--- license: creativeml-openrail-m --- Based on: ETL9G 607200 samples 3036 classes (hiragana and kanji) 200 samples each class record_length: 8199 bytes image_width: 64px image_height: 64px I was testing a few more samples locally with the below. Note the results of the model are encoded. ``` from PIL import Image import numpy as np import torch # Define the preprocessing function def preprocess_image(image_path): image = Image.open(image_path).convert('L') resized_image = image.resize((64, 64)) image_array = np.array(resized_image) / 255.0 reshaped_image = image_array.reshape(1, -1) return reshaped_image # Function to predict a label for an image using your PyTorch model def predict_label(image_path, model, device): # Convert the preprocessed image to torch tensor and send to the device processed_image = torch.tensor(preprocess_image(image_path), dtype=torch.float32).to(device) # Predict the label using the model with torch.no_grad(): outputs = model(processed_image) _, predicted_class = torch.max(outputs.data, 1) return predicted_class.item() # Create the reverse dictionary for decoding index_to_label = {index: label for label, index in label_to_index.items()} # Test using a sample image sample_image_path = ["example.png", "example.png", "example.png", "example.png", "example.png"] for sample in sample_image_path: predicted_encoded_label = predict_label(sample, model, device) # Decode the predicted label using the reversed dictionary decoded_label = index_to_label[predicted_encoded_label] print(f"The model predicts the image label as: {decoded_label}") ```