I am looking for advice or suggestion. I have photos like these: photo_1 and photo_2 and many more similar to that. The average shape of these photos is about 160 x 100. What we are doing is we are trying to find wheather or not person in a photo is wearing safety vest and helmet (if person is wearing both it is 1, if something is missing or both are missing it is 0). Training data consists of about 5k almost equally distributed image sets. I have tried to use augmentation techniques (flipping, adding noise, brighness correction) but results didn't improove. I tried to train on many pretrained popular models: resnet101, mobilenet_v2, efficientneyb3, efficientneyb0, DenseNet121, InceptionResNetV2, InceptionV3, ResNet152V2, ResNet50V2, but results are not eyepleasing. I have tried different input sizes ranging from 224x224 to 112x112 but result didn't improve as much as I would have liked it to be. And the weird thing is that the image shape does not correlate to wheather or not there are more wrong predictions using bigger or smaller images. As a side not I would lik to ask couple questions:
- Should I use my own written small net?
- Are the models that I use too big for this problem?
Any advice will be appreciated.