Comments (2)
Ok, that was easy. Just make a train and a val subfolder and put all unlabeled data in val.
from deepcluster.
I would suggest to follow the train and val data sets in line with the original implementation. Make sure the sizes do match. It has been trained on color images, therefore customization will be needed for grayscale data.
from deepcluster.
Related Issues (20)
- AttributeError: 'Clustering' object has no attribute 'obj' HOT 3
- RuntimeError: invalid argument 5: k not in range for dimension at /pytorch/aten/src/THC/generic/THCTensorTopK.cu:23 HOT 2
- train dataset that is not sorted in different folders HOT 1
- Hey, can you please share how you have solved this problem because I am also getting the same type of error which m not able to solve.hope you can help me out. Thank you. HOT 2
- Do I need labels or pseudo-labels as input for clustering? HOT 8
- A problem about the clusters and pseudolabels every epoch HOT 8
- A problem why use different optimizer for top layer and other layers HOT 1
- About the clustering loss HOT 1
- About the clustering loss HOT 3
- Is this model universal on different datasets? HOT 1
- Why does the deep cluster model cluster unevenly when using MNIST datasets? HOT 1
- Dealing with empty clusters HOT 3
- Gradcam
- about NMI
- Could I use it for image color clustering?
- PackagesNotFoundError
- Faiss
- how to avoid empty clusters. HOT 1
- Loading
- The pre-trained AlexNet on ImageNet
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