Intelligent image processing models - AI Computing Asset Management| Alibaba Cloud Documentation Center

Model	Feature
General image classification model	Returns the categories of recognized objects in images.
General image recognition model	Returns the categories and positions of recognized objects in images.
Semantic segmentation model	Returns the categories of segmented objects and the information about segmentation masks.
Instance segmentation model	Returns the categories and locations of segmented objects, and the information about segmentation masks.
General OCR model	Detects and recognizes text.
Foreground segmentation model	Segments human figures from short videos and live streams.
Scenario classification model	Recognizes various indoor and outdoor scenarios, such as skies, beaches, blue skies, kitchens, and concert halls, in images.
Inventory counting model	Returns the categories of recognized commodities in images, the coordinates of the bounding boxes that mark recognized commodities, and the number of recognized commodities in each category.
Image similarity comparison model	Returns the similarity between two images. This model can be used for image comparison and retrieval.
Image coloring model	Returns the colored image.

Go to Model Hub

To go to Model Hub, perform the following steps:

Log on to the PAI console.
In the left-side navigation pane, choose AI Computing Asset Management > Model Management.
On the Model Management page, click the Model Hub tab.

General image classification model

Overview
The general image classification model is trained by using the ImageNet dataset. This model returns the categories of recognized objects. The general image classification model uses the ResNet framework. For more information, see Deep Residual Learning for Image Recognition.
Input format
The input data must be in the JSON format. It contains the image field. The value of this field is the image content that is encoded in the Base64 format.
```
{
  "image": "Base64-encoded image content"
}
```

Output format

The output data is in the JSON format. The following table describes the fields in the output data.


Parameter	Description	Shape	Type
class	The ID of the category.	[]	INT32
class_name	The name of the category.	[]	STRING
class_probs	The probabilities for all categories.	[num_classes]	Dict[STRING, FLOAT]
request_id	The unique ID of the request.	[]	STRING
success	Indicates whether the request was successful.	[]	BOOL
error_code	The error code that is returned if the request failed.	[]	INT
error_msg	The error message that is returned if the request failed.	[]	STRING

The following code provides an example of the output data:

{
"class": 3,
"class_name": "coho4",
"class_probs": {"coho1": 4.028851974258174e-10,
          "coho2": 0.48115724325180054,
          "coho3": 5.116515922054532e-07,
          "coho4": 0.5188422446937221},
 "request_id": "9ac294a4-f387-4c48-b640-d2c6d41f****",
 "success": true
}

Test data
Download the data to test the general image classification model.

General image recognition model

Overview
The general image recognition model uses the Faster R-CNN framework. This model returns the categories and positions of recognized objects in images. For more information, see Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. The general image recognition model is trained by using the COCO dataset.
Input format
The input data must be in the JSON format. It contains the image field. The value of this field is the image content that is encoded in the Base64 format.
```
{
  "image": "Base64-encoded image content"
}
```

Output format

The output data is in the JSON format. The following table describes the fields in the output data.


Parameter	Description	Shape	Type
detection_boxes	The bounding boxes that mark the recognized objects. The coordinates of each bounding box [y1, x1, y2, x2] are specified in the [top, left, bottom, right] order.	[num_detections, 4]	FLOAT
detection_scores	The probabilities that the objects are recognized.	num_detections	FLOAT
detection_classes	The IDs of the categories to which the objects belong.	num_detections	INT
detection_class_names	The names of the categories to which the objects belong.	num_detections	STRING
request_id	The unique ID of the request.	[]	STRING
success	Indicates whether the request was successful.	[]	BOOL
error_code	The error code that is returned if the request failed.	[]	INT
error_msg	The error message that is returned if the request failed.	[]	STRING

The following code provides an example of the output data:

{
  "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"],
  "request_id": "9ac294a4-f387-4c48-b640-d2c6d41f****",
  "success": true
 }

Semantic segmentation model

Overview
The semantic segmentation model uses the DeepLab V3 framework. This model returns the categories of segmented objects and the information about segmentation masks. For more information, see Rethinking Atrous Convolution for Semantic Image Segmentation. The semantic segmentation model is trained by using the Pascal_Voc dataset.
Input format
The input data must be in the JSON format. It contains the image field. The value of this field is the image content that is encoded in the Base64 format.
```
{
  "image": "Base64-encoded image content"
}
```

Output format

The output data is in the JSON format. The following table describes the fields in the output data.


