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JialeCao001 / SipMask

SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation (ECCV2020)
https://arxiv.org/pdf/2007.14772.pdf
MIT License
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train on a custom dataset #26

Open amaanda opened 3 years ago

amaanda commented 3 years ago

hi! thanks for sharing your work :)

i am trying to train using a custom dataset, with only 1 class. i keep getting the following error, i believe it's because the number of classes is different from coco:

2020-11-10 18:53:53,782 - mmdet - INFO - load model from: open-mmlab://resnet50_caffe
2020-11-10 18:53:53,868 - mmdet - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: conv1.bias, layer1.0.conv1.bias, layer1.0.conv2.bias, layer1.0.conv3.bias, layer1.0.downsample.0.bias, layer1.1.conv1.bias, layer1.1.conv2.bias, layer1.1.conv3.bias, layer1.2.conv1.bias, layer1.2.conv2.bias, layer1.2.conv3.bias, layer2.0.conv1.bias, layer2.0.conv2.bias, layer2.0.conv3.bias, layer2.0.downsample.0.bias, layer2.1.conv1.bias, layer2.1.conv2.bias, layer2.1.conv3.bias, layer2.2.conv1.bias, layer2.2.conv2.bias, layer2.2.conv3.bias, layer2.3.conv1.bias, layer2.3.conv2.bias, layer2.3.conv3.bias, layer3.0.conv1.bias, layer3.0.conv2.bias, layer3.0.conv3.bias, layer3.0.downsample.0.bias, layer3.1.conv1.bias, layer3.1.conv2.bias, layer3.1.conv3.bias, layer3.2.conv1.bias, layer3.2.conv2.bias, layer3.2.conv3.bias, layer3.3.conv1.bias, layer3.3.conv2.bias, layer3.3.conv3.bias, layer3.4.conv1.bias, layer3.4.conv2.bias, layer3.4.conv3.bias, layer3.5.conv1.bias, layer3.5.conv2.bias, layer3.5.conv3.bias, layer4.0.conv1.bias, layer4.0.conv2.bias, layer4.0.conv3.bias, layer4.0.downsample.0.bias, layer4.1.conv1.bias, layer4.1.conv2.bias, layer4.1.conv3.bias, layer4.2.conv1.bias, layer4.2.conv2.bias, layer4.2.conv3.bias

i changed the config file with the path to my dataset, which is in coco format.

should i do something else or are this changes enough? apparently the training is running fine.

thanks in advance!

JialeCao001 commented 3 years ago

@amaanda Thanks for interests. Maybe num_classes should be 2, including objects and background.

amaanda commented 3 years ago

thanks for the quick reply! =) hm i see. my dataset contains only one class, do you consider background as a class inside your code or should i edit my json to include this class?

JialeCao001 commented 3 years ago

@amaanda Usually, donot need to change json. Just ensure that the gt class is one and background is zero during training.

amaanda commented 3 years ago

ok. thanks once again!

Traderain commented 3 years ago

@JialeCao001 Hey I am having the same issue but I my class number seems to be correct and the json seems file. My config:

