Comments (4)
MasterBin-IIAU
commented on September 28, 2024
@LYMDLUT Hi, thanks for your appreciation of our work. 4T is definitely enough for storing all datasets. I think that 2T is also enough. The largest dataset we used is TrackingNet. The complete TrackingNet may consume huge space. However, since we only use 4 of 12 splits, the space needed has been largely reduced.
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LYMDLUT
commented on September 28, 2024
非常感谢，期待您在各平台的讲解！
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LYMDLUT
commented on September 28, 2024
期待您更加优秀的工作！
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MasterBin-IIAU
commented on September 28, 2024
@LYMDLUT Thanks for your appreciation :)
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Related Issues (20)
- List index out of range in convert_to_coco_format HOT 1
- I have a simple question. Why did you use transformer inside the code for mot tracking? As there is not much explanation about it inside the paper. HOT 1
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- [Code] The purpose of learnable broadcast sum in UnicornHead HOT 1
- Checkpoint for mot17 is missing HOT 3
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- on edge devices
- The unicorn_track_large_mot_challenge_mask model is still missing HOT 1
- 为什么不管怎么调bs，单机多卡训练的时候显存占用都不变 HOT 2
- BDD100K数据存在失效的下载链接 HOT 3
- MOT/MOTS推理问题
- where is the tools/trt.py?
- Question regarding input tensor preprocessing HOT 2
- python3.7Scikit-learn requires Python 3.8 or later. HOT 1
- What is the experimental setting in the paper? HOT 2
- SOT参数问题 HOT 2
- Cuda mismatch error when building module for ModuleNotFoundError: No module named 'MultiScaleDeformableAttention' HOT 4
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