Write the model card (#4)

- Write the model card (52b3c239aa99d7fd29c01c8c022febbec374e496)


Co-authored-by: Kian Kenyon-Dean <[email protected]>

README.md

@@ -3,197 +3,126 @@ library_name: transformers
 tags: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# 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
 
-<!-- Provide a longer summary of what this model is. -->
+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
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Developed, funded, and shared by:** Recursion
+- **Model type:** Vision transformer CA-MAE
+- **Image modality:** Optimized for microscopy images from the CellPainting assay
+- **License:** 
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
+- **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)
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
+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:
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+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.
 
-[More Information Needed]
+### Direct Use
 
-### Downstream Use [optional]
+- 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
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+- A determined ML expert could fine-tune the encoder for downstream tasks such as classification
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- 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
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- 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
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
+You should be able to successfully run the below tests, which demonstrate how to use the model at inference time.
 
-<!-- This should link to a Dataset Card if possible. -->
+```python
+import pytest
+import torch
 
-[More Information Needed]
+from huggingface_mae import MAEModel
 
-#### Factors
+huggingface_phenombeta_model_dir = "models/phenom_beta_huggingface"
+# huggingface_modelpath = "recursionpharma/test-pb-model"
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-[More Information Needed]
+@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=models/phenom_beta_huggingface
+    huggingface_model = MAEModel.from_pretrained(huggingface_phenombeta_model_dir)
+    huggingface_model.eval()
+    return huggingface_model
 
-#### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+@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)
+```
 
-[More Information Needed]
 
-### Results
+## Training, evaluation and testing details
 
-[More Information Needed]
+See paper linked above for details on model training and evaluation. Primary hyperparameters are included in the repo linked above.
 
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
 
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+- **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:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+```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
 
-[More Information Needed]
+- Kian Kenyon-Dean: [email protected]
+- Oren Kraus: [email protected]
+- Or, email: [email protected]