optimised

architectures/ResNet50.py

@@ -0,0 +1,36 @@
+
+import torch.nn as nn
+from torchvision import models
+
+# Base model
+from architectures.Model import Model
+
+class ResNet50(Model):
+    # NOTE:
+    # This model had it's best performance with the following configs
+    # numbre of classes 
+    # CLASS_SIZE = 20
+    # BATCH_SIZE = 16
+    # SAMPLE_SIZE = 30
+    # TRAINING_SMPLE = 28
+    # LR_RATE = 0.0001
+    # EPOCHS = 15
+    # RESOLUTION = 224
+    # NOTE: But it may be a one time thing. 
+    # because testing again didn't repeat 
+    
+    def get(self):
+        m = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
+        
+        # freez all layers
+        #for param in m.parameters():
+            #param.requires_grad = False
+
+        # unfreez the last two
+        # NOTE: Freezing everything and unfrizing the last 3 yeilded the best performance
+        #for param in m.layer2.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