# Mara Animal Detector

This page describes how one can run the BisQue Module `MaraAnimalDetection`

## Run Instructions

This module takes in an image or a dataset of images. It generates bounding box predictions indicating the location of several animal classes in the image.

### Navigate to Module Page

[Login](https://bisque.gitbook.io/docs/bisque-service/login-signup) >> Analyze >> Maasai Mara (in Groups Column) >> MaraAnimalDetection

<figure><img src="https://1847764884-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FT4FfXoVYtIiSVERwSi0y%2Fuploads%2Fj5LiCMNLIDtCNazN0zo2%2Fimage.png?alt=media&#x26;token=0c755cb7-52e0-451b-9f2b-0d7d3300f36b" alt=""><figcaption><p>Navigation to Module Page</p></figcaption></figure>

### Expected Inputs

* An image or a dataset of images
  * [Click here](https://bisque2.ece.ucsb.edu/client_service/view?resource=https://bisque2.ece.ucsb.edu/data_service/00-KiEfGPpfTrHigpoUtTjKgB) for a sample dataset of input images
* Machine Learning Model
  * [Click here](https://bisque2.ece.ucsb.edu/client_service/view?resource=https://bisque2.ece.ucsb.edu/data_service/00-E8jASuwJRnL95uhF3fjtm5) for a sample model file
* Output G-Object Name
  * This can be any string that can be used to name the annotations predicted by this module execution.

<figure><img src="https://1847764884-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FT4FfXoVYtIiSVERwSi0y%2Fuploads%2FiZTxnxUJZDDa1p0khrT9%2Fimage.png?alt=media&#x26;token=5f300f33-d519-4d2d-848c-689ee8d2dfae" alt=""><figcaption></figcaption></figure>

### Run Inference

Hit `Run` after providing required inputs.

You should be able to see the status messages at the bottom of the page.

<figure><img src="https://1847764884-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FT4FfXoVYtIiSVERwSi0y%2Fuploads%2FFS0GNQOLYdygGUm5BbkJ%2Fimage.png?alt=media&#x26;token=23c3c66b-650c-4d35-aa58-24bb35c6943b" alt=""><figcaption><p>Module page after running the inference</p></figcaption></figure>

### Expected Outputs

* Annotations will be added to the images/dataset as G-Objects
  * Navigate to the images present in the dataset to visualize the predicted G-Object

### Output Interpretation

In this section, we describe how to visualize/update the predictions generated by running the model.

Once the module execution is complete, one can navigate to the input dataset to visualize the model predictions in the form of G-Objects.

* Navigate to the dataset.

![](https://1847764884-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FT4FfXoVYtIiSVERwSi0y%2Fuploads%2FoOpI8yLnaA0eyAIUohIt%2Fimage.png?alt=media\&token=816481d6-6165-48cb-852f-31ab5afa491b)

* Go to the Annotation View.

![](https://1847764884-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FT4FfXoVYtIiSVERwSi0y%2Fuploads%2Fv9A9YKwaqNmYo2yGfU1h%2Fimage.png?alt=media\&token=f7b301cd-5118-4cb0-94eb-34a7df5087dc)

* Click on Graphical to view the annotation

![](https://1847764884-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FT4FfXoVYtIiSVERwSi0y%2Fuploads%2FiBVysd2xTSYBSgW3cel1%2Fimage.png?alt=media\&token=9f41c7e7-89f7-4264-ab6d-b71b7e1f100f)

#### Export the Predictions

* Navigate to Dataset Page
* Click on `Download`
* Click on `Graphical Annotations as XML`

![](https://1847764884-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FT4FfXoVYtIiSVERwSi0y%2Fuploads%2FJWdpXeoE9mEvGLmIYoNf%2Fimage.png?alt=media\&token=3d278152-478f-469b-bd95-f1d693e7975b)