Parameter	Description	Shape	Type
probs	The probabilities that the pixels obtained after segmentation belong to specific categories.	[output_height, output_width]	FLOAT
preds	The IDs of the categories to which the pixels obtained after segmentation belong.	[output_height, output_widths]	INT
request_id	The unique ID of the request.	[]	STRING
success	Indicates whether the request was successful.	[]	BOOL
error_code	The error code that is returned if the request failed.	[]	INT
error_msg	The error message that is returned if the request failed.	[]	STRING

The following code provides an example of the output data:

{
  "probs" : [[[0.8, 0.8], [0.6, 0.7]],[[0.8, 0.5], [0.4, 0.3]]],
  "preds" : [[1,1], [0, 0]],
   "request_id": "9ac294a4-f387-4c48-b640-d2c6d41f****",
   "success": true
}

Test data
Download the data to test the semantic segmentation model.

Instance segmentation model

Overview
The instance segmentation model uses the Mask R-CNN framework. This model returns the categories and positions of recognized objects, and the information about segmentation masks. For more information, see Mask R-CNN. The instance segmentation model is trained by using the COCO dataset.
Input format
The input data must be in the JSON format. It contains the image field. The value of this field is the image content that is encoded in the Base64 format.
```
{
  "image": "Base64-encoded image content"
}
```

Output format

The output data is in the JSON format. The following table describes the fields in the output data.


Parameter	Description	Shape	Type
detection_boxes	The bounding boxes that mark the recognized objects. The coordinates of each bounding box [y1, x1, y2, x2] are specified in the [top, left, bottom, right] order.	[num_detections, 4]	FLOAT
detection_scores	The probabilities that the objects are recognized.	num_detections	FLOAT
detection_classes	The IDs of the categories to which the objects belong.	num_detections	INT
detection_class_names	The names of the categories to which the objects belong.	num_detections	STRING
detection_masks	The segmentation masks for the objects. The value of this field is the segmentation mask data that is encoded in the run-length encoding (RLE) format. Each segmentation mask contains the following two properties: size: the height and width of the mask image. counts: the RLE data of the mask. An odd term indicates the counts of consecutive False values. An even term indicates the counts of consecutive True values. For example, the RLE-encoded data of [True False, False, True] is [0,1,2,1]. After RLE data is decoded, you can reshape the mask data to a two-dimensional mask based on the size property.	[num_detections]	DICT
request_id	The unique ID of the request.	[]	STRING
success	Indicates whether the request was successful.	[]	BOOL
error_code	The error code that is returned if the request failed.	[]	INT
error_msg	The error message that is returned if the request failed.	[]	STRING

The following code provides an example of the output data:

{
  "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"],
  "detection_masks": [{"counts": [398408, 11, 671, 30, 652, 44, 636], "size":[640, 480]},
                      {"counts": [398408, 11, 671, 30, 652, 44, 636], "size":[640, 480]}],
   "request_id": "9ac294a4-f387-4c48-b640-d2c6d41f****",
   "success": true
 }

General OCR model

Overview
The general optical character recognition (OCR) model uses the end-to-end OCR model that is developed by PAI. This model can detect and recognize text.
Input format
The input data must be in the JSON format. It contains the image field. The value of this field is the image content that is encoded in the Base64 format.
```
{
  "image": "Base64-encoded image content"
}
```

Output format

The output data is in the JSON format. The following table describes the fields in the output data.


