch_200.pth
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```
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