Instructions to use Master-AI-Lab/Lumi-Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Master-AI-Lab/Lumi-Transformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Master-AI-Lab/Lumi-Transformer") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Master-AI-Lab/Lumi-Transformer", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -10,7 +10,7 @@ This repository contains code for Luminance(Lumi) Transformer for electrolumines
|
|
| 10 |
## Background
|
| 11 |
EL imaging is a technique used to study solar cells. It involves capturing images of solar cells using a camera sensitive to the near-infrared region of the electromagnetic spectrum. These images show the distribution of charge carriers in the solar cell, which is related to the efficiency of the cell.
|
| 12 |
|
| 13 |
-
The
|
| 14 |
|
| 15 |
## Results
|
| 16 |
|
|
|
|
| 10 |
## Background
|
| 11 |
EL imaging is a technique used to study solar cells. It involves capturing images of solar cells using a camera sensitive to the near-infrared region of the electromagnetic spectrum. These images show the distribution of charge carriers in the solar cell, which is related to the efficiency of the cell.
|
| 12 |
|
| 13 |
+
The Lumi-transformer model is designed to process EL images and predict the efficiency of the solar cell. It is based on the transformer architecture, which has been shown to be effective for processing sequential data such as natural language text.
|
| 14 |
|
| 15 |
## Results
|
| 16 |
|