# 花朵分类：从 PaddleLabel 到 PaddleClas PaddleLabel 标注数据+PaddleClas 训练预测=快速完成一次花朵分类的任务 --- ## 1. 数据准备 - 首先使用`PaddleLabel`对自制的花朵数据集进行标注，其次使用`Split Dataset`功能分割数据集，最后导出数据集 - 从`PaddleLabel`导出后的内容全部放到自己的建立的文件夹下，例如`flower_clas_dataset`，其目录结构如下： ``` ├── flower_clas_dataset │ ├── image │ │ ├── flower1.jpg │ │ ├── flower2.jpg │ │ ├── ... │ ├── labels.txt │ ├── test_list.txt │ ├── train_list.txt │ ├── val_list.txt ``` ## 2. 训练 ### 2.1 安装必备的库 **2.1.1 安装 paddlepaddle** ``` # 您的机器安装的是 CUDA9 或 CUDA10，请运行以下命令安装 # pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple # 您的机器是CPU，请运行以下命令安装 pip install paddlepaddle ``` **2.1.2 安装 paddleclas 以及依赖项** ``` git clone https://gitee.com/paddlepaddle/PaddleClas.git -b release/2.2 cd PaddleClas pip install -r requirements.txt python setup.py install ``` ### 2.2 准备自制的花朵分类数据集 ``` cd ./PaddleClas/dataset/ mkdir flower_clas_dataset cd ../../ cp -r ./flower_clas_dataset/* ./PaddleClas/dataset/flower_clas_dataset ``` ### 2.3 修改配置文件 > PaddleClas/ppcls/configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml ``` # global configs Global: checkpoints: null pretrained_model: null output_dir: ./output/ device: cpu save_interval: 20 eval_during_train: True eval_interval: 10 epochs: 100 print_batch_step: 10 use_visualdl: True # used for static mode and model export image_shape: [3, 224, 224] save_inference_dir: ./inference # model architecture Arch: name: ShuffleNetV2_x0_25 class_num: 3 # loss function config for traing/eval process Loss: Train: - CELoss: weight: 1.0 Eval: - CELoss: weight: 1.0 Optimizer: name: Momentum momentum: 0.9 lr: name: Cosine learning_rate: 0.0125 warmup_epoch: 5 regularizer: name: 'L2' coeff: 0.00001 # data loader for train and eval DataLoader: Train: dataset: name: ImageNetDataset image_root: ./dataset/ cls_label_path: ./dataset/train_list.txt transform_ops: - DecodeImage: to_rgb: True channel_first: False - RandCropImage: size: 224 - RandFlipImage: flip_code: 1 - NormalizeImage: scale: 1.0/255.0 mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: '' sampler: name: DistributedBatchSampler batch_size: 16 drop_last: False shuffle: True loader: num_workers: 0 use_shared_memory: True Eval: dataset: name: ImageNetDataset image_root: ./dataset/ cls_label_path: ./dataset/val_list.txt transform_ops: - DecodeImage: to_rgb: True channel_first: False - ResizeImage: resize_short: 256 - CropImage: size: 224 - NormalizeImage: scale: 1.0/255.0 mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: '' sampler: name: DistributedBatchSampler batch_size: 32 drop_last: False shuffle: False loader: num_workers: 0 use_shared_memory: True Infer: infer_imgs: dataset/predict_demo.jpg batch_size: 10 transforms: - DecodeImage: to_rgb: True channel_first: False - ResizeImage: resize_short: 256 - CropImage: size: 224 - NormalizeImage: scale: 1.0/255.0 mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: '' - ToCHWImage: PostProcess: name: Topk topk: 3 Metric: Train: - TopkAcc: topk: [1, 3] Eval: - TopkAcc: topk: [1, 3] ``` ### 2.4 添加类别映射文件 > PaddleClas/ppcls/configs/quick_start/new_user/label.txt ``` sunflower rose dandelion ``` ### 2.5 开始训练 ``` export CUDA_VISIBLE_DEVICES=0 # 开始训练 python PaddleClas/tools/train.py -c ./PaddleClas/ppcls/configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml ``` ## 3. 模型评估 ### 3.1 评估 ``` python PaddleClas/tools/eval.py -c ./PaddleClas/ppcls/configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml ``` ### 3.2 预测 ``` python3 PaddleClas/tools/infer.py \ -c ./PaddleClas/ppcls/configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml \ -o Infer.infer_imgs=dataset/predict_demo.jpg \ -o Global.pretrained_model=output/ShuffleNetV2_x0_25/latest ``` 预测的样例图片是： 预测的结果是： > {'class_ids': [0, 1, 2], 'scores': [0.89812, 0.09476, 0.00712], 'file_name': 'dataset/predict_demo.jpg', 'label_names': []} > 也就是说 0 的概率最大，为 0.89812，0 对应的结果是向日葵，也就是说结果是向日葵，预测无误。 ## AI Studio 第三方教程推荐 [快速体验演示案例](https://aistudio.baidu.com/aistudio/projectdetail/4337003)