I am trying to perform transfer learning on ResNet50 model pretrained on Imagenet weights for PASCAL VOC 2012 dataset. As it is a multi label dataset, I am using
sigmoid activation function in the final layer and
binary_crossentropy loss. The metrics are
precision,recall and accuracy. Below is the code I used to build the model for 20 classes (PASCAL VOC has 20 classes).
img_height,img_width = 128,128 num_classes = 20 #If imagenet weights are being loaded, #input must have a static square shape (one of (128, 128), (160, 160), (192, 192), or (224, 224)) base_model = applications.resnet50.ResNet50(weights= 'imagenet', include_top=False, input_shape= (img_height,img_width,3)) x = base_model.output x = GlobalAveragePooling2D()(x) #x = Dropout(0.7)(x) predictions = Dense(num_classes, activation= 'sigmoid')(x) model = Model(inputs = base_model.input, outputs = predictions) for layer in model.layers[-2:]: layer.trainable=True for layer in model.layers[:-3]: layer.trainable=False adam = Adam(lr=0.0001) model.compile(optimizer= adam, loss='binary_crossentropy', metrics=['accuracy',precision_m,recall_m]) #print(model.summary()) X_train, X_test, Y_train, Y_test = train_test_split(x_train, y, random_state=42, test_size=0.2) savingcheckpoint = ModelCheckpoint('ResnetTL.h5',monitor='val_loss',verbose=1,save_best_only=True,mode='min') earlystopcheckpoint = EarlyStopping(monitor='val_loss',patience=10,verbose=1,mode='min',restore_best_weights=True) model.fit(X_train, Y_train, epochs=epochs, validation_data=(X_test,Y_test), batch_size=batch_size,callbacks=[savingcheckpoint,earlystopcheckpoint],shuffle=True) model.save_weights('ResnetTLweights.h5')
It ran for 35 epochs until earlystopping and the metrics are as follows (without Dropout layer):
loss: 0.1195 - accuracy: 0.9551 - precision_m: 0.8200 - recall_m: 0.5420 - val_loss: 0.3535 - val_accuracy: 0.8358 - val_precision_m: 0.0583 - val_recall_m: 0.0757
Even with Dropout layer, I don't see much difference.
loss: 0.1584 - accuracy: 0.9428 - precision_m: 0.7212 - recall_m: 0.4333 - val_loss: 0.3508 - val_accuracy: 0.8783 - val_precision_m: 0.0595 - val_recall_m: 0.0403
With dropout, for a few epochs, the model is reaching to a validation precision and accuracy of 0.2 but not above that.
I see that precision and recall of validation set is pretty low compared to training set with and without dropout layer. How should I interpret this? Does this mean the model is overfitting. If so, what should I do? As of now the model predictions are quite random (totally incorrect). The dataset size is 11000 images.