from transformers import MobileViTForImageClassification, MobileViTImageProcessor
from PIL import Image
import torch

# 1. Load Model
model = MobileViTForImageClassification.from_pretrained("Shadow0482/mithu-mobilevit-dr")
processor = MobileViTImageProcessor.from_pretrained("Shadow0482/mithu-mobilevit-dr")

# 2. Load Image
image = Image.open("path_to_eye_scan.jpg").convert("RGB")

# 3. Predict
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)
    
print("Predicted Class:", model.config.id2label[outputs.logits.argmax(-1).item()])

Usage (ONNX)

python

import onnxruntime as ort
import numpy as np
from PIL import Image

# 1. Start Session
session = ort.InferenceSession("mithu-vit.onnx")

# 2. Prepare Input
img = Image.open("test.jpg").resize((256, 256))
img_data = np.array(img).transpose(2, 0, 1).astype(np.float32) / 255.0
img_data = np.expand_dims(img_data, axis=0)

# 3. Run
outputs = session.run(None, {"pixel_values": img_data})
print("Logits:", outputs[0])