在ModelHub中，PAI提供多种已经训练好的图像智能类模型供您使用，本文为您介绍每种模型的输入格式、输出格式及使用示例。
PAI提供以下训练好的成熟图像智能处理类模型，帮助您快速触达业务：
| 模型 | 功能 |
|---|---|
| 通用图像分类模型 | 识别常见物体的类别。 |
| 通用图像检测模型 | 识别常见物体的类别并返回位置信息。 |
| 图像语义分割模型 | 能够输出分割物体的类别和分割的Mask信息。 |
| 图像实例分割模型 | 能够输出常见物体的类别信息、位置信息及Mask信息。 |
| 通用OCR模型 | 能够进行文本检测、文本识别。 |
| 前景分割模型 | 针对短视频、直播等场景，进行人物分割。 |
| 场景分类模型 | 识别图片内容反映的各种室内外场景。例如，天空、沙滩、蓝天、厨房及音乐厅。 |
| 货架商品计数模型 | 能够返回常见物体的类别信息、检测框位置及图像中每类商品的计数。 |
| 通用图片相似度比对模型 | 能够返回两张输入图片的相似度，适用于图片比对和检索场景。 |
| 图片上色模型 | 能够返回上色后的彩色图片。 |
ModelHub登录入口
您可以通过如下方法进入ModelHub：
- 登录PAI控制台。
- 在左侧导航栏，选择大数据与AI体验 > ModelHub。
通用图像分类模型
- 模型介绍
基于ImageNet数据集训练的图像分类模型，能够识别常见物体的类别。该模型采用ResNet，详情请参见Deep Residual Learning for Image Recognition。
- 输入格式
输入数据为JSON格式字符串，包含image字段，对应的value为图片内容的Base 64编码。{ "image": "图像文件内容的Base 64编码" } - 输出格式
输出数据为JSON格式字符串，包含的字段如下表所示。字段 描述 Shape Type class 类别ID [] INT32 class_name 类别名称 [] STRING class_probs 所有类别概率 [num_classes] Dict[STRING, FLOAT] request_id 请求的唯一标识 [] STRING success 请求是否成功 [] BOOL error_code 请求错误码 [] INT error_msg 请求错误信息 [] STRING 输出数据的示例如下。
{ "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 } - 测试数据
下载通用图像分类模型的测试数据
通用图像检测模型
- 模型介绍
该模型能够识别常见物体的类别并返回位置信息，采用Faster R-CNN，详情请参见Towards Real-Time Object Detection with Region Proposal Networks。模型的训练数据集为COCO。
- 输入格式
输入数据为JSON格式字符串，包含image字段，对应的value为图片内容的Base 64编码。{ "image": "图像文件内容的Base 64编码" } - 输出格式
输出数据为JSON格式字符串，包含的字段如下表所示。字段 描述 Shape Type detection_boxes 检测到的目标框[y1, x1, y2,x2]，其坐标顺序为[top, left, bottom, right]。 [num_detections, 4] FLOAT detection_scores 目标检测概率。 num_detections FLOAT detection_classes 目标区域类别ID。 num_detections INT detection_class_names 目标区域类别名称。 num_detections STRING request_id 请求的唯一标识。 [] STRING success 请求是否成功。 [] BOOL error_code 请求错误码。 [] INT error_msg 请求错误信息。 [] STRING 输出数据的示例如下。
{ "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 }
图像语义分割模型
- 模型介绍
该模型能够识别分割物体的类别和分割的Mask信息，采用DeepLab V3，详情请参见Rethinking Atrous Convolution for Semantic Image Segmentation。模型的训练数据集为Pascal_Voc。
- 输入格式
输入数据为JSON格式字符串，包含image字段，对应的value为图片内容的Base 64编码。{ "image": "图像文件内容的Base 64编码" } - 输出格式
输出数据为JSON格式字符串，包含的字段如下表所示。字段 描述 Shape Type probs 分割像素点概率 [output_height, output_width] FLOAT preds 分割像素类别ID [output_height, output_widths] INT request_id 请求的唯一标识 [] STRING success 请求是否成功 [] BOOL error_code 请求错误码 [] INT error_msg 请求错误信息 [] STRING 输出数据的示例如下。
{ "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 } - 测试数据
下载图像语义分割模型的测试数据
图像实例分割模型
- 模型介绍
该模型能够识别常见物体的类别信息、位置信息及Mask信息，采用Mask R-CNN，详情请参见Mask R-CNN。模型的训练数据集为COCO。
- 输入格式
输入数据为JSON格式字符串，包含image字段，对应的value为图片内容的Base 64编码。{ "image": "图像文件内容的Base 64编码" } - 输出格式
