I am running a multi class classification problem using colab pro wherein i am training my model using vgg16 for feature extraction and using xgboost as a classifier, however when i check accuracy i am getting 0%. I ran the same code on my spyder ide on my local machine and there i am getting an accuracy of 72.6%. The code along with the output message is pasted below:
train_images = [] train_labels = [] for directory_path in glob.glob("/content/drive/MyDrive/indoorCVPR_09/train/*"): label = directory_path.split("\\")[-1] print(label) for img_path in glob.glob(os.path.join(directory_path, "*.jpg")): print(img_path) img = cv2.imread(img_path,cv2.IMREAD_COLOR) img = cv2.resize(img,(SIZE,SIZE)) img = cv2.cvtColor(img,cv2.COLOR_RGB2BGR) train_images.append(img) train_labels.append(label) train_images = np.array(train_images) train_labels = np.array(train_labels) test_images = [] test_labels = [] for directory_path in glob.glob("/content/drive/MyDrive/indoorCVPR_09/test/*"): fruit_label = directory_path.split("\\")[-1] for img_path in glob.glob(os.path.join(directory_path, "*.jpg")): print(img_path) img = cv2.imread(img_path,cv2.IMREAD_COLOR) img = cv2.resize(img,(SIZE,SIZE)) img = cv2.cvtColor(img,cv2.COLOR_RGB2BGR) test_images.append(img) test_labels.append(fruit_label) test_images = np.array(test_images) test_labels = np.array(test_labels) from sklearn import preprocessing le = preprocessing.LabelEncoder() le.fit(test_labels) test_labels_encoded = le.transform(test_labels) le.fit(train_labels) train_labels_encoded = le.transform(train_labels) x_train,y_train,x_test,y_test = train_images,train_labels_encoded,test_images,test_labels_encoded x_train = x_train/255 print(x_train) VGG_model = VGG16(weights='imagenet', include_top=False , input_shape=(SIZE,SIZE,3)) for layer in VGG_model.layers: layer.trainable = False VGG_model.summary() feature_extractor = VGG_model.predict(x_train) features = feature_extractor.reshape(feature_extractor.shape[0],-1) X_for_training = features import xgboost as xgb model = xgb.XGBClassifier() model.fit(X_for_training,y_train) #Output of the section above: XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=3, min_child_weight=1, missing=None, n_estimators=100, n_jobs=1, nthread=None, objective='multi:softprob', random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None, silent=None, subsample=1, verbosity=1) X_test_feature = VGG_model.predict(x_test) X_test_features = X_test_feature.reshape(X_test_feature.shape[0], -1) prediction = model.predict(X_test_features) prediction = le.inverse_transform(prediction) from sklearn import metrics print("Accuracy = ",metrics.accuracy_score(test_labels,prediction)) #Output of accuracy: Accuracy = 0.0 