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cnn_letters.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5fb7a8a2aa2b31911683753a2c1ec8ec9e2654b5d022d91bd6f096a2bead31ed
+size 428216

dowload.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from safetensors.torch import load_file
+
+class CNN(nn.Module):
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
+        self.fc1 = nn.Linear(32*7*7, 64)
+        self.fc2 = nn.Linear(64, 26)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.max_pool2d(x, 2)
+        x = F.relu(self.conv2(x))
+        x = F.max_pool2d(x, 2)
+        x = x.view(x.size(0), -1)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+model = CNN()
+weights_dict = load_file("cnn_letters.safetensors")
+model.load_state_dict(weights_dict)
+model.eval()
+
+#using
+
+#from PIL import Image
+#from torchvision import transforms
+#get you image 
+#img = Image.open("my_letter.png").convert("L")
+
+#transform = transforms.Compose([
+#    transforms.Resize((28,28)),
+#    transforms.ToTensor(),
+#    transforms.Normalize((0.5,), (0.5,))
+#])
+
+#x = transform(img).unsqueeze(0)
+
+#with torch.no_grad():
+#    output = model(x)
+#    pred = output.argmax(dim=1)
+#print(f"Predicted class: {pred.item() + 1}")