Comments (2)
We only do the few-shot finetuning on the anomaly detection task. If you want to use UniTS for labeling the new dataset, I suggest you collect are the anomaly detection datasets you have and finetune a new UniTS to do so.
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We only do the few-shot finetuning on the anomaly detection task. If you want to use UniTS for labeling the new dataset, I suggest you collect are the anomaly detection datasets you have and finetune a new UniTS to do so.
Yes, I intend to use UniTS to label my unlabeled dataset. Would it be appropriate if I loaded your pre-trained UniTS model (units_x32_pretrain_checkpoint.pth) and tested it on my unlabeled dataset without evaluating the results obtained from the model, since there are no ground truth labels available?
Regarding your advice, I apologize, but I am still unsure. Could you please provide me with further guidance?
Thank you.
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Related Issues (20)
- Typo in README HOT 1
- How to run self supervised pretraining using UniTS on custom data? HOT 1
- The initialization of Category Embedding HOT 1
- Why there is no loss.backward in training process? HOT 1
- IncompatibleKeys Error when load pre-trained model to do the fine-tuning. HOT 4
- Should requirements.txt be updated including more packages? HOT 2
- During the run of the UniTS_pretrain_x128.sh script the loss value encountered nan. HOT 1
- Validation set HOT 2
- Dataset Download HOT 1
- A question about fine-tuning
- Using UniTS for new tasks
- gluonts problem HOT 1
- Hello! Asking about Inference. HOT 2
- When loading the model, _IncompatibleKeys appears. HOT 3
- Most recent commit removed all files? HOT 2
- How is the internal review going? What's the status? HOT 1
- Question about DyLinear block HOT 2
- Question about Model Configuration and Checkpoint Availability in Zero-Shot Setting
- RuntimeError: Distributed package doesn't have NCCL built in HOT 1
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from units.