Predict

Predict

The Predict API analyzes your images or videos and tells you what's inside of them.

The API will return a list of concepts with corresponding probabilities of how likely it is these concepts are contained within the image or video.

When you make a prediction through the API, you tell it what model to use. This can be one Clarifai's pre-built models or a custom one trained by you. A model contains a group of concepts. A model will only 'see' the concepts it contains. You can also specify more parameters for predictions.

We recommend specifying the version id parameter in your predict calls. If no version id is specified, predictions will occur on the most recent version of the model. More information can be found in the Advanced Predictions section.

Images

Via URL

To get predictions for an input, you need to supply an image and the model you'd like to get predictions from. You can supply an image either with a publicly accessible URL or by directly sending bytes. You can send up to 128 images in one API call. You specify the model you'd like to use with the {model-id} parameter.

Below is an example of how you would send image URLs and receive back predictions from the general model.

You can learn all about the different public models available later in the guide.

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app.models.initModel({id: Clarifai.GENERAL_MODEL, version: "aa7f35c01e0642fda5cf400f543e7c40"})
.then(generalModel => {
return generalModel.predict("@@sampleTrain");
})
.then(response => {
var concepts = response['outputs'][0]['data']['concepts']
})
{
"status": {
"code": 10000,
"description": "Ok"
},
"outputs": [
{
"id": "ea68cac87c304b28a8046557062f34a0",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2016-11-22T16:50:25Z",
"model": {
"name": "general-v1.3",
"id": "aaa03c23b3724a16a56b629203edc62c",
"created_at": "2016-03-09T17:11:39Z",
"app_id": null,
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "concept"
},
"model_version": {
"id": "aa9ca48295b37401f8af92ad1af0d91d",
"created_at": "2016-07-13T01:19:12Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
}
}
},
"input": {
"id": "ea68cac87c304b28a8046557062f34a0",
"data": {
"image": {
"url": "https://samples.clarifai.com/metro-north.jpg"
}
}
},
"data": {
"concepts": [
{
"id": "ai_HLmqFqBf",
"name": "train",
"app_id": null,
"value": 0.9989112
},
{
"id": "ai_fvlBqXZR",
"name": "railway",
"app_id": null,
"value": 0.9975532
},
{
"id": "ai_Xxjc3MhT",
"name": "transportation system",
"app_id": null,
"value": 0.9959158
},
{
"id": "ai_6kTjGfF6",
"name": "station",
"app_id": null,
"value": 0.992573
},
{
"id": "ai_RRXLczch",
"name": "locomotive",
"app_id": null,
"value": 0.992556
},
{
"id": "ai_VRmbGVWh",
"name": "travel",
"app_id": null,
"value": 0.98789215
},
{
"id": "ai_SHNDcmJ3",
"name": "subway system",
"app_id": null,
"value": 0.9816359
},
{
"id": "ai_jlb9q33b",
"name": "commuter",
"app_id": null,
"value": 0.9712483
},
{
"id": "ai_46lGZ4Gm",
"name": "railroad track",
"app_id": null,
"value": 0.9690325
},
{
"id": "ai_tr0MBp64",
"name": "traffic",
"app_id": null,
"value": 0.9687052
},
{
"id": "ai_l4WckcJN",
"name": "blur",
"app_id": null,
"value": 0.9667078
},
{
"id": "ai_2gkfMDsM",
"name": "platform",
"app_id": null,
"value": 0.9624243
},
{
"id": "ai_CpFBRWzD",
"name": "urban",
"app_id": null,
"value": 0.960752
},
{
"id": "ai_786Zr311",
"name": "no person",
"app_id": null,
"value": 0.95864904
},
{
"id": "ai_6lhccv44",
"name": "business",
"app_id": null,
"value": 0.95720303
},
{
"id": "ai_971KsJkn",
"name": "track",
"app_id": null,
"value": 0.9494642
},
{
"id": "ai_WBQfVV0p",
"name": "city",
"app_id": null,
"value": 0.94089437
},
{
"id": "ai_dSCKh8xv",
"name": "fast",
"app_id": null,
"value": 0.9399334
},
{
"id": "ai_TZ3C79C6",
"name": "road",
"app_id": null,
"value": 0.93121606
},
{
"id": "ai_VSVscs9k",
"name": "terminal",
"app_id": null,
"value": 0.9230834
}
]
}
}
]
}

Via Bytes

Below is an example of how you would send the bytes of an image and receive back predictions from the general model.

