Image Classification using Single Class Dataset using Transfer Learning I only have around 1000 images of vehicle. I need to train a model that can identify if the image is vehicle or not-vehicle. I do not have a dataset for not-vehicle, as it could be anything besides vehicle.

I guess the best method for this would be to apply transfer learning. I am trying to train data on a pre-trained VGG19 Model. But still, I am unaware on how to train a model with just vehicle images without any non-vehicle images. I am not being able to classify it.

I am new to ML Overall, Any solution based on practical implementation will be highly appreciated.
You can use Tensorflow Object Detection API. 

In the API, you dont have to have 2 or more labels. It can be used with only one label. By transforming images to tfrecords, you can train and predict other images. The prediction will you give you bounding boxes and probabilities that how much an object identified as a vehicle.

The challenge in here is labeling object locations on the images. LabelImg is a great tool for that.

Hope it helps!