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README.md
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[Sylvain Costes](https://www.nasa.gov/people/sylvain-costes/), NASA Ames Resarch Center<br>
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## General:
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[Biological and Physical Sciences (BPS) Microscopy Benchmark Training Dataset](https://registry.opendata.aws/bps_microscopy/) or as a Huggingface dataset here:
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[kenobi/GeneLab_BPS_BenchmarkData](https://huggingface.co/datasets/kenobi/GeneLab_BPS_BenchmarkData).
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This is a Vision Transformer model trained on Fluorescence microscopy images of individual nuclei from mouse fibroblast cells,
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![](images/XRay_irradiated_Nuclei.png)
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![Right-click on this link (not the picture seen above) use 'save as'](https://roosevelt.devron-systems.com/HF/P278_73668090728-A7_003_027_proj.tif)
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## ViT base training data
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The ViT model was pretrained on a dataset consisting of 14 million images and 21k classes ([ImageNet-21k](http://www.image-net.org/).
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More information on the base model used can be found here: (https://huggingface.co/google/vit-base-patch16-224-in21k)
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## How to use this Model
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(quick snippets to work on Google Colab)
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[Sylvain Costes](https://www.nasa.gov/people/sylvain-costes/), NASA Ames Resarch Center<br>
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## General:
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This Vision Transformer model has been fine-tuned on BPS Microscopy data. We are currently working on an expansive optimisation and evaluation framework.
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The images used are available here:
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[Biological and Physical Sciences (BPS) Microscopy Benchmark Training Dataset](https://registry.opendata.aws/bps_microscopy/) or as a Huggingface dataset here:
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[kenobi/GeneLab_BPS_BenchmarkData](https://huggingface.co/datasets/kenobi/GeneLab_BPS_BenchmarkData).
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This is a Vision Transformer model trained on Fluorescence microscopy images of individual nuclei from mouse fibroblast cells,
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![](images/XRay_irradiated_Nuclei.png)
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![Right-click on this link (not the picture seen above) use 'save as'](https://roosevelt.devron-systems.com/HF/P278_73668090728-A7_003_027_proj.tif)
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## ViT base training data (currently being replaced)
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The ViT model was pretrained on a dataset consisting of 14 million images and 21k classes ([ImageNet-21k](http://www.image-net.org/).
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More information on the base model used can be found here: (https://huggingface.co/google/vit-base-patch16-224-in21k);
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## How to use this Model
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(quick snippets to work on Google Colab)
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