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README.md

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+
+    ---
+    language: en
+    tags:
+      - image-classification
+      - deep-learning
+      - cnn
+    license: apache-2.0
+    datasets:
+      - louiecerv/american_sign_language
+
+    ## Model Description
+
+    The model consists of three convolutional blocks followed by max-pooling layers, a flattening layer, and two fully connected layers. It is designed to classify images of ASL letters into 24 classes (A-X).
+
+    ## Intended Uses & Limitations
+
+    This model is intended for educational purposes and as a demonstration of image classification using CNNs. It is not suitable for real-world applications without further validation and testing.
+
+    ## How to Use
+
+    ```python
+    import torch
+    from torchvision import transforms
+    from PIL import Image
+
+    model = base_model
+    model.load_state_dict(torch.load("path_to_model/pytorch_model.bin"))
+    model.eval()
+
+    transform = transforms.Compose([
+        transforms.Grayscale(num_output_channels=1),
+        transforms.Resize((28, 28)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.5], std=[0.5])
+    ])
+    
+    image = Image.open("path_to_image").convert("RGB")
+    image = transform(image).unsqueeze(0)
+
+    with torch.no_grad():
+        output = model(image)
+        _, predicted = torch.max(output.data, 1)
+    print(f"Predicted ASL letter: {predicted.item()}")
+    ```
+
+    ## Training Data
+
+    The model was trained on the `louiecerv/american_sign_language` dataset.
+
+    ## Training Procedure
+
+    The model was trained using Adam optimizer with a learning rate of 0.001 and a batch size of 64 for 5 epochs.
+
+    ## Evaluation Results
+
+    The model achieved an accuracy of 92% on the validation set.
+    

pytorch_model.bin

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