text
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python
java
csharp
text
php
text
text
{
"status": {
"code": 10000,
"description": "Ok"
},
"outputs": [
{
"id": "e1cf385843b94c6791bbd9f2654db5c0",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2016-11-22T16:59:23Z",
"model": {
"name": "general-v1.3",
"id": "aaa03c23b3724a16a56b629203edc62c",
"created_at": "2016-03-09T17:11:39Z",
"app_id": null,
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "concept"
},
"model_version": {
"id": "aa9ca48295b37401f8af92ad1af0d91d",
"created_at": "2016-07-13T01:19:12Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
}
}
},
"input": {
"id": "e1cf385843b94c6791bbd9f2654db5c0",
"data": {
"image": {
"url": "https://s3.amazonaws.com/clarifai-api/img/prod/b749af061d564b829fb816215f6dc832/e11c81745d6d42a78ef712236023df1c.jpeg"
}
}
},
"data": {
"concepts": [
{
"id": "ai_l4WckcJN",
"name": "blur",
"app_id": null,
"value": 0.9973569
},
{
"id": "ai_786Zr311",
"name": "no person",
"app_id": null,
"value": 0.98865616
},
{
"id": "ai_JBPqff8z",
"name": "art",
"app_id": null,
"value": 0.986006
},
{
"id": "ai_5rD7vW4j",
"name": "wallpaper",
"app_id": null,
"value": 0.9722556
},
{
"id": "ai_sTjX6dqC",
"name": "abstract",
"app_id": null,
"value": 0.96476805
},
{
"id": "ai_Dm5GLXnB",
"name": "illustration",
"app_id": null,
"value": 0.922542
},
{
"id": "ai_5xjvC0Tj",
"name": "background",
"app_id": null,
"value": 0.8775655
},
{
"id": "ai_tBcWlsCp",
"name": "nature",
"app_id": null,
"value": 0.87474406
},
{
"id": "ai_rJGvwlP0",
"name": "insubstantial",
"app_id": null,
"value": 0.8196385
},
{
"id": "ai_2Bh4VMrb",
"name": "artistic",
"app_id": null,
"value": 0.8142488
},
{
"id": "ai_mKzmkKDG",
"name": "Christmas",
"app_id": null,
"value": 0.7996079
},
{
"id": "ai_RQccV41p",
"name": "woman",
"app_id": null,
"value": 0.7955615
},
{
"id": "ai_20SCBBZ0",
"name": "vector",
"app_id": null,
"value": 0.7775099
},
{
"id": "ai_4sJLn6nX",
"name": "dark",
"app_id": null,
"value": 0.7715479
},
{
"id": "ai_5Kp5FMJw",
"name": "still life",
"app_id": null,
"value": 0.7657637
},
{
"id": "ai_LM64MDHs",
"name": "shining",
"app_id": null,
"value": 0.7542407
},
{
"id": "ai_swtdphX8",
"name": "love",
"app_id": null,
"value": 0.74926054
},
{
"id": "ai_h45ZTxZl",
"name": "square",
"app_id": null,
"value": 0.7449074
},
{
"id": "ai_cMfj16kJ",
"name": "design",
"app_id": null,
"value": 0.73926914
},
{
"id": "ai_LxrzLJmf",
"name": "bright",
"app_id": null,
"value": 0.73790145
}
]
}
}
]
}

Videos

With a video input, the Predict API response will return a list of predicted concepts for every frame of a video. Video is processed at 1 frame per second. This means you will receive a list of concepts for every second of your video.

You can run Predict on your video using a select number of public models. The models that are currently supported are: Apparel, Food, General, NSFW, Travel, and Wedding. You make an API call by providing the {model-id} parameter and your data parameter is video instead of image.

Video Limits

The Predict API has limits to the length and size it can support. A video, uploaded through URL, can be anywhere up to 80MB in size or 10mins in length. When a video is sent through by bytes, the Predict API can support 10MB in size.

If your video exceeds the limits, please follow our tutorial on how to break up a large video into smaller components, and send those into the Video API. Otherwise, the processing will time out and you will receive an error response.

Via URL

Below is an example of how you would send video URLs and receive back predictions from the general model.

