GelGenie Universal Dec 2023
This model consists of an SMP-UNet trained on the GelGenie training dataset in December 2023. It is highly robust and should produce good results for a wide variety of gel types and resolutions. This is the primary...
Model source
- Kind
- Other
- File size
- 54.6–164 MB
- Version
- vRevision 1845c27
- Updated
- Sep 16, 2026
- Downloads
- 0
- Source
- Hugging Face
Source description
This model consists of an SMP-UNet trained on the GelGenie training dataset in December 2023. It is highly robust and should produce good results for a wide variety of gel types and resolutions. This is the primary model that should be the first point of reference for any new gel analysis.
The model was trained on the training set (420 images) for 600 epochs and the best checkpoint extracted based on the validation dice score.
For more details on the configuration used for training, please visit https://huggingface.co/mattaq/GelGenie-Universal-Dec-2023 and check the config.toml file. Our codebase is fully open-sourced and is available here: https://github.com/mattaq31/GelGenie.
A BioImage.IO version of this model is also available for download here.
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1 source
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1 sourceVerified Sep 1
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Model artifacts
4 artifacts
Model artifacts
4 artifactscheckpoints/checkpoint_epoch_579.pth
pth · 164 MB · SHA-256 44a2d223c908…c329 · Hugging Face
Download fileonnx_checkpoints/unet_dec_21_epoch_579_fixed_dim_1024.onnx
onnx · 54.6 MB · SHA-256 56e335e59e85…ed29 · Hugging Face
Download fileonnx_checkpoints/unet_dec_21_epoch_579.onnx
onnx · 54.6 MB · SHA-256 10ea972a4062…9c48 · Hugging Face
Download filetorchscript_checkpoints/unet_dec_21_epoch_579.pt
pt · 54.9 MB · SHA-256 fcdca2fc286b…dfeb · Hugging Face
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2 excerpts
Source excerpts
2 excerpts--- license: apache-2.0 pipeline_tag: image-segmentation library_name: pytorch --- This model consists of an SMP-UNet trained on the GelGenie training dataset in December 2023. It is highly robust and should produce good results for a wide variety of gel types and resolutions. This is the primary model that should be the first point of reference for any new gel analysis. The model was trained on the training set (420 images) for 600 epochs and the best checkpoint extracted based on the validation dice score. For more details on the configuration used for training, please visit https://huggingface.co/mattaq/GelGenie-Universal-Dec-2023 and check the config.toml file. Our codebase is fully open-sourced and is available here: https://github.com/mattaq31/GelGenie. A [BioImage.IO](https://bioimage.io/#/) version of this model is also available for download [here](https://huggingface.co/mattaq/GelGenie-Universal-Dec-2023/resolve/main/gelgenie_universal_model_bioimageio.zip).
Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Library pytorch · Repo cykavodka/GelGenie-Universal-Dec-2023
