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MODELCARD.md

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----
-library_name: transformers
-tags: []
----
-
-# Model Card for Phenom CA-MAE-S/16
-
-Channel-agnostic image encoding model designed for microscopy image featurization. 
-The model uses a vision transformer backbone with channelwise cross-attention over patch tokens to create contextualized representations separately for each channel.
-
-
-## Model Details
-
-### Model Description
-
-This model is a [channel-agnostic masked autoencoder](https://openaccess.thecvf.com/content/CVPR2024/html/Kraus_Masked_Autoencoders_for_Microscopy_are_Scalable_Learners_of_Cellular_Biology_CVPR_2024_paper.html) trained to reconstruct microscopy images over three datasets:
-1. RxRx3
-2. JUMP-CP overexpression
-3. JUMP-CP gene-knockouts
-
-- **Developed, funded, and shared by:** Recursion
-- **Model type:** Vision transformer CA-MAE
-- **Image modality:** Optimized for microscopy images from the CellPainting assay
-- **License:** 
-
-
-### Model Sources
-
-- **Repository:** [https://github.com/recursionpharma/maes_microscopy](https://github.com/recursionpharma/maes_microscopy)
-- **Paper:** [Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology](https://openaccess.thecvf.com/content/CVPR2024/html/Kraus_Masked_Autoencoders_for_Microscopy_are_Scalable_Learners_of_Cellular_Biology_CVPR_2024_paper.html)
-
-
-## Uses
-
-NOTE: model embeddings tend to extract features only after using standard batch correction post-processing techniques. **We recommend**, at a *minimum*, after inferencing the model over your images, to do the standard `PCA-CenterScale` pattern or better yet Typical Variation Normalization:
-
-1. Fit a PCA kernel on all the *control images* (or all images if no controls) from across all experimental batches (e.g. the plates of wells from your assay),
-2. Transform all the embeddings with that PCA kernel,
-3. For each experimental batch, fit a separate StandardScaler on the transformed embeddings of the controls from step 2, then transform the rest of the embeddings from that batch with that StandardScaler.
-
-### Direct Use
-
-- Create biologically useful embeddings of microscopy images
-- Create contextualized embeddings of each channel of a microscopy image (set `return_channelwise_embeddings=True`)
-- Leverage the full MAE encoder + decoder to predict new channels / stains for images without all 6 CellPainting channels
-
-### Downstream Use
-
-- A determined ML expert could fine-tune the encoder for downstream tasks such as classification
-
-### Out-of-Scope Use
-
-- Unlikely to be especially performant on brightfield microscopy images
-- Out-of-domain medical images, such as H&E (maybe it would be a decent baseline though)
-
-## Bias, Risks, and Limitations
-
-- Primary limitation is that the embeddings tend to be more useful at scale. For example, if you only have 1 plate of microscopy images, the embeddings might underperform compared to a supervised bespoke model.
-
-## How to Get Started with the Model
-
-You should be able to successfully run the below tests, which demonstrate how to use the model at inference time.
-
-```python
-import pytest
-import torch
-
-from huggingface_mae import MAEModel
-
-huggingface_phenombeta_model_dir = "."
-# huggingface_modelpath = "recursionpharma/test-pb-model"
-
-
-@pytest.fixture
-def huggingface_model():
-    # Make sure you have the model/config downloaded from https://huggingface.co/recursionpharma/test-pb-model to this directory
-    # huggingface-cli download recursionpharma/test-pb-model --local-dir=.
-    huggingface_model = MAEModel.from_pretrained(huggingface_phenombeta_model_dir)
-    huggingface_model.eval()
-    return huggingface_model
-
-
-@pytest.mark.parametrize("C", [1, 4, 6, 11])
-@pytest.mark.parametrize("return_channelwise_embeddings", [True, False])
-def test_model_predict(huggingface_model, C, return_channelwise_embeddings):
-    example_input_array = torch.randint(
-        low=0,
-        high=255,
-        size=(2, C, 256, 256),
-        dtype=torch.uint8,
-        device=huggingface_model.device,
-    )
-    huggingface_model.return_channelwise_embeddings = return_channelwise_embeddings
-    embeddings = huggingface_model.predict(example_input_array)
-    expected_output_dim = 384 * C if return_channelwise_embeddings else 384
-    assert embeddings.shape == (2, expected_output_dim)
-```
-
-
-## Training, evaluation and testing details
-
-See paper linked above for details on model training and evaluation. Primary hyperparameters are included in the repo linked above.
-
-
-## Environmental Impact
-
-- **Hardware Type:** Nvidia H100 Hopper nodes
-- **Hours used:** 400
-- **Cloud Provider:** private cloud
-- **Carbon Emitted:** 138.24 kg co2 (roughly the equivalent of one car driving from Toronto to Montreal)
-
-**BibTeX:**
-
-```TeX
-@inproceedings{kraus2024masked,
-  title={Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology},
-  author={Kraus, Oren and Kenyon-Dean, Kian and Saberian, Saber and Fallah, Maryam and McLean, Peter and Leung, Jess and Sharma, Vasudev and Khan, Ayla and Balakrishnan, Jia and Celik, Safiye and others},
-  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
-  pages={11757--11768},
-  year={2024}
-}
-```
-
-## Model Card Contact
-
-- Kian Kenyon-Dean: [email protected]
-- Oren Kraus: [email protected]
-- Or, email: [email protected]