Platform For AI:Object detection (easycv)

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 object detection (easycv) component is available only in Machine Learning Designer of PAI.

• You can use the object detection (easycv) component based only on Deep Learning Containers (DLC).

Configure the component in the PAI console

• Input ports

  Input port (from left to right)	Data type	Recommended upstream component	Required
  TFRecords for Training	OSS	Read File Data	No. If you do not want to use a port for this input, you need to configure the oss path to training tfrecord parameter on the Fields Setting tab.
  TFRecords for Evaluation	OSS	Read File Data	No. If you do not want to use a port for this input, you need to configure the oss path to evaluation tfrecord parameter on the Fields Setting tab.
  yolov5 class list file	OSS	Read File Data	No. If you do not want to use a port for this input, you need to configure the oss path of class list file parameter on the Fields Setting tab.

• Component parameters

  Tab	Parameter	Required	Description	Default value
  Fields Setting	model type	Yes	The type of the model that you want to train. Valid values: FCOS YOLOX	YOLOX
  	oss dir to save model	No	The OSS path in which you want to store the trained model. Example: examplebucket.oss-cn-shanghai-internal.aliyuncs.com/test/ckpt/. If you leave this parameter empty, the default path of the workspace is used.	N/A
  	oss annotation path for training set	No	If you set the data format to DetSourcePAI, upload the file in the .manifest format. If you set the data format to COCO, upload the file in the .json format. If you configured an input port, you can leave this parameter empty. If you configure both the input port and this parameter, the training task uses the values provided by the input port.	N/A
  	oss annotation path for validation set	No	If you set the data format to DetSourcePAI, upload the file in the .manifest format. If you set the data format to COCO, upload the file in the .json format. If you configured an input port, you can leave this parameter empty. When both the input port and this parameter are configured, the training task uses the values provided by the input port.	N/A
  	oss path of class list file	No	Specify the file that contains a list of tags in the .txt format. If you configured an input port, you can leave this parameter empty. If you configure both the input port and this parameter, the training task uses the values provided by the input port.	N/A
  	oss path to pretrained model	No	The Object Storage Service (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 leave this parameter empty, the default pre-trained model provided by PAI is used.	N/A
  	oss path to training tfrecord	Yes	The path for the training data. This parameter is required only if you set the data source to COCO.	N/A
  	oss path to evaluation tfrecord	Yes	The path for the evaluation data. This parameter is required only if you set the data source to COCO.
  	data format	Yes	The scope of the event. Valid values: COCO DetSourcePAI	DetSourcePAI
  Parameters Setting	YOLOX model Structure	Yes	This parameter is required only if you set the model type to YOLOX. Select the YOLOX model type you want to use from the drop-down list. yolox-s yolox-m yolox-l yolox-x	yolo-s
  	num classes	Yes	The number of categories in the dataset.	20
  	image scale	Yes	The size of the image after resizing. Separate the height and width with a space. Example: 320 320.	320 320
  	optimizer	Yes	This parameter is required only if you set the model type parameter to YOLOX. The optimization method for model training. Valid values: momentum adam	momentum
  	initial learning rate	Yes	The initial learning rate.	0.01
  	batch size	Yes	The size of a training batch. This parameter specifies the number of samples to use during the training process and subsequent iterative training cycles.	8
  	eval batch size	Yes	The size of an evaluation batch. This parameter specifies the number of samples to use during the training process and subsequent iterative training cycles.	8
  	num epochs	Yes	The total number of training epochs. Note If you set the model type to YOLOX, the total number of epochs must be greater than the sum of the warmup epochs and the epochs that have a steady learning rate. The epochs that have a steady learning rate are specified by the last no augmented lr epochs parameter.	20
  	loss frequency	No	The print frequency of the loss value. The default value is 200, which indicates that the loss value is printed every 200 batches.
  	Verification frequency	No	The default value is 2, which indicates that a verification is performed every three epochs.
  	warmup epochs	No	This parameter is required only if you set the model type to YOLOX.	5
  	last no augmented lr epochs	No	This parameter is required only if you set the model type to YOLOX.	5
  	type of export model	Yes	The format in which the model is exported. Valid values: raw jit	raw
  	save checkpoint epoch	No	The frequency at which a checkpoint is saved. A value of 1 indicates that all data is saved once for an iteration.	1
  Tuning	gpu machine type	Yes	You need to specify a GPU specification for this algorithm.	4
  	evtorch model with fp16	No	Specifies whether to enable FP16 to reduce memory usage during model training.	false
  	single worker or distributed on MaxCompute or DLC	Yes	Only distribute_on_dlc is available.	distribute_on_dlc

• Output ports

  Output port (from left to right)	Data type	Downstream component
  Output model	The OSS path in which the output model is stored. The value is the same as the value that you specified for the oss dir to save model parameter on the Fields Setting tab. The output model in the SavedModel format is stored in this OSS path.	image prediction

Examples