输出数据为JSON格式字符串，包含的字段如下表所示。字段 描述 Shape Type detection_boxes 检测到的目标框[y1, x1, y2,x2]，其坐标顺序为[top, left, bottom, right]。 [num_detections, 4] FLOAT detection_scores 目标检测概率。 num_detections FLOAT detection_classes 目标区域类别ID。 num_detections INT detection_class_names 目标区域类别名称。 num_detections STRING detection_masks 目标区域的Mask。采用RLE编码，每个Mask对应如下两个属性： - size：对应Mask图像的[height, width]。
- counts：对应Mask的RLE编码数据。奇数位表示连续为False的个数，偶数位表示连续为True的个数。例如，[True False, False, True]的编码为[0,1,2,1]。 解码RLE数据后，可以根据size把Mask数据序列Reshape成一个Mask二维掩码。
[num_detections] DICT request_id 请求的唯一标识。 [] STRING success 请求是否成功。 [] BOOL error_code 请求错误码。 [] INT error_msg 请求错误信息。 [] STRING 输出数据的示例如下。
{ "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 }
通用OCR模型
- 模型介绍
该模型采用PAI自研的端到端OCR模型，能够进行文本检测、文本识别。
- 输入格式
输入数据为JSON格式字符串，包含image字段，对应的value为图片内容的Base 64编码。{ "image": "图像文件内容的Base 64编码" } - 输出格式
输出数据为JSON格式字符串，包含的字段如下表所示。字段 描述 Shape Type detection_boxes 检测到的文字框，坐标顺序 [top, left, bottom, right]。 [num_detections, 4] FLOAT detection_scores 文字检测概率。 num_detections FLOAT detection_classes 文字区域类别ID。 num_detections INT detection_class_names 文字区域类别名称。 num_detections STRING detection_keypoints 检测到的文字区域四个角点，每个点的坐标为(y,x) [num_detections, 4, 2] float detection_texts_ids 单行文字识别类别ID。 [num_detections, max_text_length] INT detection_texts 单行文字识别结果。 [num_detections] STRING detection_texts_scores 单行文字识别概率。 [num_detections] FLOAT request_id 请求的唯一标识。 [] STRING success 请求是否成功。 [] BOOL error_code 请求错误码。 [] INT error_msg 请求错误信息。 [] STRING 输出数据的示例如下。
{ "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": ["这是示例", "这是示例"], "detection_texts_scores" : [0.88, 0.88], "request_id": "9ac294a4-f387-4c48-b640-d2c6d41f****", "success": true } - 测试数据
下载通用OCR模型的测试数据
前景分割模型
- 模型介绍
基于MobileNet训练的前景分割模型。针对短视频、直播等场景，进行人物分割。
- 输入格式
输入数据是JSON格式字符串，包含图片的url字段。具体格式如下。{ "input": { "url": "输入图片的URL" } } - 输出格式
输出数据为JSON格式字符串，包含的字段如下表所示。字段 描述 Shape Type human_ratio 前景占的比例 [] STRING mask 前景Mask [h,w] LIST - 示例
例如，为该模型输入如下测试数据。{"input" : {"url": "http://yq****.oss-cn-hangzhou-zmf.aliyuncs.com/tb_quality.png"}}系统输出的结果详见结果样例。
场景分类模型
- 模型介绍
该模型采用ResNet结构，详情请参见Deep Residual Learning for Image Recognition。该模型能够识别图片内容反映的各种室内外场景，例如天空、沙滩、蓝天、厨房及音乐厅等。
- 输入格式
输入数据为JSON格式字符串，包含image字段，对应的value为图片内容的Base 64编码。{ "image": "图像文件内容的Base 64编码" } - 输出格式
输出数据为JSON格式字符串，包含的字段如下表所示。字段 描述 Shape Type class Top 5类别ID。 5 INT32 class_name Top 5类别名称。 5 STRING class_probs 所有类别的概率。 [num_classes] Dict[STRING, FLOAT] request_id 请求的唯一标识。 [] STRING success 请求是否成功，包括如下取值： - true：请求成功。
- false：请求失败。
[] BOOL error_code 请求失败时，返回的错误码。 [] INT error_msg 请求失败时，返回的错误信息。 [] STRING 输出数据的示例如下。
{ "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 } }
货架商品计数模型
