# How to handle images of different sizes that are smaller than the input layer of a deep learning model?

I am performing human awareness detection and have trained my model using transfer learning with MobileNetV2. This model expects a tensor of dimension [Null,224,224,3].

I have applied face detection using BlazeFace which uses an input of [128,128,3] on the input video stream and cropped the detected faces in order to send the cropped faces to my custom model but I am not sure what to do as the cropped images are all of varying sizes and smaller than what my model expects.

Example of a cropped face tensor

Array [
1,
43,
111,
3,
]


The issue was fixed by resizing the Tensor to fit into the model. I was reshaping them instead of resizing them.

• how did you resize the Tensor? Nov 20 '20 at 1:00
• If you are using tensorflow then tf.image.resize() would be the way to do so. Nov 20 '20 at 2:13