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text
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text
text
{
"status": {
"code": 10000,
"description": "Ok"
},
"outputs": [
{
"id": "d8234da5d1f04ca8a2e13e34d51f9b85",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2017-06-28T14:58:41.835370141Z",
"model": {
"id": "aaa03c23b3724a16a56b629203edc62c",
"name": "general-v1.3",
"created_at": "2016-03-09T17:11:39.608845Z",
"app_id": "main",
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "concept",
"type_ext": "concept"
},
"model_version": {
"id": "aa9ca48295b37401f8af92ad1af0d91d",
"created_at": "2016-07-13T01:19:12.147644Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
}
}
},
"input": {
"id": "f0fc1a005f124d389da4d80823a3125b",
"data": {
"video": {
"url": "https://samples.clarifai.com/beer.mp4"
}
}
},
"data": {
"frames": [
{
"frame_info": {
"index": 0,
"time": 0
},
"data": {
"concepts": [
{
"id": "ai_zJx6RbxW",
"name": "drink",
"value": 0.98658466,
"app_id": "main"
},
{
"id": "ai_mCpQg89c",
"name": "glass",
"value": 0.97975093,
"app_id": "main"
},
{
"id": "ai_drK6ClJR",
"name": "alcohol",
"value": 0.9783862,
"app_id": "main"
},
{
"id": "ai_8XGJjH7R",
"name": "foam",
"value": 0.97157896,
"app_id": "main"
},
{
"id": "ai_TBlp0Pt3",
"name": "beer",
"value": 0.969543,
"app_id": "main"
},
{
"id": "ai_SsmKLB4z",
"name": "bar",
"value": 0.96628696,
"app_id": "main"
},
{
"id": "ai_5VHsZr8N",
"name": "liquid",
"value": 0.95581007,
"app_id": "main"
},
{
"id": "ai_Lq00FggW",
"name": "desktop",
"value": 0.92861253,
"app_id": "main"
},
{
"id": "ai_7vR9zv7l",
"name": "bubble",
"value": 0.9082134,
"app_id": "main"
},
{
"id": "ai_B3MXt5Ng",
"name": "refreshment",
"value": 0.9020835,
"app_id": "main"
},
{
"id": "ai_7Xg5SQRW",
"name": "luxury",
"value": 0.8990605,
"app_id": "main"
},
{
"id": "ai_786Zr311",
"name": "no person",
"value": 0.89708906,
"app_id": "main"
},
{
"id": "ai_3R5pJ6hB",
"name": "lager",
"value": 0.8938055,
"app_id": "main"
},
{
"id": "ai_7qwGxLch",
"name": "gold",
"value": 0.8892093,
"app_id": "main"
},
{
"id": "ai_wmbvr5TG",
"name": "celebration",
"value": 0.88606626,
"app_id": "main"
},
{
"id": "ai_4lvjn8qv",
"name": "closeup",
"value": 0.881963,
"app_id": "main"
},
{
"id": "ai_pkvDRSJ1",
"name": "mug",
"value": 0.8674431,
"app_id": "main"
},
{
"id": "ai_12dz73B9",
"name": "bottle",
"value": 0.86288416,
"app_id": "main"
},
{
"id": "ai_zFnPQdgB",
"name": "wood",
"value": 0.86252767,
"app_id": "main"
},
{
"id": "ai_8LWlDfFD",
"name": "table",
"value": 0.86069393,
"app_id": "main"
}
]
}
},
{
"frame_info": {
"index": 1,
"time": 1000
},
"data": {
"concepts": [
{
"id": "ai_zJx6RbxW",
"name": "drink",
"value": 0.98658466,
"app_id": "main"
},
{
"id": "ai_mCpQg89c",
"name": "glass",
"value": 0.97975093,
"app_id": "main"
},
{
"id": "ai_drK6ClJR",
"name": "alcohol",
"value": 0.9783862,
"app_id": "main"
},
{
"id": "ai_8XGJjH7R",
"name": "foam",
"value": 0.97157896,
"app_id": "main"
},
{
"id": "ai_TBlp0Pt3",
"name": "beer",
"value": 0.969543,
"app_id": "main"
},
{
"id": "ai_SsmKLB4z",
"name": "bar",
"value": 0.96628696,
"app_id": "main"
},
{
"id": "ai_5VHsZr8N",
"name": "liquid",
"value": 0.95581007,
"app_id": "main"
},
{
"id": "ai_7vR9zv7l",
"name": "bubble",
"value": 0.9082134,
"app_id": "main"
},
{
"id": "ai_B3MXt5Ng",
"name": "refreshment",
"value": 0.9020835,
"app_id": "main"
},
{
"id": "ai_786Zr311",
"name": "no person",
"value": 0.89708906,
"app_id": "main"
},
{
"id": "ai_3R5pJ6hB",
"name": "lager",
"value": 0.8938055,
"app_id": "main"
},
{
"id": "ai_7qwGxLch",
"name": "gold",
"value": 0.8834515,
"app_id": "main"
},
{
"id": "ai_pkvDRSJ1",
"name": "mug",
"value": 0.8674431,
"app_id": "main"
},
{
"id": "ai_b01mhdxB",
"name": "party",
"value": 0.8603341,
"app_id": "main"
},
{
"id": "ai_XNmzgDnF",
"name": "pub",
"value": 0.85809004,
"app_id": "main"
},
{
"id": "ai_2gmKZLxp",
"name": "cold",
"value": 0.85319245,
"app_id": "main"
},
{
"id": "ai_Lq00FggW",
"name": "desktop",
"value": 0.8506696,
"app_id": "main"
},
{
"id": "ai_54zxXFGL",
"name": "full",
"value": 0.84634554,
"app_id": "main"
},
{
"id": "ai_zFnPQdgB",
"name": "wood",
"value": 0.8446485,
"app_id": "main"
},
{
"id": "ai_wmbvr5TG",
"name": "celebration",
"value": 0.8383831,
"app_id": "main"
}
]
}
},
{
"frame_info": {
"index": 2,
"time": 2000
},
"data": {
"concepts": [
{
"id": "ai_zJx6RbxW",
"name": "drink",
"value": 0.9856042,
"app_id": "main"
},
{
"id": "ai_mCpQg89c",
"name": "glass",
"value": 0.97975093,
"app_id": "main"
},
{
"id": "ai_8XGJjH7R",
"name": "foam",
"value": 0.9755833,
"app_id": "main"
},
{
"id": "ai_786Zr311",
