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‎docs/apis/datasets.md‎

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```
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读取ImageNet格式的分类数据集，并对样本进行相应的处理。ImageNet数据集格式的介绍可查看文档:[数据集格式说明](../data/format/index.html)
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示例：[代码文件](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/classification/mobilenetv2.py#L25)
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示例：[代码文件](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/image_classification/mobilenetv2.py)
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> **参数**
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> > * **parallel_method** (str): 数据集中样本在预处理过程中并行处理的方式，支持'thread'线程和'process'进程两种方式。默认为'process'（Windows和Mac下会强制使用thread，该参数无效）。
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> > * **shuffle** (bool): 是否需要对数据集中样本打乱顺序。默认为False。
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## paddlex.datasets.PascalVOC
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## paddlex.datasets.VOCDetection
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> **用于目标检测模型**
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```
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paddlex.datasets.PascalVOC(data_dir, file_list, label_list, transforms=None, num_workers=‘auto’, buffer_size=100, parallel_method='thread', shuffle=False)
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paddlex.datasets.VOCDetection(data_dir, file_list, label_list, transforms=None, num_workers=‘auto’, buffer_size=100, parallel_method='thread', shuffle=False)
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```
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> 读取PascalVOC格式的检测数据集，并对样本进行相应的处理。PascalVOC数据集格式的介绍可查看文档:[数据集格式说明](../data/format/index.html)
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> 示例：[代码文件](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/detection/yolov3_darknet53.py#L29)
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> 示例：[代码文件](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/yolov3_darknet53.py)
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> **参数**
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> > * **parallel_method** (str): 数据集中样本在预处理过程中并行处理的方式，支持'thread'线程和'process'进程两种方式。默认为'process'（Windows和Mac下会强制使用thread，该参数无效）。
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> > * **shuffle** (bool): 是否需要对数据集中样本打乱顺序。默认为False。
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## paddlex.datasets.MSCOCO
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## paddlex.datasets.CocoDetection
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> **用于实例分割/目标检测模型**
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```
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paddlex.datasets.MSCOCO(data_dir, ann_file, transforms=None, num_workers='auto', buffer_size=100, parallel_method='thread', shuffle=False)
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paddlex.datasets.CocoDetection(data_dir, ann_file, transforms=None, num_workers='auto', buffer_size=100, parallel_method='thread', shuffle=False)
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```
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> 读取MSCOCO格式的检测数据集，并对样本进行相应的处理，该格式的数据集同样可以应用到实例分割模型的训练中。MSCOCO数据集格式的介绍可查看文档:[数据集格式说明](../data/format/index.html)
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> 示例：[代码文件](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/detection/mask_rcnn_r50_fpn.py#L27)
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> 示例：[代码文件](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/instance_segmentation/mask_rcnn_r50_fpn.py)
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> **参数**
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> 读取语义分割任务数据集，并对样本进行相应的处理。语义分割任务数据集格式的介绍可查看文档:[数据集格式说明](../data/format/index.html)
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> 示例：[代码文件](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/segmentation/unet.py#L27)
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> 示例：[代码文件](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/semantic_segmentation/unet.py)
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> **参数**
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‎docs/apis/transforms/cls_transforms.md‎

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* **hue_range** (int): 色调因子的范围。默认为18。
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* **hue_prob** (float): 随机调整色调的概率。默认为0.5。
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<!--
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## ComposedClsTransforms
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```python
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paddlex.cls.transforms.ComposedClsTransforms(mode, crop_size=[224, 224], mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], random_horizontal_flip=True)
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transforms.Normalize()
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])
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```
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-->

‎docs/apis/transforms/det_transforms.md‎

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* **allow_no_crop** (bool): 是否允许未进行裁剪。默认值为True。
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* **cover_all_box** (bool): 是否要求所有的真实标注框都必须在裁剪区域内。默认值为False。
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<!--
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## ComposedRCNNTransforms
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```python
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paddlex.det.transforms.ComposedRCNNTransforms(mode, min_max_size=[224, 224], mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], random_horizontal_flip=True)
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```
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-->

