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mattaq/GelGenie-SMPUNet-GP-NOLSDB-5-Nov-2023

By mattaq · ↓ 0 · ♥ 0 · Updated 2024-10-07

What's in this repo

This page lists the files in mattaq/GelGenie-SMPUNet-GP-NOLSDB-5-Nov-2023, including unet_global_padding_nov_5_no_lsdb_epoch_585.onnx, unet_global_padding_nov_5_no_lsdb_epoch_585_fixed_dim_1024.onnx.

  • 2 ONNX models (.onnx files)
  • 18 files, including 7 .pdf, 3 .txt, 2 .onnx, 1 .md, 1 .pth, 1 .toml
  • Last updated 2024-10-07

ONNX files

File Size Download
onnx_checkpoints/unet_global_padding_nov_5_no_lsdb_epoch_585.onnx — Download
onnx_checkpoints/unet_global_padding_nov_5_no_lsdb_epoch_585_fixed_dim_1024.onnx — Download

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README

This model is just a prototype used during the development process of GelGenie. It consists of a SMP UNet model trained on the non-LSDB components of the GelGenie dataset. More details in the config file.

Our codebase is fully open-sourced and is available here: https://github.com/mattaq31/GelGenie.

Model info

pytorch onnx image-segmentation license:apache-2.0 region:us
↓ 0 ♥ 0

Created: 2023-11-09

Updated: 2024-10-07

View on Hugging Face

Files (18)

.gitattributes
README.md
checkpoints/checkpoint_epoch_585.pth
config.toml
model_structure.txt
model_summary.txt
onnx_checkpoints/unet_global_padding_nov_5_no_lsdb_epoch_585.onnx ONNX
onnx_checkpoints/unet_global_padding_nov_5_no_lsdb_epoch_585_fixed_dim_1024.onnx ONNX
segmentation_samples/sample_epoch_585_UVP01948May172019.pdf
segmentation_samples/sample_epoch_585_UVP01959May212019.pdf
segmentation_samples/sample_epoch_585_UVP02159June252019.pdf
segmentation_samples/sample_epoch_600_UVP01948May172019.pdf
segmentation_samples/sample_epoch_600_UVP01959May212019.pdf
segmentation_samples/sample_epoch_600_UVP02159June252019.pdf
time_log.txt
torchscript_checkpoints/unet_global_padding_nov_5_no_lsdb_epoch_585.pt
training_logs/metric_plots.pdf
training_logs/training_stats.csv

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