API Endpoints

Domain

https://www.mesonetdetect.games/

Dataset Size
List Models
Prediction Results

Dataset Size

Returns JSON data about the number of images available in the dataset on the server.

URL: /api/dataset-size/

Django View: api.views.DatasetSizeView [source]

Method: GET

URL Parameters: None

Data Parameters: None

Success Response:

HTTP/1.1 200 OK
Server: nginx/1.18.0 (Ubuntu)
Date: Wed, 28 Apr 2021 16:33:47 GMT
Content-Type: application/json
Content-Length: 13
Connection: keep-alive
Vary: Accept, Cookie
Allow: GET, HEAD, OPTIONS
X-Frame-Options: DENY
X-Content-Type-Options: nosniff
Referrer-Policy: same-origin

{"size": 1945}

Response Fields:

size: Number of images in the dataset.

List Models

Returns JSON data listing all the MesoNet variants available to obtain predictions from.

URL: /api/available-models/

Django View: api.views.ListModelsView [source]

Method: GET

URL Parameters: None

Data Parameters: None

Success Response (showing only one item):

HTTP/1.1 200 OK
Server: nginx/1.18.0 (Ubuntu)
Date: Wed, 28 Apr 2021 16:37:57 GMT
Content-Type: application/json
Content-Length: 1054
Connection: keep-alive
Vary: Accept, Cookie
Allow: GET, HEAD, OPTIONS
X-Frame-Options: DENY
X-Content-Type-Options: nosniff
Referrer-Policy: same-origin

[
   {
      "model_id":"5aad0bb4-2d4c-4846-8636-85e0eaa0cbe3",
      "model_name":"Model 1",
      "model_desc":"Trained for 17 epochs with validation accuracy of 92.52%",
      "loss_curve":"https://www.mesonetdetect.games/media/loss_curves/model_1_curve.png",
      "accuracy":96.25,
      "clr":{
         "rows":[
             ["0", 0.96, 0.94, 0.95, 773],
             ["1", 0.96, 0.97, 0.97, 1172],
             ["accuracy", "", "", 0.96, 1945],
             ["macro avg", 0.96, 0.96, 0.96, 1945],
             ["weighted avg", 0.96, 0.96, 0.96, 1945]
         ],
         "columns":["", "precision","recall","f1-score","support"]
      },
      "conv_layers":[
         [8, [3, 3]],
         [8, [5, 5]],
         [16, [5, 5]],
         [16, [5, 5]]
      ]
   }
]

Response Fields:

Information about each variant includes the following -

model_id (string) - Unique ID for the variant.
model_name (string) - Unique user-friendly name for the variant.
model_desc (string) - One-line description of the variant (training, accuracy, etc.).
loss_curve (string) - URL to the variant’s loss curve.
accuracy (float) - Measured accuracy of the variant on the dataset available on the server.
clr (json) - Classification report on the dataset available on the server. This has the following fields:
- columns (array) - Column headers for the classification report.
- rows (array of arrays) - Rows of the classification report.
conv_layers (array of arrays) - Number of filters and their size for each convolutional layer.

Prediction Results

Returns JSON data consisting of predictions made using the given MesoNet variant for the given number of images. Optionally, it also includes URLs to plots of specified convolutional layers for each image.

URL: /api/predictions/:modelID/:numImgs/[:convIdx]/

Django View: api.views.PredictionResultsView [source]

Method: GET

URL Parameters:

modelID (required) - Unique ID of the variant that should be used for obtaining predictions. It should be a string representing a UUID.
numImgs (required) - Number of images on which a prediction should be made. It should be an integer not greater than the number of images obtained from the Dataset Size endpoint.
convIdx (optional) - Indices of the convolutional layers (0-indexed) whose plots should be included in the prediction. It should be a string of indices. For example, if you want the plots of the first and third convolutional layer, this parameter should be 02. Similarly, for the first, second and third layers, it should be 012. Note that, in the response, the order of the plots will be in the same as that of the indices in this parameter.

Data Parameters: None

Success Response (showing only one item):

With the convIdx parameter (showing only one image, plots are for the first and second layer):

HTTP/1.1 200 OK
Server: nginx/1.18.0 (Ubuntu)
Date: Wed, 28 Apr 2021 19:26:40 GMT
Content-Type: application/json
Content-Length: 204
Connection: keep-alive
Vary: Accept, Cookie
Allow: GET, HEAD, OPTIONS
X-Frame-Options: DENY
X-Content-Type-Options: nosniff
Referrer-Policy: same-origin


[
 {
    "img_url":"/media/data/real/real07167.jpg",
    "true_label":"real",
    "pred_label":"real",
    "probability":91.3399,
    "plots":[
       "/media/plots/plot_img1_conv0_SYO1pyT.png",
       "/media/plots/plot_img1_conv1_RYT0MMP.png"
    ]
 }
]

Without the convIdx parameter (showing only one image):

HTTP/1.1 200 OK
Server: nginx/1.18.0 (Ubuntu)
Date: Wed, 28 Apr 2021 19:29:49 GMT
Content-Type: application/json
Content-Length: 118
Connection: keep-alive
Vary: Accept, Cookie
Allow: GET, HEAD, OPTIONS
X-Frame-Options: DENY
X-Content-Type-Options: nosniff
Referrer-Policy: same-origin


[
 {
    "img_url":"/media/data/real/real02303.jpg",
    "true_label":"real",
    "pred_label":"real",
    "probability":98.422,
    "plots":[]
 }
]

Response Fields:

Information about each variant includes the following -

img_url (string) - Relative URL of the image file with respect to the domain.
true_label (string) - Actual class label of the image (real/deepfake).
pred_label (string) - Predicted class label of the image (real/deepfake).
probability (float) - Predicted sigmoid probability of the image in percentage. Note that this probability always indicates the likelihood of image belonging to the predicted label, irrespective of whether the label represents the positive or the negative class. For example, above, the 98.422% is the probability of the image being real. If the predicted label was deepfake, 98.422% would be the probability of the image being a deepfake.
plots (array of strings): Relative URLs of the plots of convolutional layers for the image with respect to the domain. It is an empty array when convIdx is not provided.

Table of Contents

Dataset Size

List Models

Prediction Results