Parameter	Description	Shape	Type
detection_boxes	The bounding boxes that mark the recognized text areas. The coordinates of each bounding box are specified in the [top, left, bottom, right] order.	[num_detections, 4]	FLOAT
detection_scores	The probabilities that the text areas are detected.	num_detections	FLOAT
detection_classes	The IDs of the categories to which the text areas belong.	num_detections	INT
detection_class_names	The names of the categories to which the text areas belong.	num_detections	STRING
detection_keypoints	The four vertices of each text area that is detected. The coordinates of each vertex are specified in the (y, x) format.	[num_detections, 4, 2]	float
detection_texts_ids	The ID of the category to which a single line of the recognized text belongs.	[num_detections, max_text_length]	INT
detection_texts	The recognition result of each single-line text.	[num_detections]	STRING
detection_texts_scores	The probability that each single-line text is recognized.	[num_detections]	FLOAT
request_id	The unique ID of the request.	[]	STRING
success	Indicates whether the request was successful.	[]	BOOL
error_code	The error code that is returned if the request failed.	[]	INT
error_msg	The error message that is returned if the request failed.	[]	STRING

The following code provides an example of the output data:

 {
  "detection_keypoints": [[[243.57516479492188, 198.84210205078125], [243.91038513183594, 247.62425231933594], [385.5513916015625, 246.61660766601562], [385.2197570800781, 197.79345703125]], [[292.2718200683594, 114.44700622558594], [292.2237243652344, 164.684814453125], [571.1962890625, 164.931640625], [571.2444458007812, 114.67433166503906]]], 
  "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"],
  "detection_texts_ids" : [[1,2,2008,12], [1,2,2008,12]],
  "detection_texts": ["This is an example", "This is an example"],
  "detection_texts_scores" : [0.88, 0.88],
  "request_id": "9ac294a4-f387-4c48-b640-d2c6d41f****",
  "success": true
 }

Test data
Download the data to test the general OCR model.

Foreground segmentation model

Overview
The foreground segmentation model uses the MobileNet framework. This model can segment human figures from short videos and live streams.
Input format
The input data must be in the JSON format. It contains the url field that specifies the URL of the image. The following code provides an example of the input data:
```
{
  "input": {
    "url": "Image URL"
  }
}
```

Output format

The output data is in the JSON format. The following table describes the fields in the output data.


Parameter	Description	Shape	Type
human_ratio	The ratio of the pixels of the foreground to the pixels of the image.	[]	STRING
mask	The segmentation mask for the foreground.	[h,w]	LIST

Example
The following code provides an example of the input data of the model:
```
{"input" : {"url": "http://yq****.oss-cn-hangzhou-zmf.aliyuncs.com/tb_quality.png"}}
```
For more information about the test result, see Sample result.

Scenario classification model

Overview
The scenario classification model uses the ResNet framework. For more information, see Deep Residual Learning for Image Recognition. This model can recognize various indoor and outdoor scenarios, such as skies, beaches, blue skies, kitchens, and concert halls, in images.
Input format
The input data must be in the JSON format. It contains the image field. The value of this field is the image content that is encoded in the Base64 format.
```
{
  "image": "Base64-encoded image content"
}
```

Output format

The output data is in the JSON format. The following table describes the fields in the output data.


Parameter	Description	Shape	Type
class	The IDs of the top five categories.	5	INT32
class_name	The names of the top five categories.	5	STRING
class_probs	The probabilities for all categories.	[num_classes]	Dict[STRING, FLOAT]
request_id	The unique ID of the request.	[]	STRING
success	Indicates whether the request was successful. Valid values: true: indicates that the request was successful. false: indicates that the request failed.	[]	BOOL
error_code	The error code that is returned if the request failed.	[]	INT
error_msg	The error message that is returned if the request failed.	[]	STRING

The following code provides an example of the output data:

