Comments (2)
These files for semantic embeddings are not generated in the steps mentioned in README. We had taken them from another GitHub repo for previous work on ZSD. Kindly see my reply to issue #4 for more details.
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Dear @sandipan211 ,
I have one query regarding class embedding. In the current repository, for MSCOCO, MSCOCO/fasttext.npy, it uses 81300 dimensional embedding , and Pascal VOC, VOC/fasttext_synonym.npy, it uses 21300 dimensional embedding. It may be because 81th in coco and 21st in voc may represent background class.
I want to create similar class embedding for custom data having 50 different number of classes. In that case, is the class embedding (for instance, in VOC/fasttext_synonym.npy) that can represent different categorical name of classes into numerical representation? Is using the python embedding function , such as word2vec , is only to represent the different classes with names in string to numerical value with 300 dimensions for each class ?
The class embedding weight (such as fasttext.npy) is required to train the regressor, specially in step 3.
Thank you for your time and consideration.
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Related Issues (20)
- Do you have a minimal environment YAML file? HOT 1
- Do you still have the weight file obtained by executing the fifth step HOT 2
- Do you think you'll ever migrate your code to the latest stable versions of mmcv and mmdetection? HOT 13
- Some hesitations related to background class label index HOT 5
- Confusion about validation metrics for zero-shot detection. HOT 3
- The test result of detector trained only on seen classes HOT 23
- class embedding vector for custom data HOT 4
- Custom Data HOT 1
- low accuracy on unseen classes HOT 1
- when classifier is trained
- is train_unseen_classifier.py missing HOT 1
- how to generate test npy for unseen classes HOT 2
- custom dataset tune HOT 2
- Pascal VOC data split HOT 2
- inference to single image HOT 1
- Can you provide some output files? HOT 7
- Author, Do you think you will update the code and environment to the latest version? HOT 2
- I have some questions about training on VOC datasets HOT 5
- Loss suddenly becoming very large
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