‎docs/apis/transforms/seg_transforms.md‎

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* **hue_range** (int): 色调因子的范围。默认为18。
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* **hue_prob** (float): 随机调整色调的概率。默认为0.5。
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<!--
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## ComposedSegTransforms
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paddlex.det.transforms.ComposedSegTransforms(mode, min_max_size=[400, 600], train_crop_shape=[769, 769], mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], random_horizontal_flip=True)
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```
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-->

‎docs/quick_start.md‎

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# 10分钟快速上手使用
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本文档在一个小数据集上展示了如何通过PaddleX进行训练，您可以阅读PaddleX的**使用教程**来了解更多模型任务的训练使用方式。本示例同步在AIStudio上，可直接[在线体验模型训练](https://aistudio.baidu.com/aistudio/projectdetail/439860)
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本文档在一个小数据集上展示了如何通过PaddleX进行训练。本示例同步在AIStudio上，可直接[在线体验模型训练](https://aistudio.baidu.com/aistudio/projectdetail/450220)。
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本示例代码源于Github [tutorials/train/classification/mobilenetv3_small_ssld.py](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/image_classification/mobilenetv3_small_ssld.py)，用户可自行下载至本地运行。
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PaddleX中的所有模型训练跟随以下3个步骤，即可快速完成训练代码开发！
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<a name="定义训练验证图像处理流程transforms"></a>
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**3. 定义训练/验证图像处理流程transforms**
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由于训练时数据增强操作的加入，因此模型在训练和验证过程中，数据处理流程需要分别进行定义。如下所示，代码在`train_transforms`中加入了[RandomCrop](apis/transforms/cls_transforms.html#RandomCrop)和[RandomHorizontalFlip](apis/transforms/cls_transforms.html#RandomHorizontalFlip)两种数据增强方式, 更多方法可以参考[数据增强文档](apis/transforms/augment.md)。
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由于训练时数据增强操作的加入，因此模型在训练和验证过程中，数据处理流程需要分别进行定义。如下所示，代码在`train_transforms`中加入了[RandomCrop](apis/transforms/cls_transforms.html#randomcrop)和[RandomHorizontalFlip](apis/transforms/cls_transforms.html#randomhorizontalflip)两种数据增强方式, 更多方法可以参考[数据增强文档](apis/transforms/augment.md)。
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```
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**4. 定义`dataset`加载图像分类数据集**
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- [paddlex.datasets.ImageNet接口说明](apis/datasets/classification.md)
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- [paddlex.datasets.ImageNet接口说明](apis/datasets.md)
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- [ImageNet数据格式说明](data/format/classification.md)
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<a name="更多使用教程"></a>
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**更多使用教程**
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- 1.[目标检测模型训练](tutorials/train/detection.md)
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- 2.[语义分割模型训练](tutorials/train/segmentation.md)
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- 3.[实例分割模型训练](tutorials/train/instance_segmentation.md)
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- 4.[模型太大，想要更小的模型，试试模型裁剪吧!](tutorials/compress/classification.md)
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- 1.[目标检测模型训练](train/object_detection.md)
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- 2.[语义分割模型训练](train/semantic_segmentation.md)
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- 3.[实例分割模型训练](train/instance_segmentation.md)
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- 4.[模型太大，想要更小的模型，试试模型裁剪吧!](https://github.com/PaddlePaddle/PaddleX/tree/develop/tutorials/compress)

