All Products
Search

This Product

    Platform For AI:Image classification training (Torch)

    Document Center

    Platform For AI:Image classification training (Torch)

    Last Updated:Apr 02, 2026

    The image classification (Torch) component in Platform for AI (PAI) lets you train image classification models using PyTorch within a visual, no-code pipeline in Machine Learning Designer. The component supports popular backbone architectures and integrates with Object Storage Service (OSS) for data and model storage.

    Prerequisites

    Before you begin, ensure that you have:

    • An activated OSS bucket in the same region as your Designer workspace

    • Machine Learning Designer authorized to access OSS. See Activate OSS and Grant permissions

    Limitations

    • This component is available only in Machine Learning Designer.

    • The only supported compute engine is Deep Learning Container (DLC).

    How it works

    The component supports two training approaches:

    • Transfer learning (recommended): The component starts with the default pre-trained ImageNet model and fine-tunes it on your labeled data. This approach requires less training data and converges faster. To use a custom pre-trained model instead, specify its OSS path in the oss path for pretrained model parameter.

    • Using the PAI default pre-trained model: If you leave the pre-trained model path blank, the component uses the default pre-trained model provided by PAI.

    All training runs on DLC. After training, the model artifact is saved to the OSS directory you specify and can be passed directly to the General Image Prediction component for batch inference.

    Prepare your data

    The component accepts labeled training and evaluation data in two formats:

    Format Description
    ClsSourceImageList A TXT annotation file where each line is <absolute-path/image-name.jpg> <label_id>, separated by a space
    ClsSourceItag Data labeled with iTAG, PAI's built-in data annotation tool

    Annotation file example (ClsSourceImageList):

    oss://examplebucket/data/images/cat001.jpg 0
oss://examplebucket/data/images/dog001.jpg 1
oss://examplebucket/data/images/bird001.jpg 2
    Important

    Separate the image path and label_id with a single space. Do not use tabs or other delimiters. The label_id is a zero-based integer index corresponding to the class position in your class list file.

    To prepare iTAG-labeled data, see iTAG overview.

    Configure the component

    Find the image classification (Torch) component in the component library under Video Algorithm > Offline Training. After adding it to your pipeline, configure the following tabs.

    Input ports

    Connect upstream Read OSS data components to the input ports to pass labeled data directly into the pipeline.

    Input port Data type Required
    Data annotation path for training OSS No
    Data annotation path for evaluation OSS No
    If you connect data through an input port and also set the corresponding annotation path parameter, the input port value takes precedence.

    Fields Setting tab

    Parameter Required Description Default
    model type Yes The classification task type. Only classification is supported. Classification
    oss dir to save model Yes The OSS directory where the trained model is saved. Example: oss://examplebucket/yunji.cjy/designer_test —
    oss annotation path for training data No OSS path of the labeled training annotation file. Required if no training input port is connected. Example: oss://examplebucket/yunji.cjy/data/imagenet/meta/train_labeled.txt —
    oss annotation path for evaluation data No OSS path of the labeled evaluation annotation file. Required if no evaluation input port is connected. Example: oss://examplebucket/yunji.cjy/data/imagenet/meta/val_labeled.txt —
    class list file Yes Specifies image category names. Accepts three formats: a bracket-delimited list (e.g., [person, dog, cat]), an OSS path to a TXT file with category names separated by commas or line breaks, or leave blank to use numeric labels 0 through num_classes-1. —
    Data Source Type Yes The annotation format of your input data. Valid values: ClsSourceImageList, ClsSourceItag. ClsSourceItag
    oss path for pretrained model No OSS path of a custom pre-trained model. If left blank, the default pre-trained model provided by PAI is used. —

    Parameters Setting tab

    The three parameters with the greatest impact on training quality are backbone, initial learning rate, and optimizer. Set these deliberately; the remaining parameters are safe to leave at their defaults for initial runs.

    Parameter Required Description Default
    backbone Yes The backbone architecture for feature extraction. Valid values: resnet, resnext, hrnet, vit, swint, mobilenetv2, inceptionv4. resnet
    num classes Yes The number of image categories in your dataset. —
    image size after resizing Yes The pixel dimension to which images are resized (square). 224
    optimizer Yes The optimization algorithm. Valid values: SGD, Adam. SGD
    initial learning rate Yes The starting learning rate. 0.05
    learning rate policy Yes The strategy for adjusting the learning rate during training. Only step is supported. step
    lr step Yes The epoch numbers at which the learning rate decays by 90%. Separate multiple values with commas. Example: 5,10 causes the learning rate to multiply by 0.1 at the start of epoch 6 and again at the start of epoch 11. [30,60,90]
    train batch size Yes Number of samples per training iteration. 2
    eval batch size Yes Number of samples per evaluation iteration. 2
    total train epochs Yes Total number of training passes over the full dataset. 1
    save checkpoint epoch No Interval (in epochs) at which a checkpoint is saved. Set to 1 to save a checkpoint after every epoch. 1
    Exported model type Yes Format of the exported model. Valid values: raw, ONNX. raw

    Choosing a backbone:

    Choose based on your dataset size, accuracy requirements, and available compute. Larger, more powerful backbones require more GPU memory and longer training time.

    Backbone Best for
    ResNet General-purpose; strong baseline for most datasets
    ResNeXt Higher accuracy than ResNet, with moderate additional cost
    HRNet Tasks requiring fine spatial detail
    ViT (Vision Transformer) Large datasets; excellent accuracy but compute-intensive
    Swin Transformer (SwinT) Hierarchical features; good accuracy-efficiency trade-off
    MobileNetV2 Resource-constrained environments; fast inference
    InceptionV4 High accuracy on diverse classification tasks

    Tuning tab

    Parameter Required Description Default
    number process of reading data per gpu No Number of data-loading threads per GPU. 4
    use fp 16 No Enables FP16 (half-precision) training to reduce GPU memory usage. —
    single worker or distributed on DLC Yes Compute mode. Use single_on_dlc for single-node training or distribute_on_dlc for multi-node distributed training. single_on_dlc
    number of worker No Number of worker nodes. Required when using distribute_on_dlc. 1
    cpu machine type No CPU instance type. Required when using distribute_on_dlc. 16vCPU+64GB Mem-ecs.g6.4xlarge
    gpu machine type Yes GPU instance type. 8vCPU+60GB Mem+1xp100-ecs.gn5-c8g1.2xlarge

    Output port

    Output port Data type Downstream component
    Output model OSS path General Image Prediction

    The output is the OSS directory path configured in the oss dir to save model parameter. Connect this port to the General Image Prediction component to run batch inference on your trained model.

    Example: end-to-end image classification pipeline

    The following figure shows a sample pipeline that trains an image classifier and runs batch inference.

    工作流

    Steps:

    1. Prepare and label data. Label your images using iTAG. For details, see iTAG overview.

    2. Read training and evaluation data. Add two Read File Data components to the canvas. Set the OSS Data Path parameter of each component to the OSS path of your labeled training data and evaluation data, respectively.

      Important

      Set Data Source Type to ClsSourceItag when using iTAG-labeled data.

    3. Configure the image classification (Torch) component. Connect the two Read File Data components to the training and evaluation input ports of the image classification (Torch)-1 component. Configure the parameters as described in Configure the component.

    4. Read prediction data. Add a third Read File Data component and set its OSS Data Path to the OSS path of the images you want to classify.

    5. Run batch inference. Add an image prediction component and configure the following parameters: For full configuration details, see image prediction.

      • model type: torch_classifier

      • oss path for model: Set this to the same value as the oss dir to save model parameter from the image classification (Torch)-1 component.

    What's next