I setup a DetrForObjectDetection fine-tuning run with a custom dataset. The model loss bottomed out around 2.0 and predicts zero boxes when applied to even training images.
To debug what I’d done, I stepped through the Object Detection demonstration in the documentation here:
Object detection
Running the demonstration notebook locally, the model trained on cppe5 data converges to a loss of ~1.8 and also predicts zero boxes.
Does the above demonstration work for anyone? If so, any guesses about what might be amiss? I spun up a from scratch ec2 instance and tried it there, same result. Also tried it with an Amazon DLAMI (ami-098c378a13f6a51bc) - both on g4dn.xlarge - also no predictions.
Thanks,
- Chris
Some environment info:
$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Tue_Mar__8_18:18:20_PST_2022
Cuda compilation tools, release 11.6, V11.6.124
Build cuda_11.6.r11.6/compiler.31057947_0
packages:
[(x.__name__,getattr(x, '__version__')) for x in [transformers, accelerate, evaluate, datasets, torch, torchvision, timm]]
[('transformers', '4.29.2'),
('accelerate', '0.19.0'),
('evaluate', '0.4.0'),
('datasets', '2.12.0'),
('torch', '1.13.1+cu116'),
('torchvision', '0.14.1+cu116'),
('timm', '0.9.2')]]