added two modfels

.gitignore

@@ -1,27 +1,2 @@
-
-# Python cache
-__pycache__/
-*.pyc
-*.pyo
-
-# Virtual environment
-venv/
-.venv/
-bin/
-lib/
-lib64/
-include/
-share/
-pyvenv.cfg
-
-# Data & datasets
-data/
-bin/
-
-# Model weights
-*.pth
-
-# System / logs
-.DS_Store
-*.log
-*.tmp
+# Created by venv; see https://docs.python.org/3/library/venv.html
+*

Tune.py

@@ -10,7 +10,7 @@ from architectures.Model import Model, Architecture
 # numbre of classes 
 CLASS_SIZE = 20
 # batch 
-BATCH_SIZE = 8
+BATCH_SIZE = 16
 
 # size of images per class trainset + testset
 # 30 works best, more than that and we dont have enough data
@@ -33,7 +33,8 @@ RESOLUTION = 224
 #  - RESNET50
 #  - DENSENET121
 #  - INCEPTION
-arch = Architecture.RESNET18
+#  - GOOGLENET
+arch = Architecture.EFFICIENTNET
 
 # DATA PREPARATION
 # load data set and prepare
@@ -94,7 +95,7 @@ model.train(
 # save.
 torch.save(
     model.get().state_dict(), 
-    f'{arch.name}.pth')
+    f'{arch.name.lower()}.pth')
 
 # done tuning
 print('Model saved!')

architectures/Model.py

@@ -1,3 +1,4 @@
+
 from abc import ABC, abstractmethod
 import torch
 import torch.nn as nn
@@ -85,10 +86,17 @@ class Model(ABC):
             case Architecture.DENSENET121:
                 from architectures.DenseNet121 import DenseNet121
                 return DenseNet121(device, size)
+            # googleNet
+            case Architecture.GOOGLENET:
+                from architectures.GoogleNet import GoogleNet
+                return GoogleNet(device, size)
+            # EfficientNet
+            case Architecture.EFFICIENTNET:
+                from architectures.EfficentNet import EfficientNet
+                return EfficientNet(device, size)
             case _:
                 raise ValueError(f"Unknown model: {arch}")
 
- 
 # model architectures
 from enum import Enum, auto
 
@@ -97,3 +105,5 @@ class Architecture(Enum):
     RESNET50 = auto()
     INCEPTION = auto()
     DENSENET121 = auto()
+    GOOGLENET = auto()
+    EFFICIENTNET = auto()

architectures/ResNet18.py

@@ -11,12 +11,12 @@ class ResNet18(Model):
         m = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)
 
         # freez all layers
-        for param in m.parameters():
-            param.requires_grad = False
+        #for param in m.parameters():
+        #    param.requires_grad = False
 
         # unfreez the last two
-        for param in m.layer3.parameters(): param.requires_grad = True
-        for param in m.layer4.parameters(): param.requires_grad = True
+        #for param in m.layer3.parameters(): param.requires_grad = True
+        #for param in m.layer4.parameters(): param.requires_grad = True
         
         m.fc = nn.Linear(m.fc.in_features, self.size)
         return m.to(self.device)