{
    "task_id": "MFcoPiJDQ7Ti1mKlSRbe2K6AhalHyvR5",
    "type": undefined,
    "tag": "Tag all the cars",
    "data": {
      attachment: "https://dummy.com/image.png",
      annotation_categories: ["car"]
    },
    "importance": "standard",
    "status": "pending",
    "response": {
      "annotation": {
        "size": {
          "depth": "3"
          "width": "800"
          "height": "534"
        },
        "object": [
          {
            "name": undefined,
            "pose": "Unspecified",
            "bndbox": {
                "xmax": "182",
                "ymax": "132",
                "xmin": "377",
                "ymin": "343"
            },
            "polygon": {
              "x1": "137",
              "x2": "140",
              "x3": "166",
              "x4": "176",
              "x5": "181",
              "x6": "182",
              "x7": "181",
              "x8": "175".
              "x9": "174",
              "y1": "377",
              "y2": "376",
              "y3": "377",
              "y4": "375",
              "y5": "371",
              "y6": "363",
              "y7": "355",
              "y8": "354",
              "y9": "345",
              "x10": "153",
              "x11": "145",
              "x12": "138",
              "x13": "134",
              "x14": "132",
              "x15": "136",
              "x16": "134",
              "x17": "132",
              "y10": "343",
              "y11": "343",
              "y12": "351",
              "y13": "350",
              "y14": "355",
              "y15": "354",
              "y16": "258",
              "y17": "376"
            }
            "occuluded": "0",
            "difficuilt": "0",
            "truncated": "0"
          },...
        ],
        "filename": "image.png",
        "segmented": "0",
      }
    },
    "completed_at": null,
    "credit_cost": null
  }
          
Polygons
Leverage our human workers to delegate polygon annotation tasks using our extremely extensive and simple API.
Use Cases
Self Driving Cars
With the ability to support any categories, Raidon's API provides the perfect platform to annotate images that may be used to power self driving cars and other AI automation needs such as planes, boats and robots. With support for the PASCAL VOC format our annotations can easily integrate into Google's Tensforflow.


Drones
With Drones becoming more popular year-by-year and companies recognising the ability of such devices to accomplish a variety of tasks such as the delivery of items, storm tracking and supplying essentials for disaster management means there's an ever greater need for smarter drones to detect and recognise objects.


Retail
Machine learning can be easily used to recognise products such as Nike sneakers. This can then be used to power stores with no checkout with cameras being used to track customers and record the products they add to their shopping basket. This will require annotating lots of different products which is where Raidon comes in.


Face and Celeberity Recognition
With security becoming more important, machine learning can power extremely accurate facial recognition by training neural network to first recognise faces and then compare results. Other uses include recognising famous celeberities and providing metadate of the actors present within a particular scene whilst watching movies.