# model settings
model = dict(
    type='SipMask',
    pretrained='open-mmlab://resnet50_caffe',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=False),
        style='caffe'),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs=True,
        extra_convs_on_inputs=False,  # use P5
        num_outs=5,
        relu_before_extra_convs=True),
    bbox_head=dict(
        type='SipMaskHead',
        num_classes=2,
        in_channels=256,
        stacked_convs=3,
        feat_channels=256,
        strides=[8, 16, 32, 64, 128],
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='IoULoss', loss_weight=1.0),
        loss_centerness=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        center_sampling=True,
        center_sample_radius=1.5))
# training and testing settings
train_cfg = dict(
    assigner=dict(
        type='MaxIoUAssigner',
        pos_iou_thr=0.5,
        neg_iou_thr=0.4,
        min_pos_iou=0,
        ignore_iof_thr=-1),
    allowed_border=-1,
    pos_weight=-1,
    debug=False)
test_cfg = dict(
    nms_pre=200,
    min_bbox_size=0,
    score_thr=0.03,
    nms=dict(type='nms', iou_thr=0.5),
    max_per_img=10)
# dataset settings
dataset_type = 'CocoDataset'
data_root = './dateset/blob/'
img_norm_cfg = dict(
    mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
data = dict(
    imgs_per_gpu=8,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'train/annotations.json_coco.json',
        img_prefix=data_root + 'train/',
        img_scale=(1200, 1200),
        img_norm_cfg=img_norm_cfg,
        size_divisor=32,
        flip_ratio=0.5,
        with_mask=True,
        with_crowd=False,
        with_label=True,
        with_track=True
    ),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'valid/annotations.json_coco.json',
        img_prefix=data_root + 'valid/',
        img_scale=(1200, 1200),
        img_norm_cfg=img_norm_cfg,
        size_divisor=32,
        flip_ratio=0,
        with_mask=True,
        with_crowd=False,
        with_label=True
    ),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'test/annotations.json_coco.json',
        img_prefix=data_root + 'test/',
        img_scale=(1200, 1200),
        img_norm_cfg=img_norm_cfg,
        size_divisor=32,
        flip_ratio=0,
        with_mask=False,
        with_label=False,
        test_mode=True
    )
)

# optimizer
optimizer = dict(
    type='SGD',
    lr=0.005,
    momentum=0.9,
    weight_decay=0.0001,
    paramwise_options=dict(bias_lr_mult=2., bias_decay_mult=0.))
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=1000,
    warmup_ratio=1.0 / 80,
    step=[8, 11])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook'),
        # dict(type='TensorboardLoggerHook')
    ])
# yapf:enable
# runtime settings
total_epochs = 12
device_ids = [0]
#dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './dataset/blob/result/'
load_from = './models/vis_sipmask_ms_1x_final.pth'
resume_from = None
workflow = [('train', 1)]

I am tryinng on a single gpu:

        {
            "name": "Train model",
            "type": "python",
            "request": "launch",
            "program": "${workspaceFolder}\\tools\\train.py",
            "console": "integratedTerminal",
            "args": [
                "configs\\sipmask\\sipmask_r50_caffe_fpn_gn_ms_1x_blob.py",
                "--validate",
                "--launcher=none"
            ]
        },

Output:

(sip-mask-test) PS D:\Repos\SipMask\SipMask-VIS> python tools\train.py configs\sipmask\sipmask_r50_caffe_fpn_gn_ms_1x_blob.py --validate --launcher=none
2020-11-19 15:06:37,023 - INFO - Distributed training: False
2020-11-19 15:06:37,359 - INFO - load model from: open-mmlab://resnet50_caffe
2020-11-19 15:06:37,436 - WARNING - unexpected key in source state_dict: conv1.bias, layer1.0.downsample.0.bias, layer1.0.conv1.bias, layer1.0.conv2.bias, layer1.0.conv3.bias, layer1.1.conv1.bias, layer1.1.conv2.bias, layer1.1.conv3.bias, layer1.2.conv1.bias, layer1.2.conv2.bias, layer1.2.conv3.bias, layer2.0.downsample.0.bias, layer2.0.conv1.bias, layer2.0.conv2.bias, layer2.0.conv3.bias, layer2.1.conv1.bias, layer2.1.conv2.bias, layer2.1.conv3.bias, layer2.2.conv1.bias, layer2.2.conv2.bias, layer2.2.conv3.bias, layer2.3.conv1.bias, layer2.3.conv2.bias, layer2.3.conv3.bias, layer3.0.downsample.0.bias, layer3.0.conv1.bias, layer3.0.conv2.bias, layer3.0.conv3.bias, layer3.1.conv1.bias, layer3.1.conv2.bias, layer3.1.conv3.bias, layer3.2.conv1.bias, layer3.2.conv2.bias, layer3.2.conv3.bias, layer3.3.conv1.bias, layer3.3.conv2.bias, layer3.3.conv3.bias, layer3.4.conv1.bias, layer3.4.conv2.bias, layer3.4.conv3.bias, layer3.5.conv1.bias, layer3.5.conv2.bias, layer3.5.conv3.bias, layer4.0.downsample.0.bias, layer4.0.conv1.bias, layer4.0.conv2.bias, layer4.0.conv3.bias, layer4.1.conv1.bias, layer4.1.conv2.bias, layer4.1.conv3.bias, layer4.2.conv1.bias, layer4.2.conv2.bias, layer4.2.conv3.bias

missing keys in source state_dict: layer2.0.bn1.num_batches_tracked, layer3.0.downsample.1.num_batches_tracked, layer2.1.bn3.num_batches_tracked, layer1.0.bn3.num_batches_tracked, bn1.num_batches_tracked, layer1.2.bn2.num_batches_tracked, layer2.2.bn3.num_batches_tracked, layer3.4.bn2.num_batches_tracked, layer1.0.downsample.1.num_batches_tracked, layer4.0.downsample.1.num_batches_tracked, layer3.3.bn1.num_batches_tracked, layer3.4.bn3.num_batches_tracked, layer3.4.bn1.num_batches_tracked, layer3.2.bn2.num_batches_tracked, layer4.2.bn2.num_batches_tracked, layer2.1.bn1.num_batches_tracked, layer1.2.bn3.num_batches_tracked, layer2.0.downsample.1.num_batches_tracked, layer3.5.bn1.num_batches_tracked, layer2.0.bn2.num_batches_tracked, layer3.1.bn2.num_batches_tracked, layer2.2.bn2.num_batches_tracked, layer3.0.bn2.num_batches_tracked, layer4.1.bn3.num_batches_tracked, layer3.0.bn1.num_batches_tracked, layer2.1.bn2.num_batches_tracked, layer1.0.bn2.num_batches_tracked, layer4.0.bn1.num_batches_tracked, layer4.1.bn1.num_batches_tracked, layer2.3.bn2.num_batches_tracked, layer1.1.bn2.num_batches_tracked, layer3.2.bn3.num_batches_tracked, layer3.0.bn3.num_batches_tracked, layer4.0.bn2.num_batches_tracked, layer4.2.bn1.num_batches_tracked, layer2.3.bn3.num_batches_tracked, layer1.1.bn1.num_batches_tracked, layer4.2.bn3.num_batches_tracked, layer2.2.bn1.num_batches_tracked, layer4.0.bn3.num_batches_tracked, layer3.1.bn3.num_batches_tracked, layer2.0.bn3.num_batches_tracked, layer1.1.bn3.num_batches_tracked, layer1.2.bn1.num_batches_tracked, layer3.1.bn1.num_batches_tracked, layer3.2.bn1.num_batches_tracked, layer3.5.bn3.num_batches_tracked, layer4.1.bn2.num_batches_tracked, layer3.5.bn2.num_batches_tracked, layer3.3.bn3.num_batches_tracked, layer3.3.bn2.num_batches_tracked, layer2.3.bn1.num_batches_tracked, layer1.0.bn1.num_batches_tracked

Traceback (most recent call last):
  File "tools\train.py", line 110, in <module>
    main()
  File "tools\train.py", line 88, in main
    datasets = [build_dataset(cfg.data.train)]
  File "D:\Repos\SipMask\SipMask-VIS\tools\..\mmdet\datasets\builder.py", line 39, in build_dataset
    dataset = build_from_cfg(cfg, DATASETS, default_args)
  File "D:\Repos\SipMask\SipMask-VIS\tools\..\mmdet\utils\registry.py", line 74, in build_from_cfg
    return obj_type(**args)
TypeError: __init__() got an unexpected keyword argument 'with_track'
(sip-mask-test) PS D:\Repos\SipMask\SipMask-VIS>

What am I doing wrong? Thanks in advance.

