Comments (4)
why do you want to finetune on 1k ? i dont really see the point
anyway, similar to standard 1k training, only with lower learning rate, and you can try adam instead SGD.
from imagenet21k.
Thanks. I want to reproduce the results in the table.
The finetune result on Imagenet-1K is only with lower learning rate and adam?
from imagenet21k.
basically yes (compared to standard 21K training)
from imagenet21k.
@pawopawo have you reproduced the acc?
@mrT23 In the paper, Table 3, the imagenet21k pretrained model were obtained using single label or semantic softmax training strategy? Did the official site mention if they actually use which strategy? Or by default people are all using semantic softmax way?
from imagenet21k.
Related Issues (20)
- Is the 1k validation set included in the 21k data? HOT 2
- About hierarchy balancing HOT 2
- No parent for n09450163 (sun)
- Any label map for ImageNet-1K?
- could you please provide image-label map directly?
- When using your ImageNet21K pretrained ResNet50 model in Detectron2, performance degrades HOT 1
- I see no normalization of images.
- Hyperparameters to finetune ResNet50 from IN21k to IN1k
- Missing details on Dropout and momentum value used for SGD when fine tuning on ImageNet1k
- What is the teacher model when using semantic softmax with KD?
- Where can I find the data?
- Does there any decriptions abount classes in "imagenet21k_small_classes"
- Anyone here have trouble reaching the mentioned accuracy for ViT-B?
- How to test Imagenet1K with pretrained backbone MobilenetV3_large_100? Could you release the testing script? Thanks a lot.
- ask for the pretrained model on ImageNet-21k-P from single label
- ask for the Transfer Learning Code of train.py on cifar100
- Dependencies to run code
- CIFAR-100 pretrained ViT-B-16 weight HOT 2
- winter21
- 请教一下,Winter21 Imagenet21k在国内服务器上如何才能快速下载? HOT 1
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from imagenet21k.