Comments (15)
Could you provide the code of how to get the multi-label of each image according to words.txt file
all the transformation and semantic data can be found in the resource file. make sure you review it thoroughly
https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/resources/winter21/imagenet21k_miil_tree.pth
you are also welcome to validate them via the link @cissoidx sent (that was my source for generating the tree)
from imagenet21k.
review thoroughly https://github.com/Alibaba-MIIL/ImageNet21K/blob/main/train_semantic_softmax.py file.
specifically the conversion is done in
line 20
from imagenet21k.
Does the multi-label here refer to the multi-label of the same kind of the sematic tree, such as animal-dog-border_collie? But if a picture contains multiple different types of labels, such as animal-dog-border_collie, human-woman-young_woman, how to solve this problem?
from imagenet21k.
So you extract the meaning of each category in imagenet22k, and then use wordnet to construct a semantic tree. Finally, the semantic tree is used to generate multi-label information for each category, that is, the multi-label information of all images in each directory of imagenet22k is the same.
from imagenet21k.
So you extract the meaning of each category in imagenet22k, and then use wordnet to construct a semantic tree. Finally, the semantic tree is used to generate multi-label information for each category, that is, the multi-label information of all images in each directory of imagenet22k is the same.
i did not understand if there is a question or not. your description seems correct
from imagenet21k.
Does the multi-label here refer to the multi-label of the same kind of the sematic tree, such as animal-dog-border_collie? But if a picture contains multiple different types of labels, such as animal-dog-border_collie, human-woman-young_woman, how to solve this problem?
it won't. the base tagging of original imagenet21K is single label.
from imagenet21k.
Where can I find the mapping between class ids and class names of the original ImageNet-21K?
from imagenet21k.
https://raw.githubusercontent.com/niharikajainn/imagenet-ancestors-descendants/master/words.txt
from imagenet21k.
Thank you very much
from imagenet21k.
Could you provide the code of how to get the multi-label of each image according to words.txt file
from imagenet21k.
Could you provide the code of how to get the multi-label of each image according to words.txt file
same question here
from imagenet21k.
Could you provide the code of how to get the multi-label of each image according to words.txt file
https://github.com/niharikajainn/imagenet-ancestors-descendants
check the categories.py
file
from imagenet21k.
Could you provide the code of how to generating the tree
from imagenet21k.
No at the moment. you are welcome to write a code of your own and validate the tree.
from imagenet21k.
Thank you very much
from imagenet21k.
Related Issues (20)
- why no data normlization in data pre-processing?when I use data normlization in data pre-processing, rate of convergence of the network is slow HOT 1
- Can you share mean, std of imagenet21k? HOT 2
- 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
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from imagenet21k.