Custom dataset without segmentations

Is there a way to run a custom dataset that only has bounding boxes? I have the Wider Face dataset in COCO api json format, but it won’t train without a segmentation field in annotations. I had to use two work arounds:

1. Use annotation['segmenation'] = 0 and annotation['area'] = bbox_height * bbox_width (and of course leaving TRAIN.GT_MIN_AREA = -1)
2. To allow TRAIN.USE_FLIPPED = True, Detectron/lib/datasets/roidb.py had to have some code commented out in this function:

def extend_with_flipped_entries(roidb, dataset):
    """Flip each entry in the given roidb and return a new roidb that is the
    concatenation of the original roidb and the flipped entries.
    "Flipping" an entry means that that image and associated metadata (e.g.,
    ground truth boxes and object proposals) are horizontally flipped.
    """
    flipped_roidb = []
    for entry in roidb:
        width = entry['width']
        boxes = entry['boxes'].copy()
        oldx1 = boxes[:, 0].copy()
        oldx2 = boxes[:, 2].copy()
        boxes[:, 0] = width - oldx2 - 1
        boxes[:, 2] = width - oldx1 - 1
        assert (boxes[:, 2] >= boxes[:, 0]).all()
        flipped_entry = {}
        dont_copy = ('boxes', 'segms', 'gt_keypoints', 'flipped')
        for k, v in entry.items():
            if k not in dont_copy:
                flipped_entry[k] = v
        flipped_entry['boxes'] = boxes
        ### commenting out to allow flipping for datasets without segmentations annotated
        #flipped_entry['segms'] = segm_utils.flip_segms(
        #   entry['segms'], entry['height'], entry['width']
        #)
        if dataset.keypoints is not None:
            flipped_entry['gt_keypoints'] = keypoint_utils.flip_keypoints(
                dataset.keypoints, dataset.keypoint_flip_map,
                entry['gt_keypoints'], entry['width']
            )
        flipped_entry['flipped'] = True
        flipped_roidb.append(flipped_entry)
    roidb.extend(flipped_roidb)

With those two adjustments the code runs beautifully. Is there a flag or config param that I am missing, or an easier way to run datasets with only bboxes?

Thank you for the code and the docker version!

[ir413]

Hi @learnbott, I think it may be simpler and less error-prone to preprocess your json annotations to include the following entries:

'segmentation': []
'area': box_width * box_height
'iscrowd': 0

I believe this would allow you to perform training (w/ flipping) without any modifications to the code.

[learnbott]

Much appreciated!

On Sat, Jan 27, 2018 at 4:51 PM, Ilija Radosavovic <[e-mail]

wrote:

Hi @learnbott https://github.com/learnbott, I think it may be simpler and less error-prone to preprocess your json annotations to include the following entries:

‘segmentation’ : [] ‘area’: box_width * box_height ‘iscrowd’: 0

I believe this would allow you to perform training (w/ flipping) without any modifications to the code.

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