Comments (2)
Perfect! Thank you so much, my model learns much better now 👍
from cell-detr.
Hi,
so in our particular use case, we have not more than 10 instances present in a specimen image. Thus, we have set the number of object queries to 12. If you want to detect more instances per image you can increase the parameter number_of_query_positions: int = 12
in detr.py.
Be aware that more object queries lead to an increase in computational complexity both in the CNN encoder and the transformer encoder.
Cheers
Christoph
from cell-detr.
Related Issues (18)
- Computation consumption problem HOT 1
- ModulatedDeformConvPack not found! HOT 3
- How to prepare data in .pt format! HOT 1
- Coutld you share the download link of the data set? HOT 1
- No module named 'modules' HOT 1
- An issue related to the dataset.py
- Preparing the train and validation data in .pt format from coco format json file HOT 1
- nvcc fatal : Unsupported gpu architecture 'compute_75' HOT 1
- how can i download the dataset HOT 1
- Enquiry about validate and test methods in ModelWrapper HOT 1
- Enquiry about the difference between ohem and non-ohem HOT 2
- Failed to setup Pau_cuda HOT 1
- Training without segmentation on my own dataset HOT 1
- Issues with Airspeed during installation HOT 1
- about the test of the network
- IndexError: tuple index out of range HOT 1
- RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, HOT 2
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from cell-detr.