Training on Private Dataset

Thank you for sharing this code with us.

Can I train on my own dataset with only 11 classes? I am overriding config.num_classes with 11 and overriding config.image_size with 512 but I get very bad results, it is almost like the model is not even aware of the image…

I made sure to put in the boxes as yxyx in the dataset and also made sure the classes start from 1 as I think it is needed due to how fast_collate is working.

Would appreciate your kind help, thank you.

[pasandrei]

After 40 epochs with 50 images with batch size of 2, lr 0.001, AdamW:

1. 50 images is WAY too few images. I would suggest using at least a few thousand.
2. 40 epochs is too little for efficientdet
3. Because of the way BatchNorm works, a batch size of 2 is also too little. I think 32 is the hotspot.
4. You could also increase the learning rate to something like 0.01

Just as a reference, I trained EfficientDet-Lite0 with ~12000 images, 300 epochs, batch size of 40 and with lr ~0.05

[jinfagang]

@ofekp cascased maskrcnn’s Mask Head with single stage detector seems not a good practise, since the bboxes are too much and not high quality as 2 stages does. Instead, if you try Yolact++ style, you can get a very nice && very easy to deploy instance segmentation model.