I have a training job that completed training (metrics visible, 0 seconds remaining) but is stuck in Pending and cannot be used for inference.
I clicked “Stop Training Early” and waited several minutes, refreshed, and the model never finalized.
My credits are now exhausted, and I cannot retrain.
Please either finalize this model or refund the training credits, as this appears to be a backend finalization issue.
Project: planpoint
Dataset version: 2026-01-08 1:24am
Model type: RF-DETR-Seg (Preview)
this is quite urgent, so i would appreciate if you can solve it as soon as possible please
@patrick_nihranz can you please take a moment to look into this?
Hi @jimmy123A !
Happy to look into this for you. Before I begin do I have permission to access your workspace?
Sure. I just accessed the dashboard again, and the version seems to be available now. I ran out of credits, which is why it didn’t properly complete all the epochs. If I create a new version now and train from a previous checkpoint (v11), will it work? How many prepaid credits should I buy?
Great question @jimmy123A !
When doing a fine tune, you should get an approximation of how many credits it takes to train. This is typically an overestimate, but we implement that to ensure that you will have enough credits to complete the training. Let me know if that helps!
Got it. However, this estimated number is not getting updated when I switch from Objects365 pretrained weights to a previous checkpoint
Based on our current metrics, is it recommended to finish it, or is it better to leave it as is? How much would that improve precision, approximately? Any idea?
Although its unlikely to be the case they would consume the same amount of credits, again this is to be safe and ensure you will have enough credits for training by overestimating a little bit.
This really depends on a large number of factors. From my perspective, you metrics are quite good at this point but it depends on what your KPI targets are. I don’t think your dataset is so big that you have to worry about overfitting, so additional well annotated data wouldn’t hurt. Unfortunately, I’m not exactly sure how much it would improve precision just because of the nature of computer vision and all the confounding factors involved, however well annotated clean data always goes a long way!