Update: It seems some of my settings in the CFG were wrong, still I get a new error which I can't make sense of:

(sip-mask-test) PS D:\Repos\SipMask\SipMask-VIS> python tools\train.py configs\sipmask\sipmask_r50_caffe_fpn_gn_ms_1x_blob.py --validate --launcher=none
2020-11-19 16:23:45,230 - INFO - Distributed training: False
2020-11-19 16:23:45,518 - INFO - load model from: open-mmlab://resnet50_caffe
2020-11-19 16:23:45,585 - WARNING - unexpected key in source state_dict: conv1.bias, layer1.0.downsample.0.bias, layer1.0.conv1.bias, layer1.0.conv2.bias, layer1.0.conv3.bias, layer1.1.conv1.bias, layer1.1.conv2.bias, layer1.1.conv3.bias, layer1.2.conv1.bias, layer1.2.conv2.bias, layer1.2.conv3.bias, layer2.0.downsample.0.bias, layer2.0.conv1.bias, layer2.0.conv2.bias, layer2.0.conv3.bias, layer2.1.conv1.bias, layer2.1.conv2.bias, layer2.1.conv3.bias, layer2.2.conv1.bias, layer2.2.conv2.bias, layer2.2.conv3.bias, layer2.3.conv1.bias, layer2.3.conv2.bias, layer2.3.conv3.bias, layer3.0.downsample.0.bias, layer3.0.conv1.bias, layer3.0.conv2.bias, layer3.0.conv3.bias, layer3.1.conv1.bias, layer3.1.conv2.bias, layer3.1.conv3.bias, layer3.2.conv1.bias, layer3.2.conv2.bias, layer3.2.conv3.bias, layer3.3.conv1.bias, layer3.3.conv2.bias, layer3.3.conv3.bias, layer3.4.conv1.bias, layer3.4.conv2.bias, layer3.4.conv3.bias, layer3.5.conv1.bias, layer3.5.conv2.bias, layer3.5.conv3.bias, layer4.0.downsample.0.bias, layer4.0.conv1.bias, layer4.0.conv2.bias, layer4.0.conv3.bias, layer4.1.conv1.bias, layer4.1.conv2.bias, layer4.1.conv3.bias, layer4.2.conv1.bias, layer4.2.conv2.bias, layer4.2.conv3.bias

missing keys in source state_dict: layer1.1.bn2.num_batches_tracked, layer3.5.bn2.num_batches_tracked, layer4.1.bn2.num_batches_tracked, layer1.0.downsample.1.num_batches_tracked, layer3.0.downsample.1.num_batches_tracked, layer1.2.bn2.num_batches_tracked, layer3.5.bn3.num_batches_tracked, layer3.3.bn1.num_batches_tracked, layer2.3.bn1.num_batches_tracked, layer2.0.bn3.num_batches_tracked, layer1.1.bn1.num_batches_tracked, bn1.num_batches_tracked, layer4.0.downsample.1.num_batches_tracked, layer4.2.bn3.num_batches_tracked, layer1.0.bn2.num_batches_tracked, layer3.2.bn2.num_batches_tracked, layer2.2.bn2.num_batches_tracked, layer3.0.bn3.num_batches_tracked, layer3.0.bn1.num_batches_tracked, layer1.0.bn3.num_batches_tracked, layer2.2.bn1.num_batches_tracked, layer4.2.bn1.num_batches_tracked, layer1.0.bn1.num_batches_tracked, layer3.4.bn1.num_batches_tracked, layer1.1.bn3.num_batches_tracked, layer4.2.bn2.num_batches_tracked, layer3.4.bn3.num_batches_tracked, layer3.1.bn2.num_batches_tracked, layer3.2.bn1.num_batches_tracked, layer1.2.bn3.num_batches_tracked, layer2.0.downsample.1.num_batches_tracked, layer3.5.bn1.num_batches_tracked, layer3.0.bn2.num_batches_tracked, layer4.1.bn3.num_batches_tracked, layer2.0.bn1.num_batches_tracked, layer3.1.bn1.num_batches_tracked, layer2.1.bn2.num_batches_tracked, layer3.3.bn3.num_batches_tracked, layer4.1.bn1.num_batches_tracked, layer4.0.bn3.num_batches_tracked, layer1.2.bn1.num_batches_tracked, layer3.1.bn3.num_batches_tracked, layer3.2.bn3.num_batches_tracked, layer2.1.bn3.num_batches_tracked, layer4.0.bn1.num_batches_tracked, layer3.3.bn2.num_batches_tracked, layer4.0.bn2.num_batches_tracked, layer2.0.bn2.num_batches_tracked, layer2.3.bn3.num_batches_tracked, layer2.2.bn3.num_batches_tracked, layer3.4.bn2.num_batches_tracked, layer2.1.bn1.num_batches_tracked, layer2.3.bn2.num_batches_tracked

