run on our dataset, but not convergence

[liangxuejingjing]

@MengyuWang826 hi~

When I ran your code on our dataset, a floorplan, we got white outputs...I don't know the reason...

And the total loss kept rising， here is my training log. 20240221_075018.log.json

How do we resolve this problem? retrain your model on our dataset? or generate a new coarse mask dataset only about the edge? Please reply to me ASAP.

[yanrihong]

can i check how you load your custom dataset？

[liangxuejingjing]

can i check how you load your custom dataset？

I generated the coarse mask as the authors have released and then collected them into JSON format.

`base = [ './segrefiner_lr.py' ]

object_size = 256 img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=False, with_label=False, with_mask=True), dict(type='LoadPatchData', object_size=object_size, patch_size = object_size), dict(type='Resize', img_scale=(object_size, object_size), keep_ratio=False), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['object_img', 'object_gt_masks', 'object_coarse_masks', 'patch_img', 'patch_gt_masks', 'patch_coarse_masks'])]

dataset_type = 'HRCollectionDataset' data_root = '/mmdetection/SegRefiner-main/data/' train_dataloader=dict( samples_per_gpu=6, workers_per_gpu=1) data = dict( delete=True, train=dict( type=dataset_type, pipeline=train_pipeline, data_root=data_root, collection_datasets=['vanyi'], collection_json=data_root + 'collection_vanyi.json'), train_dataloader=train_dataloader, val=dict(), test=dict())`

[yanrihong]

Great! I just want to test the performance of segrefiner, since i use mmsegmentation before, the dataloading is a really big problem for me, thank u for sharing how you do on your custom dataset, now i gonna check the model and see if it will occur the same problem as what you happen, 3Q

[yanrihong]

我也遇到了一样的问题，到最后IOU会直接变成0