{
    "request_id": "a72304d8-cf84-479e-b29e-5e341e3d****", 
    "success": true, 
    "class": [266, 57, 159,  260,  243], 
    "class_name": [
        "pier", 
        "boardwalk", 
        "gazebo-exterior", 
        "pavilion", 
        "ocean"
    ], 
    "class_probs": {
        "airfield": 2.6841306066671677e-8, 
        "airplane_cabin": 2.4176902702066627e-9, 
        "airport_terminal": 1.3229835360561992e-7, 
        "alcove": 1.5998873337252917e-8, 
        "alley": 7.053529316181084e-8, 
        "amphitheater": 2.0278820400676523e-8, 
        "amusement_arcade": 1.5128257757623942e-8, 
        "amusement_park": 6.29929459705636e-8, 
        "apartment_building-outdoor": 1.846876926947516e-7, 
        "aquarium": 6.034031940771456e-8, 
        "aqueduct": 1.6192875307297072e-7, 
        "arcade": 3.719276833180629e-7, 
        "arch": 0.000001615617293282412, 
        "archaelogical_excavation": 1.9157377906253714e-9, 
        "archive": 1.915566905097421e-8
    }
}

Inventory counting model

Overview

The inventory counting model integrates the YOLOv5 model and fine-grained classification model in two different phases. This model returns the categories of recognized commodities in images, the coordinates of the bounding boxes that mark recognized commodities, and the number of recognized commodities in each category. This model supports 171 regular categories of bottled drinks, each category identified by a unique stock keeping unit (SKU). The following information shows the complete list.

CLASSES = ['189', '772', '773', '307', '306', '305', '304', '303', '757', '342', '343', '344', '5', '346', '347', '348', '349', '438', '439', '440', '441', '341', '460', '457', '764', '459', '469', '329', '331', '332', '333', '334', '762', '763', '337', '338', '340', '471', '470', '336', '335', '767', '770', '769', '758', '455', '456', '454', '446', '775', '761', '778', '777', '779', '789', '780', '453', '452', '451', '450', '449', '448', '444', '447', '445', '472', '468', '190', '759', '195', '196', '301', '300', '188', '186', '185', '184', '183', '182', '181', '308', '309', '310', '311', '734', '312', '313', '355', '339', '774', '791', '180', '187', '198', '197', '368', '369', '299', '298', '374', '372', '373', '371', '370', '350', '351', '352', '353', '755', '754', '429', '432', '431', '753', '433', '434', '435', '436', '437', '718', '717', '716', '715', '714', '712', '430', '354', '200', '6', '476', '477', '478', '479', '713', '474', '473', '768', '443', '442', '321', '315', '318', '316', '322', '317', '319', '179', '320', '324', '323', '325', '326', '327', '328', '330', '458', '776', '765', '766', '4', '461', '199', '462', '464', '466', '467', '756', '465', '463', '719', '4345']

Input format
The input data must be in the JSON format. It contains the image field. The value of this field is the image content that is encoded in the Base64 format.
```
{
  "image": "Base64-encoded image content"
}
```

Output format

The output data is in the JSON format. The following table describes the fields in the output data.


Parameter	Description	Shape	Type
detection_boxes	The bounding boxes that mark the recognized objects. The coordinates of each bounding box [y1, x1, y2, x2] are specified in the [top, left, bottom, right] order.	[num_detections, 4]	FLOAT
detection_scores	The probabilities that the commodities are recognized.	num_detections	FLOAT
detection_classes	The IDs of the categories to which the commodities belong.	num_detections	INT
detection_class_names	The names of the categories to which the commodities belong.	num_detections	STRING
product_count	The number of recognized commodities in each category.	num_classes	DICT/INT
request_id	The unique ID of the request.	[]	STRING
success	Indicates whether the request was successful.	[]	BOOL
error_code	The error code that is returned if the request failed.	[]	INT
error_msg	The error message that is returned if the request failed.	[]	STRING

The following code provides an example of the output data:

