This is a TensorFlow2.0 implementation of the YOLOv1 paper https://arxiv.org/abs/1506.02640, with the following changes,

• The feature exactor resembles the one mentioned in the YOLO9000 paper
• Input size is changed from 448x448 to 608x608
• The output stride is reduced from 64 to 32, to capture smaller objects
• Used 9 boxes per grid location, the paper uses 2. [doing this did not help much]

trained weights

• Change backbone to the one from YOLO9000
• Use tf.distribute.MirroredStrategy for multi gpu training
• Use tf.data.Dataset to implement the data input pipeline
• Add quantitative results
• Use learning_rate schedule during training
• Add horizonal flip in data input pipeline

• The model was trained on the Berkeley Deep Drive (BDD) dataset, which has 70,000 training images and 10000 validation images
• The model was trained for 200 epochs with a learning_rate of 5e-4 and a batch size of 24 [8 images per gpu] with heavy augmentations [brightness, saturation, contrast]
• No learning_rate schedule was followed (but was used by the authors).