loading annotations into memory...
Done (t=0.02s)
creating index...
index created!
Starting non distributed training
2020-11-19 16:23:47,221 - INFO - Start running, host: hamba@DESKTOP-AV82M29, work_dir: D:\Repos\SipMask\SipMask-VIS\dataset\blob\result
2020-11-19 16:23:47,222 - INFO - workflow: [('train', 1)], max: 12 epochs
Traceback (most recent call last):
  File "tools\train.py", line 110, in <module>
    main()
  File "tools\train.py", line 106, in main
    logger=logger)
  File "D:\Repos\SipMask\SipMask-VIS\tools\..\mmdet\apis\train.py", line 61, in train_detector
    _non_dist_train(model, dataset, cfg, validate=validate)
  File "D:\Repos\SipMask\SipMask-VIS\tools\..\mmdet\apis\train.py", line 222, in _non_dist_train
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
  File "D:\ProgramData\Miniconda3\envs\sip-mask-test\lib\site-packages\mmcv\runner\runner.py", line 358, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "D:\ProgramData\Miniconda3\envs\sip-mask-test\lib\site-packages\mmcv\runner\runner.py", line 260, in train
    for i, data_batch in enumerate(data_loader):
  File "C:\Users\hamba\AppData\Roaming\Python\Python37\site-packages\torch\utils\data\dataloader.py", line 363, in __next__
    data = self._next_data()
  File "C:\Users\hamba\AppData\Roaming\Python\Python37\site-packages\torch\utils\data\dataloader.py", line 989, in _next_data
    return self._process_data(data)
  File "C:\Users\hamba\AppData\Roaming\Python\Python37\site-packages\torch\utils\data\dataloader.py", line 1014, in _process_data
    data.reraise()
  File "C:\Users\hamba\AppData\Roaming\Python\Python37\site-packages\torch\_utils.py", line 395, in reraise
    raise self.exc_type(msg)
Exception: Caught Exception in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "C:\Users\hamba\AppData\Roaming\Python\Python37\site-packages\torch\utils\data\_utils\worker.py", line 185, in _worker_loop
    data = fetcher.fetch(index)
  File "C:\Users\hamba\AppData\Roaming\Python\Python37\site-packages\torch\utils\data\_utils\fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "C:\Users\hamba\AppData\Roaming\Python\Python37\site-packages\torch\utils\data\_utils\fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "D:\Repos\SipMask\SipMask-VIS\tools\..\mmdet\datasets\custom.py", line 181, in __getitem__
    data = self.prepare_train_img(idx)
  File "D:\Repos\SipMask\SipMask-VIS\tools\..\mmdet\datasets\custom.py", line 215, in prepare_train_img
    ann = self.get_ann_info(idx)
  File "D:\Repos\SipMask\SipMask-VIS\tools\..\mmdet\datasets\coco.py", line 45, in get_ann_info
    return self._parse_ann_info(ann_info, self.with_mask)
  File "D:\Repos\SipMask\SipMask-VIS\tools\..\mmdet\datasets\coco.py", line 93, in _parse_ann_info
    gt_masks.append(self.coco.annToMask(ann))
  File "D:\ProgramData\Miniconda3\envs\sip-mask-test\lib\site-packages\pycocotools\coco.py", line 439, in annToMask
    rle = self.annToRLE(ann)
  File "D:\ProgramData\Miniconda3\envs\sip-mask-test\lib\site-packages\pycocotools\coco.py", line 424, in annToRLE
    rles = maskUtils.frPyObjects(segm, h, w)
  File "pycocotools\_mask.pyx", line 307, in pycocotools._mask.frPyObjects
Exception: input type is not supported.

