Project name: Intersection recognition by EfficientNet-d0 or ResNet-50 1.dataset we orgnize our street data in train_dataset like this: root/dog/xxx.png root/dog/xxy.png root/dog/xxz.png root/cat/123.png root/cat/nsdf3.png root/cat/asd932_.png 2.about our model ResNet-50 and EfficientNet-d0 are trained here. when training EfficientNet-d0, autoaugmentation policy is used for data agumentation. 3.usage: how to train an model? just orgnize your dataset in above format and set hyperparameters such as lr in train_ResNet.py or train_efficient.py. then run it. how to test an model? put your test images in .jpg or .png in the test folder and then run exam.py. results would be written in a .dat file.
Edwardwaw/Intersection-recognition
Intersection recognition or image classfication by EfficientNet-d0 with auto-augmentation
Python