Instructions to use HuangLab/CELL-E_2_HPA_480 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuangLab/CELL-E_2_HPA_480 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("HuangLab/CELL-E_2_HPA_480", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -53,10 +53,10 @@ OpenCell models are trained on the OpenCell dataset. These only contain HEK cell
|
|
| 53 |
|
| 54 |
| Model | Size | Notes
|
| 55 |
|------------------------|--------------------------------|-------|
|
| 56 |
-
| [`
|
| 57 |
-
| [`
|
| 58 |
-
| [`
|
| 59 |
-
| [`
|
| 60 |
|
| 61 |
**Finetuned HPA Models**:
|
| 62 |
These models were used the HPA models as checkpoints, but then were finetuned on the OpenCell dataset. We found that they improve image generation capabilities, but did not necessary see an improvement in sequence prediction.
|
|
@@ -98,4 +98,4 @@ url={https://openreview.net/forum?id=YSMLVffl5u}
|
|
| 98 |
|
| 99 |
### Contact
|
| 100 |
|
| 101 |
-
We are an interdisciplinary lab based at [UCSF](https://www.ucsf.edu). We are particularly seeking talents in optical engineering, machine learning, and cellular microscopy. [Please reach out to Bo if you're interested in collaborating!](http://huanglab.ucsf.edu/Contact.html)
|
|
|
|
| 53 |
|
| 54 |
| Model | Size | Notes
|
| 55 |
|------------------------|--------------------------------|-------|
|
| 56 |
+
| [`OpenCell_480`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_480) | 4.73 GB | |
|
| 57 |
+
| [`OpenCell_640`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_640) | 6.31 GB | |
|
| 58 |
+
| [`OpenCell_1280`](https://huggingface.co/HuangLab/CELL-E_2_OpenCel_1280) | 10.8 GB | |
|
| 59 |
+
| [`OpenCell_2560`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_2560) | 17.5 GB | **Best for Sequence Prediction** |
|
| 60 |
|
| 61 |
**Finetuned HPA Models**:
|
| 62 |
These models were used the HPA models as checkpoints, but then were finetuned on the OpenCell dataset. We found that they improve image generation capabilities, but did not necessary see an improvement in sequence prediction.
|
|
|
|
| 98 |
|
| 99 |
### Contact
|
| 100 |
|
| 101 |
+
We are an interdisciplinary lab based at [UCSF](https://www.ucsf.edu). We are particularly seeking talents in optical engineering, machine learning, and cellular microscopy. [Please reach out to Bo if you're interested in collaborating!](http://huanglab.ucsf.edu/Contact.html)
|