(sip-mask-test) PS D:\Repos\SipMask\SipMask-VIS>

It seems the issue is that my data is in polygon format, is there a way to train with this? If not how can I convert this to RLE?

Traderain commented 3 years ago

I managed to convert my data to RLE but I cannot train the modell still.

Exception has occurred: TypeError
forward_train() missing 3 required positional arguments: 'ref_img', 'ref_bboxes', and 'gt_pids'
  File "D:\Repos\SipMask\SipMask-VIS\mmdet\models\detectors\base.py", line 86, in forward
    return self.forward_train(img, img_meta, **kwargs)
  File "D:\Repos\SipMask\SipMask-VIS\mmdet\core\fp16\decorators.py", line 49, in new_func
    return old_func(*args, **kwargs)
  File "D:\Repos\SipMask\SipMask-VIS\mmdet\apis\train.py", line 38, in batch_processor
    losses = model(**data)
  File "D:\Repos\SipMask\SipMask-VIS\mmdet\apis\train.py", line 221, in _non_dist_train
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
  File "D:\Repos\SipMask\SipMask-VIS\mmdet\apis\train.py", line 60, in train_detector
    _non_dist_train(model, dataset, cfg, validate=validate)
  File "D:\Repos\SipMask\SipMask-VIS\tools\train.py", line 106, in main
    logger=logger)
  File "D:\Repos\SipMask\SipMask-VIS\tools\train.py", line 110, in <module>
    main()

@JialeCao001 Any ideas?

JialeCao001 commented 3 years ago

@Traderain I cannot get your point. What is your final error?

Traderain commented 3 years ago

I managed to convert my data to RLE but I cannot train the modell still.

Exception has occurred: TypeError
forward_train() missing 3 required positional arguments: 'ref_img', 'ref_bboxes', and 'gt_pids'
  File "D:\Repos\SipMask\SipMask-VIS\mmdet\models\detectors\base.py", line 86, in forward
    return self.forward_train(img, img_meta, **kwargs)
  File "D:\Repos\SipMask\SipMask-VIS\mmdet\core\fp16\decorators.py", line 49, in new_func
    return old_func(*args, **kwargs)
  File "D:\Repos\SipMask\SipMask-VIS\mmdet\apis\train.py", line 38, in batch_processor
    losses = model(**data)
  File "D:\Repos\SipMask\SipMask-VIS\mmdet\apis\train.py", line 221, in _non_dist_train
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
  File "D:\Repos\SipMask\SipMask-VIS\mmdet\apis\train.py", line 60, in train_detector
    _non_dist_train(model, dataset, cfg, validate=validate)
  File "D:\Repos\SipMask\SipMask-VIS\tools\train.py", line 106, in main
    logger=logger)
  File "D:\Repos\SipMask\SipMask-VIS\tools\train.py", line 110, in <module>
    main()

@JialeCao001 Any ideas?

@JialeCao001 This is the final issue. My annotations are ms coco (rle)

JialeCao001 commented 3 years ago

@Traderain dataset_type = 'CocoDataset' should be changed to dataset_type = 'YTVOSDataset'

Traderain commented 3 years ago

So this cant be trained with ms coco?

On 2020. Dec 4., Fri at 2:56, Jiale Cao notifications@github.com wrote:

@Traderain https://github.com/Traderain dataset_type = 'CocoDataset' should be changed to dataset_type = 'YTVOSDataset'

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/JialeCao001/SipMask/issues/26#issuecomment-738504604, or unsubscribe https://github.com/notifications/unsubscribe-auth/ABQ5LVFAN3BOJW4IHSQRGVTSTA6UVANCNFSM4TRBJVMA .

JialeCao001 commented 3 years ago

@Traderain For image instance segmentation, you can use coco-style annotations with SipMask-benchmark or SipMask-mmdet. For video instance segmentation, Sipmask-VIS only supports the yotube-vis style annotations.