Merge pull request #157 from thearyadev/151-add-new-model

151 add new model

classifier/train.py

@@ -104,7 +104,7 @@ def main() -> int:
     shutil.copy(Path("./classifier/model.py"), model_dir / "frozen_model.py")
 
     with open(model_dir / "detail", "w+") as file:
-        file.writelines(info_file)
+        file.write("\n".join(info_file))
 
     with open(model_dir / "__init__.py", "w+") as file:
         file.write("from .frozen_model import transformer, NNModel as FrozenNeuralNetworkModel")

models/thearyadev-initial-15-02-2024/__init__.py

@@ -0,0 +1 @@
+from .frozen_model import transformer, NNModel as FrozenNeuralNetworkModel

models/thearyadev-initial-15-02-2024/classes

@@ -0,0 +1,40 @@
+0
+1
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+2
+20
+21
+22
+23
+24
+25
+26
+27
+28
+29
+3
+30
+31
+32
+33
+34
+35
+36
+37
+38
+39
+4
+40
+6
+7
+8
+9

models/thearyadev-initial-15-02-2024/detail

@@ -0,0 +1,12 @@
+Model Name: thearyadev-initial-15-02-2024
+Class Size: 250
+Epoch 1: Validation Loss: 0.0213, Accuracy: 1.00
+Epoch 2: Validation Loss: 0.0083, Accuracy: 1.00
+Epoch 3: Validation Loss: 0.0045, Accuracy: 1.00
+Epoch 4: Validation Loss: 0.0027, Accuracy: 1.00
+Epoch 5: Validation Loss: 0.0019, Accuracy: 1.00
+Epoch 6: Validation Loss: 0.0013, Accuracy: 1.00
+Epoch 7: Validation Loss: 0.0010, Accuracy: 1.00
+Epoch 8: Validation Loss: 0.0007, Accuracy: 1.00
+Epoch 9: Validation Loss: 0.0006, Accuracy: 1.00
+Epoch 10: Validation Loss: 0.0005, Accuracy: 1.00

models/thearyadev-initial-15-02-2024/frozen_model.py

@@ -0,0 +1,28 @@
+"""this module is written with the help of chatgpt"""
+import torchvision.transforms as transforms # type: ignore
+import torch.nn as nn
+import torch.nn.functional as F
+
+transformer = transforms.Compose([
+    transforms.ToTensor(), 
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+class NNModel(nn.Module):
+    def __init__(self, num_classes: int) -> None:
+        super(NNModel, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 8 * 22, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, num_classes)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 8 * 22)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x