EasyVision of Machine Learning Platform for AI (PAI) allows you to perform model training and prediction in image object detection. You can also use EasyVision to perform distributed training and prediction on multiple servers. This topic describes how to use a trained model of EasyVision to perform offline object detection.

Data formats

For more information, see Input data formats.

Image object detection

Based on the generated file list, you can start prediction tasks of image object detection by running the PAI command.
pai -name ev_predict_ext
             -Dmodel_path='OSS path of your model'
             -Dmodel_type='detector'
             -Dinput_oss_file='oss://path/to/your/filelist.txt'
             -Doutput_oss_file='oss://path/to/your/result.txt'
             -Dimage_type='url'
             -Dnum_worker=2
             -DcpuRequired=800
             -DgpuRequired=100
             -Dbuckets='Your OSS folder'
             -Darn='Alibaba Cloud Resource Name (ARN) of your role'
             -DossHost='Your OSS domain'
For more information, see Parameters.

Output

Prediction results are written to an output file. Each entry in the file consists of an OSS path and a JSON sting. The OSS path indicates the path of the original image, whereas the JSON string indicates the prediction result. The entries are in the following format:
oss://path/to/your/image1.jpg, JSON string
oss://path/to/your/image1.jpg, JSON string
oss://path/to/your/image1.jpg, JSON string
The JSON string is in the following format:
{
  "detection_boxes": [[243.5308074951172, 197.69570922851562, 385.59625244140625, 247.7247772216797], [292.1929931640625, 114.28043365478516, 571.2748413085938, 165.09771728515625]], 
  "detection_scores": [0.9942291975021362, 0.9940272569656372],
  "detection_classes": [1, 1],
  "detection_classe_names": ["text", "text"]
 }
The following table describes the parameters in the JSON string.
Parameter Description Shape Type
detection_boxes The detected area with coordinates in the following order: [top, left, bottom, right]. [num_detections, 4] FLOAT
detection_scores The detection probability of the object. num_detections FLOAT
detection_classes The ID of the class to which the object belongs. num_detections INT
detection_class_names The name of the class to which the object belongs. num_detections STRING