license: mit
pipeline_tag: image-classification
Model Details
Model Description
This model is the best model released from the CHAMMI-75:pre-training multi-channel models with heterogeneous microscopy images paper currently under review at a conference. This model was pre-trained on the entire CHAMMI-75 dataset using the bag of channels method. It has acheived state of the art in 7/8 self-supervised learning benchmarks. The utility of this model is to be used in single-cell analysis of microscopic imaging.
- Developed by: Vidit Agrawal, Juan Caicedo
- Funded by: NSF, Meta AI
- Shared by: [Caicedo Lab]
- Model type: [More Information Needed]
- License: MIT License
Model Sources [optional]
- Repository: https://github.com/CaicedoLab/CHAMMI-75
- Paper
- Demo: https://github.com/CaicedoLab/CHAMMI-75/tree/main/aws-tutorials
Uses
Direct Use
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Out-of-Scope Use
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How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
We have utilized the DINO model.
Preprocessing [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: Nvidia RTX A6000
- Hours used: 2500
- Cloud Provider: Private Infrastructure
- Compute Region: Private Infrastructure
- Carbon Emitted: 324 kg CO2
Technical Specifications [optional]
Model Architecture and Objective
The model is a ViT Small trained on 2500 Nvidia A6000 GPU hours.
Compute Infrastructure
The model was trained on a multi-node system with 2 nodes, each containing 7 GPUs.
Hardware
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Software
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Citation [optional]
BibTeX:
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APA:
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Glossary [optional]
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More Information [optional]
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Model Card Authors [optional]
Vidit Agrawal, John Peters, Tyler N. Thompson, Mohammad Vali Sanian, Chau Pham, Nikita Moshkov, Arshad Kazi, Aditya Pillai, Jack Freeman, Byunguk Kang, Samouil L. Farhi, Ernest Fraenkel, Ron M. Stewart, Lassi Paavolainen, Bryan A. Plummer, Juan C. Caicedo