‎docs/train/classification.md‎

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| 模型(点击获取代码) | Top1精度 | 模型大小 | GPU预测速度 | Arm预测速度 | 备注 |
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| :---------------- | :------- | :------- | :--------- | :--------- | :----- |
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| [MobileNetV3_small_ssld](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/image_classification/mobilenetv3_small_ssld.py) | 71.3% | 21.0MB | 6.809ms | - | 模型小，预测速度快，适用于低性能或移动端设备 |
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| [MobileNetV2](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/image_classification/mobilenetv2.py) | 72.2% | 14.0MB | 4.546ms | - | 模型小，预测速度快，适用于低性能或移动端设备 |
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| [ShuffleNetV2](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/image_classification/shufflenetv2.py) | 68.8% | 9.0MB | 6.101ms | - | 模型体积小，预测速度快，适用于低性能或移动端设备 |
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| [ResNet50_vd_ssld](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/image_classification/resnet50_vd_ssld.py) | 82.4% | 102.8MB | 9.058ms | - | 模型精度高，适用于服务端部署 |
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| [MobileNetV3_small_ssld](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/image_classification/mobilenetv3_small_ssld.py) | 71.3% | 21.0MB | 6.809ms | - | 模型小，预测速度快，适用于低性能或移动端设备 |
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| [MobileNetV2](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/image_classification/mobilenetv2.py) | 72.2% | 14.0MB | 4.546ms | - | 模型小，预测速度快，适用于低性能或移动端设备 |
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| [ShuffleNetV2](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/image_classification/shufflenetv2.py) | 68.8% | 9.0MB | 6.101ms | - | 模型体积小，预测速度快，适用于低性能或移动端设备 |
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| [ResNet50_vd_ssld](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/image_classification/resnet50_vd_ssld.py) | 82.4% | 102.8MB | 9.058ms | - | 模型精度高，适用于服务端部署 |
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## 开始训练

‎docs/train/instance_segmentation.md‎

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| 模型(点击获取代码) | Box MMAP/Seg MMAP | 模型大小 | GPU预测速度 | Arm预测速度 | 备注 |
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| :---------------- | :------- | :------- | :--------- | :--------- | :----- |
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| [MaskRCNN-ResNet50-FPN](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/instance_segmentation/mask_rcnn_r50_fpn.py) | 36.5%/32.2% | 170.0MB | 160.185ms | - | 模型精度高，适用于服务端部署 |
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| [MaskRCNN-ResNet18-FPN](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/instance_segmentation/mask_rcnn_r18_fpn.py) | -/- | 120.0MB | - | - | 模型精度高，适用于服务端部署 |
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| [MaskRCNN-HRNet-FPN](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/instance_segmentation/mask_rcnn_hrnet_fpn.py) | -/- | 116.MB | - | - | 模型精度高，预测速度快，适用于服务端部署 |
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| [MaskRCNN-ResNet50-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/instance_segmentation/mask_rcnn_r50_fpn.py) | 36.5%/32.2% | 170.0MB | 160.185ms | - | 模型精度高，适用于服务端部署 |
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| [MaskRCNN-ResNet18-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/instance_segmentation/mask_rcnn_r18_fpn.py) | -/- | 120.0MB | - | - | 模型精度高，适用于服务端部署 |
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| [MaskRCNN-HRNet-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/instance_segmentation/mask_rcnn_hrnet_fpn.py) | -/- | 116.MB | - | - | 模型精度高，预测速度快，适用于服务端部署 |
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‎docs/train/object_detection.md‎

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| [YOLOv3-MobileNetV1](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/object_detection/yolov3_mobilenetv1.py) | 29.3% | 99.2MB | 15.442ms | - | 模型小，预测速度快，适用于低性能或移动端设备 |
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| [YOLOv3-MobileNetV3](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/object_detection/yolov3_mobilenetv3.py) | 31.6% | 100.7MB | 143.322ms | - | 模型小，移动端上预测速度有优势 |
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| [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/object_detection/yolov3_darknet53.py) | 38.9 | 249.2MB | 42.672ms | - | 模型较大，预测速度快，适用于服务端 |
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| [FasterRCNN-ResNet50-FPN](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/object_detection/faster_rcnn_r50_fpn.py) | 37.2% | 136.0MB | 197.715ms | - | 模型精度高，适用于服务端部署 |
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| [FasterRCNN-ResNet18-FPN](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/object_detection/faster_rcnn_r18_fpn.py) | - | - | - | - | 模型精度高，适用于服务端部署 |
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| [FasterRCNN-HRNet-FPN](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/object_detection/faster_rcnn_hrnet_fpn.py) | 36.0% | 115.MB | 81.592ms | - | 模型精度高，预测速度快，适用于服务端部署 |
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| [YOLOv3-MobileNetV1](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/yolov3_mobilenetv1.py) | 29.3% | 99.2MB | 15.442ms | - | 模型小，预测速度快，适用于低性能或移动端设备 |
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| [YOLOv3-MobileNetV3](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/yolov3_mobilenetv3.py) | 31.6% | 100.7MB | 143.322ms | - | 模型小，移动端上预测速度有优势 |
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| [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/yolov3_darknet53.py) | 38.9 | 249.2MB | 42.672ms | - | 模型较大，预测速度快，适用于服务端 |
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| [FasterRCNN-ResNet50-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/faster_rcnn_r50_fpn.py) | 37.2% | 136.0MB | 197.715ms | - | 模型精度高，适用于服务端部署 |
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| [FasterRCNN-ResNet18-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/faster_rcnn_r18_fpn.py) | - | - | - | - | 模型精度高，适用于服务端部署 |
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| [FasterRCNN-HRNet-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/faster_rcnn_hrnet_fpn.py) | 36.0% | 115.MB | 81.592ms | - | 模型精度高，预测速度快，适用于服务端部署 |
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‎docs/train/semantic_segmentation.md‎

