---
license: apache-2.0
language: en
tags:
- image-classification
- vision-transformer
- pytorch
- sem
- materials-science
- nffa-di
base_model: timm/vit_base_patch8_224.augreg2_in21k_ft_in1k
pipeline_tag: image-classification
---

# Vision Transformer for SEM Image Scale Classification

This is a fine-tuned **Vision Transformer (ViT-B/8)** model for classifying the magnification scale of Scanning Electron Microscopy (SEM) images—**pico, nano, or micro**—directly from pixel data.

The model addresses the challenge of unreliable scale information in large SEM archives, which is often hindered by proprietary file formats or error-prone Optical Character Recognition (OCR). 

This model was developed as part of the **NFFA-DI (Nano Foundries and Fine Analysis Digital Infrastructure)** project, funded by the European Union's NextGenerationEU program.

## Model Description

The model is based on the `timm/vit_base_patch8_224.augreg2_in21k_ft_in1k` checkpoint and has been fine-tuned for a 3-class image classification task on SEM images. The three scale categories are:

1.  **Pico**: Images where the pixel size is in the atomic or sub-nanometer scale (less than 1 nm).
2.  **Nano**: Images where the pixel size is in the nanometer range (1 nm to 1,000 nm, or 1 µm).
3.  **Micro**: Images where the pixel size is in the micrometer scale (greater than 1 µm).

## Model Performance

The model achieves **91,7% accuracy** on a held-out test set. Notably, most misclassifications occur at the transitional nano-micro boundary, which indicates that the model is learning physically meaningful feature representations related to the magnification level.

## How to Use

The following Python code shows how to load the model and its processor from the Hub and use it to classify a local SEM image.

```python
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch

# Load the model and image processor from the Hub
model_name = "t0m-R/vit-sem-scale-classifier" 
image_processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)

# Load and preprocess the image
image_path = "path/to/your/sem_image.png" 
try:
    image = Image.open(image_path).convert("RGB")

    # Prepare the image for the model
    inputs = image_processor(images=image, return_tensors="pt")

    # Run inference
    with torch.no_grad():
        logits = model(**inputs).logits
        predicted_label_id = logits.argmax(-1).item()
        predicted_label = model.config.id2label[predicted_label_id]

    print(f"Predicted Scale: {predicted_label}")

except FileNotFoundError:
    print(f"Error: The file at {image_path} was not found.")
```
## Training Data

This model was fine-tuned on a custom dataset of 17,700 Scanning Electron Microscopy (SEM) images, curated specifically for this project.
The images were selected to create a balanced dataset for the task of scale classification. This set contains an equal one-third split of images corresponding to the pico, nano, and micro scales (5,900 images per class).

The 17,700 images were then divided into:

    Training set: 12,000 images

    Validation set: 3,000 images

    Test set: 2,700 images

**Note on Availability**: This dataset is not publicly available at the moment but is planned for publication at a later stage. Please check this model card for future updates on data access.