add document for multilabel

README.md

@@ -82,6 +82,7 @@ PaddleClas is a toolset for image classification tasks prepared for the industry
 - Advanced tutorials
     - [Knowledge distillation](./docs/en/advanced_tutorials/distillation/distillation_en.md)
     - [Data augmentation](./docs/en/advanced_tutorials/image_augmentation/ImageAugment_en.md)
+    - [Multilabel classification](./docs/en/advanced_tutorials/multilabel/multilabel_en.md)
 - Applications
     - [Transfer learning](./docs/en/application/transfer_learning_en.md)
     - [Pretrained model with 100,000 categories](./docs/en/application/transfer_learning_en.md)

README_cn.md

@@ -83,6 +83,7 @@
 - 高阶使用
     - [知识蒸馏](./docs/zh_CN/advanced_tutorials/distillation/distillation.md)
     - [数据增广](./docs/zh_CN/advanced_tutorials/image_augmentation/ImageAugment.md)
+    - [多标签分类](./docs/zh_CN/advanced_tutorials/multilabel/multilabel.md)
 - 特色拓展应用
     - [迁移学习](./docs/zh_CN/application/transfer_learning.md)
     - [10万类图像分类预训练模型](./docs/zh_CN/application/transfer_learning.md)

docs/en/advanced_tutorials/index.rst

@@ -6,4 +6,4 @@ advanced_tutorials
 
    image_augmentation/index
    distillation/index
-
+   multilabel/index

docs/en/advanced_tutorials/multilabel/index.rst

+Multilabel Classification
+================================
+
+.. toctree::
+   :maxdepth: 3
+
+   multilabel.md
\ No newline at end of file

docs/en/advanced_tutorials/multilabel/multilabel_en.md

+# Multilabel classification quick start
+
+Based on the [NUS-WIDE-SCENE](https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html) dataset which is a subset of NUS-WIDE dataset, you can experience multilabel of PaddleClas, include training, evaluation and prediction. Please refer to [Installation](install.md) to install at first.
+
+## Preparation
+
+* Enter PaddleClas directory
+
+```
+cd path_to_PaddleClas
+```
+
+* Create and enter `dataset/NUS-WIDE-SCENE` directory, download and decompress NUS-WIDE-SCENE dataset
+
+```shell
+mkdir dataset/NUS-WIDE-SCENE
+cd dataset/NUS-WIDE-SCENE
+wget https://paddle-imagenet-models-name.bj.bcebos.com/data/NUS-SCENE-dataset.tar
+tar -xf NUS-SCENE-dataset.tar
+```
+
+* Return `PaddleClas` root home
+
+```
+cd ../../
+```
+
+## Environment
+
+### Download pretrained model
+
+You can use the following commands to download the pretrained model of ResNet50_vd.
+
+```bash
+mkdir pretrained
+cd pretrained
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
+cd ../
+```
+
+## Training
+
+```shell
+export CUDA_VISIBLE_DEVICES=0
+python -m paddle.distributed.launch \
+    --gpus="0" \
+    tools/train.py \
+        -c ./configs/quick_start/ResNet50_vd_multilabel.yaml
+```
+
+After training for 10 epochs, the best accuracy over the validation set should be around 0.72.
+
+## Evaluation
+
+```bash
+python tools/eval.py \
+    -c ./configs/quick_start/ResNet50_vd_multilabel.yaml \
+    -o pretrained_model="./output/ResNet50_vd/best_model/ppcls" \
+    -o load_static_weights=False
+```
+
+The metric of evaluation is based on mAP, which is commonly used in multilabel task to show model perfermance. The mAP over validation set should be around 0.57.
+
+## Prediction
+
+```bash
+python tools/infer/infer.py \
+    -i "./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/images/0199_434752251.jpg" \
+    --model ResNet50_vd \
+    --pretrained_model "./output/ResNet50_vd/best_model/ppcls" \
+    --use_gpu True \
+    --load_static_weights False \
+    --multilabel True \
+    --class_num 33
+```
+
+You will get multiple output such as the following:
+```    
+    class id: 3, probability: 0.6025
+    class id: 23, probability: 0.5491
+    class id: 32, probability: 0.7006
+```
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docs/zh_CN/advanced_tutorials/index.rst

@@ -6,4 +6,4 @@
 
    image_augmentation/index
    distillation/index
-
+   multilabel/index

docs/zh_CN/advanced_tutorials/multilabel/index.rst

+多标签分类
+================================
+
+.. toctree::
+   :maxdepth: 3
+
+   multilabel.md
\ No newline at end of file

docs/zh_CN/advanced_tutorials/multilabel/multilabel.md

+# 多标签分类quick start
+
+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。
+
+## 一、数据和模型准备
+
+* 进入PaddleClas目录。
+
+```
+cd path_to_PaddleClas
+```
+
+* 创建并进入`dataset/NUS-WIDE-SCENE`目录，下载并解压NUS-WIDE-SCENE数据集。
+
+```shell
+mkdir dataset/NUS-WIDE-SCENE
+cd dataset/NUS-WIDE-SCENE
+wget https://paddle-imagenet-models-name.bj.bcebos.com/data/NUS-SCENE-dataset.tar
+tar -xf NUS-SCENE-dataset.tar
+```
+
+* 返回`PaddleClas`根目录
+
+```
+cd ../../
+```
+
+## 二、环境准备
+
+### 2.1 下载预训练模型
+
+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
+
+```bash
+mkdir pretrained
+cd pretrained
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
+cd ../
+```
+
+## 三、模型训练
+
+```shell
+export CUDA_VISIBLE_DEVICES=0
+python -m paddle.distributed.launch \
+    --gpus="0" \
+    tools/train.py \
+        -c ./configs/quick_start/ResNet50_vd_multilabel.yaml
+```
+
+训练10epoch之后，验证集最好的正确率应该在0.72左右。
+
+## 四、模型评估
+
+```bash
+python tools/eval.py \
+    -c ./configs/quick_start/ResNet50_vd_multilabel.yaml \
+    -o pretrained_model="./output/ResNet50_vd/best_model/ppcls" \
+    -o load_static_weights=False
+```
+
+评估指标采用mAP，验证集的mAP应该在0.57左右。
+
+## 五、模型预测
+
+```bash
+python tools/infer/infer.py \
+    -i "./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/images/0199_434752251.jpg" \
+    --model ResNet50_vd \
+    --pretrained_model "./output/ResNet50_vd/best_model/ppcls" \
+    --use_gpu True \
+    --load_static_weights False \
+    --multilabel True \
+    --class_num 33
+```
+
+得到类似下面的输出：
+```    
+    class id: 3, probability: 0.6025
+    class id: 23, probability: 0.5491
+    class id: 32, probability: 0.7006
+```
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