uvo_challenge's People
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jozhang97 junyongyou sporterman yjingyu wzb1005 majx1997 yscheung liangshaohua clw5180 fenglian425 zhongqiu1245uvo_challenge's Issues
About the mask annotation used 'box2seg.py' and '../_base_/datasets/uvo_finetune.py'
Hi, @dulucas ,
In these two config files, many "mask annotations" are used, like in this:
oid_train= dict(
type='RepeatDataset',
times=1,
dataset=dict(
type=dataset_type,
data_root='data/oid/',
img_dir = 'images/',
ann_dir = 'masks/',
split = ['train_clean_v2.txt', ],
pipeline=train_pipeline
)
)
and :
uvo_dense_val = dict(
type='RepeatDataset',
times=1,
dataset=dict(
type=dataset_type,
data_root='data/uvo/',
img_dir = 'images/dense_val/',
ann_dir = 'masks/dense_val/',
split = ['dense_val_list.txt', ],
pipeline=train_pipeline
)
)
And I tried the code like this(https://github.com/alicranck/coco2voc)(url) to generate the mask pngs, but there maybe something wrong with the generated masks, the training losses are unusual.
Could you please provide the code or scripts that you used to generate the mask? or giving a link you are referred?
Loop call get_targets?
glue_masks_w_flow.py
The pretrained model can not be download
Hello, the pretrained model can not be download
How do you expect to use these Config and what is your replay process
Can you explain your training process in detail? Because I see you have a lot of config files, I don't know which ones to use and how to use them
the coco pretrained UVO_Detector weight's url can't be opened
can you share it one more time ? Thanks a lot!
Init segmentor config file problem
Hi! Thanks for the code!
I tried to use your config_file in segmentation dir, but got an error. It seems in config.py there is no "type" key. I'm not familiar with openmmlab. Could you help me figure it out? Thanks a lot!
Script:
config_file = "./segmentation/configs/swin/swin_l_upper_w_jitter_inference.py"
ckpt_file = "../../models/seg_swin_l_uvo_finetuned.pth"
model = init_segmentor(config_file, ckpt_file, device="cuda:0")
Log:
Traceback (most recent call last):
File "infer.py", line 20, in <module>
model = init_segmentor(config_file, ckpt_file, device="cuda:0")
File "/home/xin/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmseg/apis/inference.py", line 32, in init_segmentor
model = build_segmentor(config.model, test_cfg=config.get('test_cfg'))
File "/home/xin/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmseg/models/builder.py", line 49, in build_segmentor
cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg))
File "/home/xin/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmcv/utils/registry.py", line 212, in build
return self.build_func(*args, **kwargs, registry=self)
File "/home/xin/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmcv/cnn/builder.py", line 27, in build_model_from_cfg
return build_from_cfg(cfg, registry, default_args)
File "/home/xin/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmcv/utils/registry.py", line 25, in build_from_cfg
'`cfg` or `default_args` must contain the key "type", '
KeyError: '`cfg` or `default_args` must contain the key "type", but got {\'pretrained\': None, \'backbone\': {\'pretrain_img_size\': 384, \'embed_dims\': 192, \'depths\': [2, 2, 18, 2], \'num_heads\': [6, 12, 24, 48], \'drop_path_rate\': 0.2, \'window_size\': 12}, \'decode_head\': {\'in_channels\': [192, 384, 768, 1536], \'num_classes\': 2, \'loss_decode\': {\'type\': \'CrossEntropyLoss\', \'use_sigmoid\': False, \'loss_weight\': 1.0}}, \'auxiliary_head\': {\'in_channels\': 768, \'num_classes\': 2, \'loss_decode\': {\'type\': \'CrossEntropyLoss\', \'use_sigmoid\': False, \'loss_weight\': 1.0}}, \'train_cfg\': None}\n{\'train_cfg\': None, \'test_cfg\': None}'
AttributeError: 'ConfigDict' object has no attribute 'fusion_cfg
checkpoint seem broken.
this checkpoint(seg_swin_l_mixed_pretrained.pth) seem broken.
import torch
torch.load('seg_swin_l_mixed_pretrained.pth')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/chen/miniconda3/envs/sota/lib/python3.9/site-packages/torch/serialization.py", line 600, in load
with _open_zipfile_reader(opened_file) as opened_zipfile:
File "/home/chen/miniconda3/envs/sota/lib/python3.9/site-packages/torch/serialization.py", line 242, in __init__
super(_open_zipfile_reader, self).__init__(torch._C.PyTorchFileReader(name_or_buffer))
RuntimeError: PytorchStreamReader failed reading zip archive: failed finding central directory
Question regrading the UVO Dataset
Firstly, Thank You for your amazing work.
There’s a question I would like to ask regarding the UVO Dataset. I would like to work on the dense dataset. Through the download link in the homepage of the challenge, I could access ‘UVO_video_train_dense.json’, ‘UVO_video_val_dense.json’ and ‘UVO_video_test_dense.json’ that specify the video ids of the train, validation and test datasets. However, I’m unsure how I could obtain the videos. Could you please guide me on how I could obtain the original and annotated videos ?
Many thanks
Where is this "resnet" imported from?
Which data split was the final result coming from
Hello! Many thanks for this nice GitHub repo. I am wondering on which data split did you evaluate your model. Whether it was UVO sparse test
or UVO dense test
? In the paper I saw the statement of Challenge final results on UVO-Sparse test dataset
. But the testing script in Github code loaded test annotation from the dense split. I guess dense split's test set was what the challenge targeted?
关于track2
wgts links failed
Hi!
Sorry to bother! The links to download weights failed.
Can you offer model weights ? I'm seeking for object detection model.
Thank you!
BR!
George.
KeyError: 'StageCascadeRPNHead is already registered in models'
I'm a newer mmdetection
How to run the segmentation demo?
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