"name": "no person",
"value": 0.9733174,
"app_id": "main"
},
{
"id": "ai_TBlp0Pt3",
"name": "beer",
"value": 0.969543,
"app_id": "main"
},
{
"id": "ai_SsmKLB4z",
"name": "bar",
"value": 0.94170487,
"app_id": "main"
},
{
"id": "ai_5VHsZr8N",
"name": "liquid",
"value": 0.92778283,
"app_id": "main"
},
{
"id": "ai_2gmKZLxp",
"name": "cold",
"value": 0.9227257,
"app_id": "main"
},
{
"id": "ai_3PlgVmlN",
"name": "food",
"value": 0.9179274,
"app_id": "main"
},
{
"id": "ai_drK6ClJR",
"name": "alcohol",
"value": 0.90887475,
"app_id": "main"
},
{
"id": "ai_B3MXt5Ng",
"name": "refreshment",
"value": 0.9045203,
"app_id": "main"
},
{
"id": "ai_3R5pJ6hB",
"name": "lager",
"value": 0.8938055,
"app_id": "main"
},
{
"id": "ai_WbwL0pPL",
"name": "breakfast",
"value": 0.87420183,
"app_id": "main"
},
{
"id": "ai_54zxXFGL",
"name": "full",
"value": 0.8699659,
"app_id": "main"
},
{
"id": "ai_pkvDRSJ1",
"name": "mug",
"value": 0.8674431,
"app_id": "main"
},
{
"id": "ai_XNmzgDnF",
"name": "pub",
"value": 0.85809004,
"app_id": "main"
},
{
"id": "ai_Lq00FggW",
"name": "desktop",
"value": 0.8506696,
"app_id": "main"
},
{
"id": "ai_zFnPQdgB",
"name": "wood",
"value": 0.8446485,
"app_id": "main"
},
{
"id": "ai_qNxqNBWN",
"name": "cream",
"value": 0.844169,
"app_id": "main"
},
{
"id": "ai_7D0mdp1W",
"name": "delicious",
"value": 0.8397074,
"app_id": "main"
}
]
}
},
{
"frame_info": {
"index": 3,
"time": 3000
},
"data": {
"concepts": [
{
"id": "ai_8XGJjH7R",
"name": "foam",
"value": 0.996614,
"app_id": "main"
},
{
"id": "ai_zJx6RbxW",
"name": "drink",
"value": 0.9794438,
"app_id": "main"
},
{
"id": "ai_786Zr311",
"name": "no person",
"value": 0.9733174,
"app_id": "main"
},
{
"id": "ai_TBlp0Pt3",
"name": "beer",
"value": 0.9645849,
"app_id": "main"
},
{
"id": "ai_mCpQg89c",
"name": "glass",
"value": 0.94761443,
"app_id": "main"
},
{
"id": "ai_pkvDRSJ1",
"name": "mug",
"value": 0.92864025,
"app_id": "main"
},
{
"id": "ai_2gmKZLxp",
"name": "cold",
"value": 0.9227257,
"app_id": "main"
},
{
"id": "ai_5VHsZr8N",
"name": "liquid",
"value": 0.91797745,
"app_id": "main"
},
{
"id": "ai_3PlgVmlN",
"name": "food",
"value": 0.9179274,
"app_id": "main"
},
{
"id": "ai_WbwL0pPL",
"name": "breakfast",
"value": 0.904904,
"app_id": "main"
},
{
"id": "ai_B3MXt5Ng",
"name": "refreshment",
"value": 0.9045203,
"app_id": "main"
},
{
"id": "ai_54zxXFGL",
"name": "full",
"value": 0.889248,
"app_id": "main"
},
{
"id": "ai_BrnHNkt0",
"name": "coffee",
"value": 0.8689867,
"app_id": "main"
},
{
"id": "ai_7D0mdp1W",
"name": "delicious",
"value": 0.86591685,
"app_id": "main"
},
{
"id": "ai_SsmKLB4z",
"name": "bar",
"value": 0.8546975,
"app_id": "main"
},
{
"id": "ai_mZ2tl6cW",
"name": "health",
"value": 0.8544879,
"app_id": "main"
},
{
"id": "ai_cHsR7RS8",
"name": "milk",
"value": 0.852397,
"app_id": "main"
},
{
"id": "ai_zFnPQdgB",
"name": "wood",
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{
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{
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{
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{
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{
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{
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},
{
"id": "ai_8LWlDfFD",
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{
"id": "ai_SsmKLB4z",
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},
{
"id": "ai_cHsR7RS8",
"name": "milk",
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},
{
"id": "ai_qNxqNBWN",
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}
},
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"data": {
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{
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{
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{
"id": "ai_4sJLn6nX",
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{
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{
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{
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{
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{
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{
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},
{
"id": "ai_cHsR7RS8",
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{
"id": "ai_TBlp0Pt3",
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},
{
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},
{
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},
{
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},
{
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{
