82 lines
2.1 KiB
Markdown
82 lines
2.1 KiB
Markdown
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# 多标签分类quick start
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基于[NUS-WIDE-SCENE](https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html)数据集，体验多标签分类的训练、评估、预测的过程，该数据集是NUS-WIDE数据集的一个子集。请事先参考[安装指南](install.md)配置运行环境和克隆PaddleClas代码。
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## 一、数据和模型准备
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* 进入PaddleClas目录。
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```
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cd path_to_PaddleClas
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```
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* 创建并进入`dataset/NUS-WIDE-SCENE`目录，下载并解压NUS-WIDE-SCENE数据集。
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```shell
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mkdir dataset/NUS-WIDE-SCENE
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cd dataset/NUS-WIDE-SCENE
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wget https://paddle-imagenet-models-name.bj.bcebos.com/data/NUS-SCENE-dataset.tar
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tar -xf NUS-SCENE-dataset.tar
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```
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* 返回`PaddleClas`根目录
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```
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cd ../../
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```
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## 二、环境准备
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### 2.1 下载预训练模型
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本例展示基于ResNet50_vd模型的多标签分类流程，因此首先下载ResNet50_vd的预训练模型
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```bash
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mkdir pretrained
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cd pretrained
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wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
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cd ../
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```
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## 三、模型训练
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```shell
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export CUDA_VISIBLE_DEVICES=0
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python -m paddle.distributed.launch \
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--gpus="0" \
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tools/train.py \
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-c ./configs/quick_start/ResNet50_vd_multilabel.yaml
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```
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训练10epoch之后，验证集最好的正确率应该在0.72左右。
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## 四、模型评估
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```bash
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python tools/eval.py \
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-c ./configs/quick_start/ResNet50_vd_multilabel.yaml \
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-o pretrained_model="./output/ResNet50_vd/best_model/ppcls" \
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-o load_static_weights=False
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```
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评估指标采用mAP，验证集的mAP应该在0.57左右。
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## 五、模型预测
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```bash
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python tools/infer/infer.py \
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-i "./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/images/0199_434752251.jpg" \
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--model ResNet50_vd \
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--pretrained_model "./output/ResNet50_vd/best_model/ppcls" \
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--use_gpu True \
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--load_static_weights False \
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--multilabel True \
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--class_num 33
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```
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得到类似下面的输出：
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```
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class id: 3, probability: 0.6025
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class id: 23, probability: 0.5491
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class id: 32, probability: 0.7006
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```
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