Platform For AI:Image classification (torch)

Prerequisites

OSS is activated, and Machine Learning Studio is authorized to access OSS. For more information, see Activate OSS and Grant the permissions that are required to use Machine Learning Designer.

Limits

• The pose detection component is available only in Machine Learning Designer of Machine Learning Platform for AI (PAI).

• The pose detection component can only be used with the computing resources of Deep Learning Containers (DLC) of PAI.

Background information

The image classification (torch) component provides the convolutional neural network (CNN) and transformer model types and supports the ResNet, ResNeXt, HRNet, ViT, SwinT, and MobileNet-v2 algorithms. The component also provides the pre-trained ImageNet model to facilitate model tuning.

You can find the image classification (torch) component in the Offline Training subfolder in the Video Algorithm folder of the component library.

Configure the component in the PAI console

• Input ports

  Input port (from left to right)	Data type	Recommended upstream component	Required
  data annotation path for training	OSS	Read File Data	No
  data annotation path for evaluation	OSS	Read File Data	No

• Component parameters

  Tab	Parameter	Required	Description	Default value
  Fields Setting	model type	Yes	The model type used for model training. Only Classification is supported.	Classification
  	oss dir to save model	Yes	The Object Storage Service (OSS) directory in which the training model is stored. Example: oss://examplebucket/yunji.cjy/designer_test.	N/A
  	oss annotation path for training data	No	If you did not use the data annotation path for training input port to specify the labeled training data, you must configure this parameter. Note If you use both an input port and this parameter to specify the labeled training data, the value specified by the input port takes precedence. The OSS path of the labeled training data. Example: oss://examplebucket/yunji.cjy/data/imagenet/meta/train_labeled.txt. Each data record in the train_labeled.txt file is stored in the absolute path/image name.jpg label_id format. Important Separate image storage path and label_id with a space. Important The component supports the following formats: ClsSourceImageList and ClsSourceItag. You can use the data labeled by iTAG as training input.
  	oss annotation path for evaluation data	No	If you did not use the data annotation path for evaluation input port to specify the labeled evaluation data, you must configure this parameter. Note If you use both an input port and this parameter to specify the labeled evaluation data, the value specified by the input port takes precedence. The OSS path of the labeled evaluation data. Example: oss://examplebucket/yunji.cjy/data/imagenet/meta/val_labeled.txt. Each data record in the val_labeled.txt file is stored in the absolute path/image name.jpg label_id format. Important Separate image storage path and label_id with a space. Note The component supports the following formats: ClsSourceImageList and ClsSourceItag. You can use the data labeled by iTAG as training input.	N/A
  	class list file	Yes	The class list file parameter specifies a list of image category names. You can specify category names or the path of a TXT file that stores category names. If you specify category names, use the following format: [name1,name2,……]. Separate multiple category names with commas (,). Examples: [0, 1, 2] and [person, dog, cat]. If you specify the path of a TXT file, add category names to a TXT file, upload the file to an OSS bucket in the same region, and then specify the OSS path of the file. You can separate the category names in the TXT file with commas (,) or line breaks (\n). Examples: 0, 1, 2 and 0, \n 1, \n 2\n. If you do not configure this parameter, category names ranging from str(0) to str(num_classes-1) are used. num_classes indicates the number of categories. If the number of categories generated by image classification is 3, the category names are 0,1,2.	N/A
  	Data Source Type	Yes	The type of input data. Valid values: ClsSourceImageList and ClsSourceItag.	ClsSourceItag
  	oss path for pretrained model	No	The OSS path in which your pre-trained model is stored. If you have a pre-trained model, set this parameter to the OSS path of your pre-trained model. If you do not configure this parameter, the default pre-trained model provided by PAI is used.	N/A
  Parameters Setting	backbone	Yes	The backbone model that you want to use. Valid values: resnet resnext hrnet vit swint mobilenetv2 inceptionv4	resnet
  	num classes	Yes	The number of categories in the data.	N/A
  	image size after resizing	Yes	The length and width to which images are resized. By default, the length and width are the same.	224
  	optimizer	Yes	The optimization method for model training. Valid values: SGD Adam	SGD
  	initial learning rate	Yes	The initial learning rate.	0.05
  	learning rate policy	Yes	The policy that is used to adjust the learning rate. Only step is supported. A value of step indicates that the learning rate is manually adjusted at specific epochs.	step
  	lr step	Yes	The epochs at which the learning rate is adjusted. Separate multiple values with commas (,). If the number of epochs reaches the values specified by this parameter, the learning rate automatically decays by 90%. For example, the initial learning rate is set to 0.1, the total number of epochs is set to 20, and the lr step parameter is set to 5,10. In this case, when the training is in epoch 1 to 5, the learning rate is 0.1. When the training enters epoch 6, the learning rate decays to 0.01 and continues until epoch 10 ends. When the training enters epoch 11, the learning rate decays to 0.001 and continues until the end of all epochs.	[30,60,90]
  	train batch size	Yes	The size of a training batch. The value indicates the number of data samples used for model training in a single iteration.	2
  	eval batch size	Yes	The size of an evaluation batch. The value indicates the number of data samples used for model evaluation in each iteration.	2
  	total train epochs	Yes	The total number of epochs. An epoch ends when a round of training is complete on all data samples. The total number of epochs indicates the total number of training rounds conducted on data samples.	1
  	save checkpoint epoch	No	The frequency at which a checkpoint is saved. For example, a value of 1 indicates that a checkpoint is saved each time an epoch ends.	1
  	type of export model	Yes	The format in which the model is exported. Valid values: raw onnx	raw
  Tuning	number process of reading data per gpu	No	The number of threads used to read the training data for each GPU.	4
  	use fp 16	No	Specifies whether to enable FP16 to reduce memory usage during model training.	N/A
  	single worker or distributed on DLC	Yes	The compute engine that is used to run the component. You can select a compute engine based on your business requirements. Valid values: single_on_dlc distribute_on_dlc	single_on_dlc
  	number of worker	No	This parameter is required if you set the single worker or distributed on DLC parameter to distribute_on_dlc. The number of concurrent workers for computing.	1
  	cpu machine type	No	This parameter is required only if you set the single worker or distributed on DLC parameter to distribute_on_dlc. The CPU type that you want to use.	16vCPU+64GB Mem-ecs.g6.4xlarge
  	gpu machine type	Yes	The GPU type that you want to use.	8vCPU+60GB Mem+1xp100-ecs.gn5-c8g1.2xlarge

• Output ports

  Output port	Data type	Downstream component
  output model	The OSS directory in which the output model is stored. The value is the same as that you specified for the oss dir to save model parameter on the Fields Setting tab.	image prediction

Examples