Databunch for Object detection

Can anyone help me with an example on how to create databunch for object detection with ? I am aware that this will be explained in part -2, but I am asking it out of enthusiaism . Please let me know if anyone has any idea.

The databunch is really just a wrapper around train/valid/test dataloaders. The bits that are different for image segmentation are the datasets which have to include not just image/category but also bbox and possibly mask. A dataset is just an abstract class so you can define getitem to return anything you want.

I have implementation of maskrcnn which uses fastai. Probably could be better e.g. did not use the data block api. And the architecture of maskrcnn is crazy complex. Nevertheless may help.


I want more detailed example using datablock api.

You can find all kind of examples in .There include the Object Detection like below:

data = (ObjectItemList.from_folder(coco)
    #Where are the images? -> in coco and its subfolders
    #How to split in train/valid? -> randomly with the default 20% in valid
    #How to find the labels? -> use get_y_func on the file name of the data
    .transform(get_transforms(), tfm_y=True)
    #Data augmentation? -> Standard transforms; also transform the label images
    .databunch(bs=16, collate_fn=bb_pad_collate))   
    #Finally we convert to a DataBunch, use a batch size of 16,
    # and we use bb_pad_collate to collate the data into a mini-batch

this is fine but i want something like how to make a databunch of images and their bounding box values .

See my post here: Object detection databunch