{
    "request_id": "c3e8572d-95fc-479c-8fa9-bfe5b9be****", 
    "success": true, 
    "ori_img_shape": [1280, 1706], 
    "detection_boxes": [[1630.1121826171875, 548.7086791992188, 1702.461181640625, 763.6509399414062], [1620.415283203125, 94.60894775390625, 1682.6427001953125, 233.49900817871094], [1553.8291015625, 98.07244110107422, 1617.0072021484375, 235.3670196533203], [1172.0789794921875, 777.32861328125, 1226.4949951171875, 959.086669921875], [772.0833129882812, 758.675048828125, 825.0681762695312, 913.5953369140625], [828.8756713867188, 760.6256713867188, 882.7286376953125, 920.3554077148438], [987.754150390625, 1031.87841796875, 1044.6632080078125, 1207.215576171875], [1111.7740478515625, 772.5474243164062, 1167.0968017578125, 951.8721923828125], [886.6332397460938, 765.0888061523438, 938.5805053710938, 927.5157470703125], [1345.31689453125, 1060.8140869140625, 1406.5404052734375, 1269.9930419921875], [1371.146728515625, 112.70679473876953, 1427.5396728515625, 245.45986938476562], [1560.9510498046875, 545.3655395507812, 1626.23876953125, 761.7933349609375], [1197.1800537109375, 123.90782165527344, 1249.66357421875, 253.5529022216797], [1310.7371826171875, 115.76203918457031, 1367.01904296875, 248.2571563720703], [1107.890380859375, 1023.4066772460938, 1164.3438720703125, 1228.36865234375], [942.4257202148438, 771.8615112304688, 998.5577392578125, 934.6956176757812], [1002.333984375, 777.1546020507812, 1060.7852783203125, 941.6340942382812], [1493.2554931640625, 544.4757080078125, 1557.4088134765625, 657.531005859375], [631.6664428710938, 322.0820007324219, 678.197265625, 486.9432067871094], [1407.86865234375, 363.3863220214844, 1468.095458984375, 498.1813049316406], [653.595458984375, 970.340087890625, 703.8126220703125, 1146.232666015625], [1173.056884765625, 538.8171997070312, 1228.7896728515625, 644.2966918945312], [585.7047119140625, 321.9823913574219, 628.947021484375, 487.156982421875], [891.7557983398438, 336.12506103515625, 947.5447387695312, 494.72930908203125], [1179.0208740234375, 368.91839599609375, 1231.1116943359375, 497.6968078613281], 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}

Image similarity comparison model

Overview
The image similarity comparison model is developed based on the ResNet50 model and returns the similarity between two images. This model can be used for image comparison and retrieval.
Input format
The input data must be in the JSON format. It contains the imagea and imageb fields. The value of each field is the image content that is encoded in the Base64 format.
```
{
  "imagea": "Base64-encoded image content",
  "imageb": "Base64-encoded image content"
}
```

Output format

The output data is in the JSON format. The following table describes the fields in the output data.


Parameter	Description	Shape	Type
similarity	The similarity between the two images identified by the imagea and imageb fields. A value of 100 indicates that the two images are the same. A value smaller than 80 indicates that the two images are different.	[]	FLOAT
l2_distance	The distance between the feature vectors of the two images. The larger the value is, the less similarity the two images share.	[]	FLOAT
request_id	The unique ID of the request.	[]	STRING
success	Indicates whether the request was successful. Valid values: true: indicates that the request was successful. false: indicates that the request failed.	[]	BOOL
error_code	The error code that is returned if the request failed.	[]	INT
error_msg	The error message that is returned if the request failed.	[]	STRING

The following code provides an example of the output data:

{
  "request_id": "d4e4348a-6101-43d1-9203-dbe8f531****", 
  "success": true, 
  "similarity": [1.0],
  "l2_distance":[0.0]
 }

Test data
- Image A
- Image B

Image coloring model

Overview
The image coloring model is developed based on the NoGAN model and returns the colored image.
Input format
The input data must be in the JSON format. It contains the image field. The value of this field is the image content that is encoded in the Base64 format.
```
{
  "image": "Base64-encoded image content"
}
```

Output format

The output data is in the JSON format. The following table describes the fields in the output data.


Parameter	Description	Shape	Type
out_image	The image to be colored.	[]	BASE 64
request_id	The unique ID of the request.	[]	STRING
success	Indicates whether the request was successful.	[]	BOOL
error_code	The error code that is returned if the request failed.	[]	INT
error_msg	The error message that is returned if the request failed.	[]	STRING

The following code provides an example of the output data:

{
   "out_image": "Base64-encoded image content"
   "request_id": "9ac294a4-f387-4c48-b640-d2c6d41f****",
   "success": true
}