Comments (7)
Hello. The format of Foggy Cityscapes dataset is the same as Cityscapes. You can refer to cityscapes_to_yolo.py to process foggy version of cityscapes :)
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Hi,
You can refer to code here for selecting a specific number of target domain dataset. And use the selected sub-dataset for training :)
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Hi! I find I probably misunderstood your requirement :( I randomly select 8 images across the whole dataset. The samples are not choiced by classes.
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Hi,
Yes you are right!!
This is what I wanted to know that whether you have chosen images based upon the classes or completely random. Thanks alot for reconsidering my question. It is of great help to me. But since I am running your code on a custom dataset so I have few question, please guide me in this.
if completely random 8-images set is chosen, than is there any such assumption that every sample selected must be having all the instances of the classes in the dataset or at least there are few instances of all the classes in chosen 8-images collectively?
If above assumption is not made, and there are chances of missing a particular class in target 8-image set, then how do you make up for an unseen class in test data?
Thanks!
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Yeah. I practically met the same problem.
I tried to solve this problem by manually selecting the images to comprise each target class sampled.
But, I think it's ok that some target classes are missing since the main problem is the domain gap, and the training pipeline uses the source data, which has all of the classes during training.
You can try both methods and share the results here to help us better understand this task thx :)
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ok that sounds cool now!!!
Sure I will share the results.
Thanks alot for helping me!
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Related Issues (18)
- Plz provide the tools and instructions for processing the raw data, thanks! HOT 7
- 训练自己数据集
- Hello, could you please provide a usable visualization code for TSNE? HOT 3
- How can we get voc dataset? HOT 2
- 数据集在哪啊,大佬请指教 HOT 2
- Domain-aware Augmentation HOT 10
- Training Error! HOT 6
- Hand crafted transformation matrix A, in code HOT 1
- class setting in config file
- Data Augmentation Sampling Strategy HOT 1
- 生成邻域数据的代码
- 转到yolov7 HOT 6
- Box level augmentaion HOT 1
- ❓ Question
- S->C cross-domain few-shot HOT 1
- question HOT 2
- 导出模型 HOT 1
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