Why Raidon?
With free repetition allowance, ability to download VOC complaint XML files and superb customer service we're confident you'll love us.
Pixel Perfect Annotation
Whether you want tight or loose polygons, our trained workers will be able to meet your requirements. With our simple and easy to understand API, you'll be provided with every vertex of the polygon.
PASCAL VOC Annotations
All of our annotations are PASCAL VOC complaint allowing you to easily download an XML file that you can feed directly to leading machine-learning frameworks such as Tensorflow for object detection.
Cost Free Repetition
If you don't agree with the annotation of one of our workers or we didn't meet your requirement just contact support and we'll repeat your annotations free of charge. We're not happy unless you're happy.
Supports all files
Whether you want to provide us a url to image you want to annotate or upload a file to our servers, Raidon supports all file types. As long as we can open the file we'll annotate it.
Edit your tasks
Accidently submitted the wrong image or file? No problem - Raidon provided free cost edits allowing you to change the type of annotation, file submitted or a tasks importance allowing you to speed its annotation up!
See Annotations in Dashboard
Forget the terminal, with Raidon's Dashboard you'll be able to view the polygons drawn for each image annotated, send images back for reannotation, download results in JSON or XML format and much more!
How it Works
Just send us your unannotated images and get back VOC complaint annotated polygons
   curl -X POST \
      https://raidon.io/api/v1/task/annotation/ \
      -H 'Authorization: Token 49fa77ade8a3b9504a8d5138836030b44dd9ea46' \
      -d type='polygons' \
      -d tag='Tag the appropriate vechiles' \
      -d attachment='https://dummyimageurl.com/oxford_street.jpg' \
      -d categories='bus' \
      -d categories='taxi'
Execute Code
   import requests 
   headers = {
      'Authorization': 'Token 49fa77ade8a3b9504a8d5138836030b44dd9ea46'
   }
   data = {
      'type': 'polygons', 
      'tag': 'Tag the appropriate vechiles', 
      'attachment': 'https://dummyimageurl.com/oxford_street.jpg', 
      'categories': ['bus', 'taxi']
   }
   r = requests.post('https://raidon.io/api/v1/task/annotation/',
                     headers=headers,
                     data=data)
   r.content
Execute Code
   const request = await fetch('https://raidon.io/api/v1/task/annotation', method={
   method: 'POST', 
   headers: {
         'Authorization': 'Token 49fa77ade8a3b9504a8d5138836030b44dd9ea46', 
   }, 
   body: {
         type: 'polygon',
         tag: 'Tag the appropriate vechiles',
         attachment: 'https://dummyimageurl.com/oxford_street.jpg', 
         categories: ['bus', 'taxi']
   }
   })
   const response = await request.json()
   console.log(response)
Execute Code
   {
      "task_id": "2rXrvjvYST8dQppTFHA9Ni68Tz3X8DpM",
      "created_at": "2018-08-24T20:59:05.374168Z",
      "type": "polygons",
      "tag": "Tag the appropriate vechile",
      "data": {
         "attachment": "http://media-raidon.s3-us-west-2.amazonaws.com/2rXrvjvYST8dQppTFHA9Ni68Tz3X8DpM/oxford_street.jpg",
         "categories": [
               "bus",
               "taxi"
         ]
      },
      "importance": "standard",
      "status": "completed",
      "callback_url": "www.raidon.io/api/v1/task/2rXrvjvYST8dQppTFHA9Ni68Tz3X8DpM",
      "response": {
         "annotation": {
               "size": {
                  "depth": "3",
                  "width": "1800",
                  "height": "1200"
               },
               "object": [
               {
                     "name": "taxi",
                     "pose": "Unspecified",
                     "occluded": "0",
                     "difficult": "0",
                     "truncated": "0"
               },
               {
                     "name": "taxi",
                     "pose": "Unspecified",
                     "bndbox": {
                        "xmax": "902",
                        "xmin": "860",
                        "ymax": "846",
                        "ymin": "793"
                     },
                     "polygon": {
                        "x1": "898",
                        "x2": "902",
                        "x3": "902",
                        "x4": "902",
                        "x5": "900",
                        "x6": "900",
                        "x7": "901",
                        "x8": "902",
                        "x9": "902",
                        "y1": "805",
                        "y2": "805",
                        "y3": "806",
                        "y4": "808",
                        "y5": "808",
                        "y6": "809",
                        "y7": "812",
                        "y8": "813",
                        "y9": "816",
                        "x10": "902",
                        "x11": "902",
                        "x12": "902",
                        "x13": "900",
                        "x14": "901",
                        "x15": "901",
                        "x16": "900",
                        "x17": "899",
                        "x18": "896",
                        "x19": "895",
                        "x20": "895",
                        "x21": "893",
                        "x22": "890",
                        "x23": "889",
                        "x24": "889",
                        "x25": "889",
                        "x26": "888",
                        "x27": "887",
                        "x28": "883",
                        "x29": "882",
                        "x30": "881",
                        "x31": "879",
                        "x32": "878",
                        "x33": "877",
                        "x34": "873",
                        "x35": "872",
                        "x36": "873",
                        "x37": "872",
                        "x38": "871",
                        "x39": "871",
                        "x40": "868",
                        "x41": "865",
                        "x42": "866",
                        "x43": "865",
                        "x44": "864",
                        "x45": "862",
                        "x46": "861",
                        "x47": "860",
                        "x48": "860",
                        "x49": "863",
                        "x50": "864",
                        "x51": "863",
                        "x52": "864",
                        "x53": "863",
                        "x54": "863",
                        "x55": "863",
                        "x56": "865",
                        "x57": "870",
                        "x58": "883",
                        "x59": "884",
                        "x60": "887",
                        "x61": "890",
                        "x62": "892",
                        "x63": "894",
                        "x64": "895",
                        "x65": "896",
                        "x66": "897",
                        "x67": "897",
                        "x68": "898",
                        "y10": "820",
                        "y11": "824",
                        "y12": "827",
                        "y13": "828",
                        "y14": "830",
                        "y15": "831",
                        "y16": "832",
                        "y17": "832",
                        "y18": "834",
                        "y19": "833",
                        "y20": "830",
                        "y21": "829",
                        "y22": "829",
                        "y23": "830",
                        "y24": "832",
                        "y25": "834",
                        "y26": "836",
                        "y27": "839",
                        "y28": "843",
                        "y29": "845",
                        "y30": "846",
                        "y31": "846",
                        "y32": "844",
                        "y33": "843",
                        "y34": "841",
                        "y35": "837",
                        "y36": "835",
                        "y37": "833",
                        "y38": "831",
                        "y39": "829",
                        "y40": "829",
                        "y41": "829",
                        "y42": "832",
                        "y43": "834",
                        "y44": "835",
                        "y45": "832",
                        "y46": "829",
                        "y47": "827",
                        "y48": "824",
                        "y49": "823",
                        "y50": "820",
                        "y51": "817",
                        "y52": "813",
                        "y53": "809",
                        "y54": "805",
                        "y55": "801",
                        "y56": "794",
                        "y57": "793",
                        "y58": "793",
                        "y59": "793",
                        "y60": "793",
                        "y61": "793",
                        "y62": "793",
                        "y63": "794",
                        "y64": "795",
                        "y65": "797",
                        "y66": "800",
                        "y67": "803",
                        "y68": "804"
                     },
                     "occluded": "0",
                     "difficult": "0",
                     "truncated": "0"
                  },...
               ],
               "filename": "oxford_street.jpg",
               "segmented": "0"
         }
      },
      "completed_at": "2018-08-24T21:02:46.146796Z",
      "credit_cost": "$0.28"
   }
         
