Weird behaviour with instance segmentation

Hello, I’m trying to use Detr for instance segmentation and trained following the guidelines:

1. Train Box-Model 2. train with --masks without any coco_panoptic flags. The training curve for my segm mAP looks good, however, i noticed that the mask spans all instances for every valid prediction instead of the expected one mask per instance. Boxes work fine though.

out = model(img)
probas = out['pred_logits'].softmax(-1)[0, :, :-1]
keep = probas.max(-1).values > 0.5
ncols = 2
fig, axs = plt.subplots(ncols=ncols, nrows=math.ceil(keep.sum().item() / ncols), figsize=(18, 10))
for line in axs:
    for a in line:
        a.axis('off')
for i, mask in enumerate(out["pred_masks"][keep]):
    ax = axs[i // ncols, i % ncols]
    ax.imshow(mask, cmap="cividis")
    ax.axis('off')

1. Target masks in each image are separated (e.g. 4 individual masks for each instance) in train/valid dataloader
2. Not a (big) issue using the out-of-the-box panoptic model (although obviously poor results due to lack of fine tuning):

model, postprocessor = torch.hub.load('facebookresearch/detr', 'detr_resnet101_panoptic', pretrained=True, return_postprocessor=True, num_classes=250)

So what could be the cause of this problem?

[m-klasen]

Training both bbox&segm straight on the pretrained panoptic weights with only class weights removed and nothing frozen was the solution. Got box mAP 0.65 & segm mAP 0.60 right now. Thank you for your help.

[gfiameni]

Hi @m-klasen

(1) Use a pretrained object detection model from the Model ZOO to prepare a model_weights file that includes all pretrained weights except for the fully connected class layer (or just resume a model zoo one an deal with the class layer shape mismatch in the main.py file)

what do you exactly mean with point #1?

I delete class embed weights and biases before fine tuning.

checkpoint = torch.hub.load_state_dict_from_url(
            url='https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth',
            map_location='cpu',
            check_hash=True)

del checkpoint["model"]["class_embed.weight"]
del checkpoint["model"]["class_embed.bias"]

The output of this operation becomes the --resume input.