Comments (1)
baldassarreFe
commented on July 19, 2024
Hi,
how many lab_images_.tfrecord do we need to do before training?
Ideally the whole ImageNet, but in practice you can start with ~10,000 or so.
For val_lab_images_.tfrecord, do we just need to rename one lab_images_.tfrecord ?
Yes, just pick one and rename it.
Where are the grey images ?
Not sure I understand the question. All images need to have color information for training/validation/testing which is then separated when they are pre-processed.
After training, you can feed a gray image to the network, but that requires a bit of a work-around and it's not included in the repo.
from deep-koalarization.
Related Issues (14)
- train on multiple gpu HOT 1
- train.py? HOT 3
- training on my own data HOT 1
- from resizeimage import resizeimage HOT 1
- queue_single_images_from_folder HOT 1
- Labels from Inception at the beginning of training seem incorrect
- [REQ] Colab version
- koalarization.dataset.lab_batch issue HOT 5
- How do you train your neural network? HOT 1
- TypeError: Can not convert a LabImageRecordReader into a Tensor or Operation. HOT 3
- Pre-trained model HOT 1
- Lab_Batch file is not creating Tf-records
- Inception output size HOT 1
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