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License: MIT License
Hi, I have two simple questions.
Hi, I have some confusion about the versions of the mapillary dataset.
The Mapillary Vistas Dataset is a large-scale street-level image dataset containing 25,000 high-resolution images annotated into 66/124 object categories of which 37/70 classes are instance-specific labels (v.1.2 and v2.0, respectively).
_PREDEFINED_SPLITS_MAP['map'] = {"map_val": ("path/mapillary-vistas/mapillary-vistas-dataset_public_v2.0/validation/images",
First of all, thank you for your research.
I have analyzed the provided UVO_frame_val_exist.json, and it appears that evaluation is being conducted on 742 videos. However, the original UVO_sparse dataset's validation json contains information for 2452 videos. I'm curious about the criteria for evaluating only 742 videos, and if there's something I might have missed.
Thank you.
Hi,
I notice that the performance of Mask-RCNN-P is very similar to the vanilla Mask-RCNN in table 1. Is there any reason why the pseudo label method like OWOD didn't work?
File "/home/subinyi/anaconda3/envs/ldet/lib/python3.7/site-packages/detectron2/modeling/box_regression.py", line 327, in _dense_box_regression_loss
anchors = type(anchors[0]).cat(anchors).tensor # (R, 4)
AttributeError: type object 'Tensor' has no attribute 'cat'
i dont know how to solve this issue? can you help me
Hi,
I notice that you update the README and said there is an evaluation code bug. Could you plz give more information about this?
Hi,
Would you plz give some instructions on how to visualize the LDET results?
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