Pricing
Raidon charges a fixed price for each image and each polygon annotated so you know exactly what you're paying for. There’s no monthly charge, no inactivity charges and absolutely no hiddent costs. It's just simple and transparent pricing.
$0.08 + $0.10
per annotation
Frequently Asked Questions
What is Raidon?

Raidon provides machine learning firms with the ability to leverage our human workers to annotate images and other tasks to power their AI products speeding up their route to market.

What are polygons?

Polygons refer to the shape of each annotation on the image described by a group of x and y cordinates. Each x cordinate respresents the pixel distance between the polygon vertex and the left border of the image and each y cordinate represents the pixel distance between the polygon vertex and the top border of the image. More information can be found in our API docs.

Do I get a free trial?

Yes, new users are given the ability to request the annotation of 20 images to be annotated by polygons, without being charged or their bank details being asked for, to try out our services. Early stage machine learning startups can request to have their free limit increased to 100 by contacting sales.

What is an urgency task?

Urgency tasks are given priority over other tasks and we aim to complete urgency tasks within 1 working day. However, urgency tasks are subject to different pricing compared to standard tasks

How long do standard tasks take?

Standard task will vary depending on the on the number of images sent to be annotated as well as the number of categories, however, we aim to to complete standard tasks within 3 working days.

How do I send the images to be annotated?

You can send images either as a url or upload the image from your local storage to our API. In either case your images will be saved on our servers.

Do you sell our images to third parties?

Absolutely not, Raidon has a strict policy not to sell our clients data to third parties. We only store your uploaded data for your use so you can confirm the annotations using our dashboard. All images are deleted if the task is deleted via an API request. Furthermore, all data associated with the user is deleted if the user makes a requests to have his account terminated indefinitely.

How does the pricing work?

Raidon charges $0.08 for each image sent to be annoated and a further $0.10 for each annotation. An annotation is defined as actual blue boxes drawed on top of the images you see above.

How do I view the annotations once they're completed?

You can either view each task you sent to our API to be annotated, using our interative dashboard or get the annotation back using our API either as a JSON response or a VOC complaint XML file.

What other annotation types do you support?

Plans to support the COCO and KITTI format are currently being worked on. Sign up and create an account to recieve updates.