zhangyuanhan-ai / bamboo Goto Github PK
View Code? Open in Web Editor NEWBamboo: 4 times larger than ImageNet; 2 time larger than Object365; Built by active learning.
Bamboo: 4 times larger than ImageNet; 2 time larger than Object365; Built by active learning.
How to download full bamboo dataset?
Multiple applications have been submitted within a year, but no response has been received. The website link is also disappear.
Hi, I want to try Bamboo-Det, but it seems that detectron2 needs a configuration file (.yaml) as well as a weight file (.pkl). I can only find the .pkl file from the link you provided. Where can I find the .yaml file?
The explore website you posted in the project failed.
https://opengvlab.shlab.org.cn/bamboo/home
Would you update it later? I would like to get sample images of curtain categories in a convenient way.
Great work.
Where can I access the 809 common concepts mentioned in the paper? The 'id2name' in the label system has 304048 concepts, while i am more interested in those 809 common concepts. Hope you can help me!
Hi, I've read the paper about Bamboo. It is mentioned that Bamboo-CLS contains ~68M images and Bamboo-DET contains ~3M images. Is Bamboo-DET contained in Bamboo-CLS? I've downloaded all images of Bamboo-CLS, so what I need for Bamboo-DET is only the box-level annotation file?
I think the Bamboo is a great job. And I find the label system of Bamboo in README, also find the classification-demo on https://huggingface.co/spaces/CVPR/Bamboo_ViT-B16_demo, and I think it will be useful for me to build a subclass-chinese-classification system
I have a question, how to map the class-name of 115217-cls-model on huggingface into Bamboo-label-system:
for example,bamboo['id2name_zh'][node_id]
can get the chinese-class-name, and I can get thee class-name of the classification by trainid2name.json. There is not a mapping between bamboo and trainid2name
Thanks!
hi, why has no class of 'head','face' in detect labels。 i think the two classes are so inportant .
Great work.
However, I can't find the Supplementary material you mentioned in the description of Fig.3. Can you provide me with the link to download it?
On the other hand, I have a question about how you do Data annotation (Sec 4.1.5) for object detection task:
It seems that the annotator just needs to tell the system if the prediction is right or not. Does that mean an image is valid only if all interesting objects are correctly detected?
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