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May I ask about the procedure used to train deeplab-v3 on 1024 images, I noticed the provided code in the repo samples train_interpreter.py, and trains train_deeplab.py on images with 512 x 512 resolution for face_34 task.
When doing the same for 1024 x 1024 images, the cross_validation script results in this:
Could you provide more information on the procedure for training deeplab on 1024 x 1024 images? How large was the training set that you used? The number of epochs, and batch size? I ask the latter, as I also had some issues with OOM cuda error, when training on 32G NVIDIA Tesla V100.
The text was updated successfully, but these errors were encountered:
Hi!
Thanks once again for your responsiveness :)
May I ask about the procedure used to train
deeplab-v3
on 1024 images, I noticed the provided code in the repo samplestrain_interpreter.py
, and trainstrain_deeplab.py
on images with 512 x 512 resolution for face_34 task.When doing the same for 1024 x 1024 images, the cross_validation script results in this:
Could you provide more information on the procedure for training deeplab on 1024 x 1024 images? How large was the training set that you used? The number of epochs, and batch size? I ask the latter, as I also had some issues with OOM cuda error, when training on 32G NVIDIA Tesla V100.
The text was updated successfully, but these errors were encountered: