Create README.md

README.md

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+## Model description
+This is a fine-tuned model based on [apple/mobilevitv2-1.0-imagenet1k-256](https://huggingface.co/apple/mobilevitv2-1.0-imagenet1k-256) trained for sketch image recognition using [Xenova/quickdraw-small](https://huggingface.co/datasets/Xenova/quickdraw-small) dataset.
+
+## How to use?
+```
+from transformers import MobileViTImageProcessor, MobileViTV2ForImageClassification
+from PIL import Image
+import requests
+import torch
+import numpy as np  # Importing NumPy
+
+url = "https://static.thenounproject.com/png/2024184-200.png"
+response = requests.get(url, stream=True)
+
+# Convert to grayscale to ensure a single channel input
+image = Image.open(response.raw).convert('L')  # Convert to grayscale
+
+processor = MobileViTImageProcessor.from_pretrained("laszlokiss27/doodle-dash2")
+model = MobileViTV2ForImageClassification.from_pretrained("laszlokiss27/doodle-dash2")
+
+# Convert the PIL image to a tensor and add a channel dimension
+image_tensor = torch.unsqueeze(torch.tensor(np.array(image)), 0).float()
+image_tensor = image_tensor.unsqueeze(0)  # Add batch dimension
+
+# Check if processor requires specific form of input
+inputs = processor(images=image_tensor, return_tensors="pt")
+
+outputs = model(**inputs)
+logits = outputs.logits
+
+# Get prediction
+predicted_class_idx = logits.argmax(-1).item()
+predicted_class = model.config.id2label[predicted_class_idx]
+print("Predicted class:", predicted_class)
+
+```