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| [DeepLabv3p-MobileNetV2-x0.25](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/semantic_segmentation/deeplabv3p_mobilenetv2_x0.25.py) | - | 2.9MB | - | - | 模型小，预测速度快，适用于低性能或移动端设备 |
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| [DeepLabv3p-MobileNetV2-x1.0](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/semantic_segmentation/deeplabv3p_mobilenetv2.py) | 69.8% | 11MB | - | - | 模型小，预测速度快，适用于低性能或移动端设备 |
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| [DeepLabv3p-Xception65](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/semantic_segmentation/deeplabv3p_xception65.pyy) | 79.3% | 158MB | - | - | 模型大，精度高，适用于服务端 |
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| [UNet](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/semantic_segmentation/unet.py) | - | 52MB | - | - | 模型较大，精度高，适用于服务端 |
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| [FastSCNN](https://github.com/PaddlePaddle/PaddleX/blob/doc/tutorials/train/semantic_segmentation/fast_scnn.py) | - | 4.5MB | - | - | 模型小，预测速度快，适用于低性能或移动端设备 |
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| [DeepLabv3p-MobileNetV2-x0.25](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/semantic_segmentation/deeplabv3p_mobilenetv2_x0.25.py) | - | 2.9MB | - | - | 模型小，预测速度快，适用于低性能或移动端设备 |
14+
| [DeepLabv3p-MobileNetV2-x1.0](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/semantic_segmentation/deeplabv3p_mobilenetv2.py) | 69.8% | 11MB | - | - | 模型小，预测速度快，适用于低性能或移动端设备 |
15+
| [DeepLabv3p-Xception65](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/semantic_segmentation/deeplabv3p_xception65.pyy) | 79.3% | 158MB | - | - | 模型大，精度高，适用于服务端 |
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| [UNet](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/semantic_segmentation/unet.py) | - | 52MB | - | - | 模型较大，精度高，适用于服务端 |
17+
| [HRNet](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/semantic_segmentation/hrnet.py) | 79.4% | 37MB | - | - | 模型较小，模型精度高，适用于服务端部署 |
18+
| [FastSCNN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/semantic_segmentation/fast_scnn.py) | - | 4.5MB | - | - | 模型小，预测速度快，适用于低性能或移动端设备 |
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2020

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## 开始训练

‎paddlex/__init__.py‎

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@@ -13,6 +13,7 @@
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# limitations under the License.
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from __future__ import absolute_import
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import os
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if 'FLAGS_eager_delete_tensor_gb' not in os.environ:
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os.environ['FLAGS_eager_delete_tensor_gb'] = '0.0'
@@ -21,6 +22,7 @@
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if "CUDA_VISIBLE_DEVICES" in os.environ:
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if os.environ["CUDA_VISIBLE_DEVICES"].count("-1") > 0:
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os.environ["CUDA_VISIBLE_DEVICES"] = ""
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from .utils.utils import get_environ_info
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from . import cv
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from . import det
@@ -38,7 +40,7 @@
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"[WARNING] pycocotools is not installed, detection model is not available now."
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)
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print(
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"[WARNING] pycocotools install: https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/install.md"
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"[WARNING] pycocotools install: https://paddlex.readthedocs.io/zh_CN/develop/install.html#pycocotools"
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)
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import paddlehub as hub
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from . import interpret
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__version__ = '1.0.7'
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__version__ = '1.0.8'

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