Then we train and build a model out these uploaded images:
POST https://southcentralus.api.cognitive.microsoft.com/customvision/v2.0/Prediction/{PID}/url?iterationId={iterationId} HTTP/1.1 Host: southcentralus.api.cognitive.microsoft.com Prediction-Key: {PKEY} Content-Type: application/json
{ "Url": "https://di-uploads-pod11.dealerinspire.com/raylaethemchryslerdodgejeep/uploads/2017/09/2018-Jeep-Compass-Hero.png" }
apim-request-id: {id} Strict-Transport-Security: max-age=31536000; includeSubDomains; preload x-content-type-options: nosniff Date: Mon, 25 Jun 2018 21:01:34 GMT Content-Length: 389 Content-Type: application/json; charset=utf-8
{ "id": "{id}", "project": "{pid}", "iteration": "{iteration}", "created": "2018-06-25T21:01:34.487003Z", "predictions": [{ "probability": 0.468212515, "tagId": "b2af220e-a4f4-43b7-9c98-c13a6c9f8ca1", "tagName": "Mercedes" }, { "probability": 0.367403924, "tagId": "8e958793-dfd4-40c5-92ee-b861c7005b5e", "tagName": "Jeep" }] }
So our model is not si good as it's saying the new image os 47% Mercedes and 36% channces that it could be Jeep
Now we'll uploaded more images so that our system can understand the differnces b/t these 2 cars very well
Once we are doing the training phase 2, we test same image against our model then see the result :
Http Response : apim-request-id: {id} Strict-Transport-Security: max-age=31536000; includeSubDomains; preload x-content-type-options: nosniff Date: Mon, 25 Jun 2018 20:59:48 GMT Content-Length: 388 Content-Type: application/json; charset=utf-8
{ "id": "{id}", "project": "{pid}", "iteration": "{iteration2}", "created": "2018-06-25T20:59:48.4573752Z", "predictions": [{ "probability": 0.9310171, "tagId": "8e958793-dfd4-40c5-92ee-b861c7005b5e", "tagName": "Jeep" }, { "probability": 0.166567564, "tagId": "b2af220e-a4f4-43b7-9c98-c13a6c9f8ca1", "tagName": "Mercedes" }] }