- 模型介绍
该模型采用YoloV5检测模型和细粒度分类模型两阶段串联，能够返回常见物体的类别信息、检测框位置及图像中每类商品的计数。目前，该模型共支持171种不同的瓶饮SKU类别，类别名称列表如下所示。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'] - 输入格式
输入数据为JSON格式字符串，包含image字段，对应的value为图片内容的Base 64编码。{ "image": "图像文件内容的Base 64编码" } - 输出格式
输出数据为JSON格式字符，包含的字段如下表所示。字段 描述 Shape Type detection_boxes 检测到的目标框[y1, x1, y2,x2]，其坐标顺序为[top, left, bottom, right]。 [num_detections, 4] FLOAT detection_scores 目标检测概率。 num_detections FLOAT detection_classes 目标区域类别ID。 num_detections INT detection_class_names 目标区域类别名称。 num_detections STRING product_count 商品计数。 num_classes DICT/INT request_id 请求的唯一标识。 [] STRING success 请求是否成功。 [] BOOL error_code 请求错误码。 [] INT error_msg 请求错误信息。 [] STRING 输出数据的示例如下。
{ "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, 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"368": 16, "370": 1, "714": 4, "434": 3, "354": 6, "301": 4, "372": 3, "479": 3, "755": 3, "757": 6, "299": 4, "374": 4, "350": 4, "200": 3, "4": 19, "477": 2, "440": 2, "438": 6, "713": 2, "351": 3, "439": 3, "348": 4, "754": 5, "349": 3, "432": 5, "347": 1, "352": 4, "437": 2, "431": 2, "298": 2, "303": 4, "478": 1, "305": 4, "476": 3, "304": 3, "306": 2, "346": 2, "753": 2, "353": 2, "441": 6, "6": 4, "342": 1, "196": 5, "373": 1, "5": 2, "343": 2, "716": 2, "473": 2, "181": 2, "313": 4, "344": 5, "443": 1, "768": 2, "717": 1, "195": 6, "198": 4, "190": 2, "759": 1, "197": 1, "474": 2, "773": 1, "300": 2, "772": 3, "442": 2, "472": 1, "371": 1} }
通用图片相似度比对模型
- 模型介绍
模型采用ResNet50，可以返回两张输入图片的相似度，适用于图片比对和检索场景。
- 输入格式
输入数据为JSON格式字符串，包含imagea字段和imageb字段，对应的value为图片内容的Base 64编码。{ "imagea": "图像文件内容的Base 64编码", "imageb": "图像文件内容的Base 64编码" } - 输出格式
输出数据为JSON格式字符串，包含的字段如下表所示。字段 描述 Shape Type similarity 输入的imagea和imageb两张图像的相似度。取值为100表示二者为同一张图片，取值小于80表示二者不是同一张图像。 [] FLOAT l2_distance 图片在特征空间的距离，取值越大表示越不相似。 [] FLOAT request_id 请求的唯一标识。 [] STRING success 请求是否成功，支持以下取值： - true：请求成功。
- false：请求失败。
[] BOOL error_code 请求失败时，返回的错误码。 [] INT error_msg 请求失败时，返回的错误信息。 [] STRING 输出数据的示例如下。
{ "request_id": "d4e4348a-6101-43d1-9203-dbe8f531****", "success": true, "similarity": [1.0], "l2_distance":[0.0] } - 测试数据
- 下载样本A
- 下载样本B
图片上色模型
- 模型介绍
该模型采用NoGAN模型，能够返回上色后的彩色图片。
- 输入格式
输入数据为JSON格式字符串，包含image字段，对应的value为图片内容的Base 64编码。{ "image": "图像文件内容的Base 64编码" } - 输出格式
输出数据为JSON格式字符，包含的字段如下表所示。字段 描述 Shape Type out_image 待上色的图像 [] BASE 64 request_id 请求的唯一标识 [] STRING success 请求是否成功 [] BOOL error_code 请求错误码 [] INT error_msg 请求错误信息 [] STRING 输出数据的示例如下。
{ "out_image": "图像文件内容的Base 64编码" "request_id": "9ac294a4-f387-4c48-b640-d2c6d41f****", "success": true }
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