Instructions to use recursionpharma/OpenPhenom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use recursionpharma/OpenPhenom with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="recursionpharma/OpenPhenom", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("recursionpharma/OpenPhenom", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Oren Kraus commited on
added CVPR links (#13)
Browse files
README.md
CHANGED
|
@@ -1,7 +1,8 @@
|
|
| 1 |
# Masked Autoencoders are Scalable Learners of Cellular Morphology
|
| 2 |
Official repo for Recursion's two recently accepted papers:
|
| 3 |
- Spotlight full-length paper at [CVPR 2024](https://cvpr.thecvf.com/Conferences/2024/AcceptedPapers) -- Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
|
| 4 |
-
- Paper:
|
|
|
|
| 5 |
- Spotlight workshop paper at [NeurIPS 2023 Generative AI & Biology workshop](https://openreview.net/group?id=NeurIPS.cc/2023/Workshop/GenBio)
|
| 6 |
- Paper: https://arxiv.org/abs/2309.16064
|
| 7 |
|
|
|
|
| 1 |
# Masked Autoencoders are Scalable Learners of Cellular Morphology
|
| 2 |
Official repo for Recursion's two recently accepted papers:
|
| 3 |
- Spotlight full-length paper at [CVPR 2024](https://cvpr.thecvf.com/Conferences/2024/AcceptedPapers) -- Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
|
| 4 |
+
- Paper: https://arxiv.org/abs/2404.10242
|
| 5 |
+
- CVPR poster page with video: https://cvpr.thecvf.com/virtual/2024/poster/31565
|
| 6 |
- Spotlight workshop paper at [NeurIPS 2023 Generative AI & Biology workshop](https://openreview.net/group?id=NeurIPS.cc/2023/Workshop/GenBio)
|
| 7 |
- Paper: https://arxiv.org/abs/2309.16064
|
| 8 |
|