Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
vit
nasa
biological
Eval Results (legacy)
Instructions to use kenobi/NASA_GeneLab_MBT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kenobi/NASA_GeneLab_MBT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kenobi/NASA_GeneLab_MBT") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("kenobi/NASA_GeneLab_MBT") model = AutoModelForImageClassification.from_pretrained("kenobi/NASA_GeneLab_MBT") - Notebooks
- Google Colab
- Kaggle
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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
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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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## General:
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This Vision Transformer model has been fine-tuned on BPS Microscopy data. We are currently working on an extensive 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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