The main use of this application is to detect sheep from the image, which can then be used for many purposes like counting sheep and keeping track of the sheep in the yard.
It will help farms to maintain a proper record of there sheep too.
So, Lets get started
This dataset contains 203 images of sheep. Mainly, Sheep images with bounding box annotations in Pascal VOC format
Now lets have a look at the necessary imports
Since we are not having many images we need to apply data augmentation
Data augmentation helps us by duplicating images while applying tilt, rotations and other methods which does not tamper with the main object but changes it a bit thereby keeping the main goal intact.
Lets have a look at the samples
This model is then trained using binary cross entropy using Adam with the learning rate of 0.0001. Also callbacks are used to introduce early stopping. With the help of early stopping we stop the model from further training by monitoring defined parameters.
As you can see you the model has reached more than 90% accuracy allowing us to predict whether the sheep is in the image on not.
Lets have a look at the predictions
Notebook Link : Here
Credit: vishal yadav