Comments (9)
Hi, token labeling can improve volo_d1 by 0.4% (from 83.8% to 84.2%). For larger volo models, token labeling can achieve improvement from 0.0 to 0.4%.
from volo.
Hi @Ree1s , You can refer to zihangJiang/TokenLabeling#7 (comment) for generating token label data for your own dataset.
from volo.
Hi,thank you for sharing this work. If I want to use VOLO to finetune on my own dataset, how can I create my own token labeling?
If you only want to 'finetune' VOLO on your own dataset, one option is to directly use the 'class token' or mean the feature tokens without using token labeling, thus you don't need to generate your own token labels.
from volo.
Thank you, I think these numbers are probably worth adding to the paper as well for context.
from volo.
Hi, token labeling can improve volo_d1 by 0.4% (from 83.8% to 84.2%). For larger volo models, token labeling can achieve improvement from 0.0 to 0.4%.
Could you please also put a copy of token labeling data and pretrained models on Baidu Netdisk?
from volo.
Hi,thank you for sharing this work. If I want to use VOLO to finetune on my own dataset, how can I create my own token labeling?
from volo.
Hi @Ree1s , You can refer to zihangJiang/TokenLabeling#7 (comment) for generating token label data for your own dataset.
Thanks, and I will try on my dataset through this way mentioned.
from volo.
Hi,thank you for sharing this work. If I want to use VOLO to finetune on my own dataset, how can I create my own token labeling?
If you only want to 'finetune' VOLO on your own dataset, one option is to directly use the 'class token' or mean the feature tokens without using token labeling, thus you don't need to generate your own token labels.
I'll have a try. Thanks a lot!
from volo.
Hi,thank you for sharing this work. If I want to use VOLO to finetune on my own dataset, how can I create my own token labeling?
If you only want to 'finetune' VOLO on your own dataset, one option is to directly use the 'class token' or mean the feature tokens without using token labeling, thus you don't need to generate your own token labels.
Hi, when you say class token, do you mean configure return_mean
to True
the feeding parameters of VOLO?
from volo.
Related Issues (20)
- Halonets declare a SOTA in CVPR, any contribution to conference?
- Some thoughts about volo
- Colab Notebook doesn't work & gives wrong results. HOT 4
- Finetune with 512 image size HOT 2
- Balanced class weight HOT 1
- Will you release the codes for Semantic Segmentation?
- AttributeError: 'tuple' object has no attribute 'log_softmax' HOT 2
- Could you share the training script for training volo_d5 ↑512?
- run_error HOT 1
- SystemError: returned NULL without setting an error
- May you provide the FLOPs calculation script ?
- size mismatch for pos_embed: copying a param with shape torch.Size([1, 14, 14, 768]) from checkpoint, the shape in current model is torch.Size([1, 14, 14, 512]). HOT 1
- RuntimeError: The size of tensor a (28) must match the size of tensor b (14) at non-singleton dimension 2 HOT 2
- is the order of v_pj(x) result in algoritm1 correct ?
- The code for Semantic Segmentation?
- When training own dataset, an error occurs when changing numberclasses to the corresponding category. If it is the default, it will report an error
- volo-d1 training without token label data HOT 1
- pre trained model file is broken. HOT 1
- Question about computational complexity formulation of Outlooker Attention
- Ablation study and official code problem
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