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},
{
"id": "ai_3PlgVmlN",
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{
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{
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},
{
"id": "ai_BrnHNkt0",
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},
{
"id": "ai_mCpQg89c",
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},
{
"id": "ai_WbwL0pPL",
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{
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},
{
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{
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{
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},
{
"id": "ai_MmRdqDFp",
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{
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},
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},
{
"id": "ai_786Zr311",
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},
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},
{
"id": "ai_pkvDRSJ1",
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},
{
"id": "ai_4sJLn6nX",
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},
{
"id": "ai_B3MXt5Ng",
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},
{
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{
"id": "ai_3PlgVmlN",
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},
{
"id": "ai_54zxXFGL",
"name": "full",
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{
"id": "ai_SsmKLB4z",
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},
{
"id": "ai_BrnHNkt0",
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},
{
"id": "ai_mCpQg89c",
"name": "glass",
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},
{
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"name": "breakfast",
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},
{
"id": "ai_7D0mdp1W",
"name": "delicious",
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},
{
"id": "ai_zFnPQdgB",
"name": "wood",
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},
{
"id": "ai_3R5pJ6hB",
"name": "lager",
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},
{
"id": "ai_8LWlDfFD",
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},
{
"id": "ai_MmRdqDFp",
"name": "soap",
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},
{
"id": "ai_5VHsZr8N",
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}
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},
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},
"data": {
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},
{
"id": "ai_TBlp0Pt3",
"name": "beer",
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},
{
"id": "ai_786Zr311",
"name": "no person",
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},
{
"id": "ai_zJx6RbxW",
"name": "drink",
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},
{
"id": "ai_4sJLn6nX",
"name": "dark",
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},
{
"id": "ai_pkvDRSJ1",
"name": "mug",
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},
{
"id": "ai_B3MXt5Ng",
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},
{
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},
{
"id": "ai_3PlgVmlN",
"name": "food",
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},
{
"id": "ai_54zxXFGL",
"name": "full",
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},
{
"id": "ai_SsmKLB4z",
"name": "bar",
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},
{
"id": "ai_BrnHNkt0",
"name": "coffee",
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},
{
"id": "ai_mCpQg89c",
"name": "glass",
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},
{
"id": "ai_7D0mdp1W",
"name": "delicious",
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},
{
"id": "ai_WbwL0pPL",
"name": "breakfast",
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},
{
"id": "ai_zFnPQdgB",
"name": "wood",
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"app_id": "main"
},
{
"id": "ai_3R5pJ6hB",
"name": "lager",
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},
{
"id": "ai_8LWlDfFD",
"name": "table",
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},
{
"id": "ai_MmRdqDFp",
"name": "soap",
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},
{
"id": "ai_5VHsZr8N",
"name": "liquid",
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}
]
}
}
]
}
}
]
}

Via Bytes

Below is an example of how you would send the bytes of a video and receive back predictions from the general model.

text
javascript
python
java
csharp
text
php
text
text
text