Блог

Я обучил модель effectivenetB0, используя обучающий набор данных, содержащий около 400 изображений военнослужащих, широкой общественности и армейских транспортных средств, в частности танков каждой категории. После тренировки на тренировочном наборе я получаю точность около 98%, а на тестовом наборе я получаю довольно хорошую точность.

Матрица путаницы в тестовом наборе: [Армия, генерал, транспортное средство]

 [[52 35 7] [ 5 93 15] [ 5 5 86]]  

Но когда я пытаюсь предсказать отдельное изображение, оно предсказывает его не очень хорошо. Я пробовал разные решения из StackOverflow, но ничего не получалось.

 import numpy as np import tensorflow as tf  import numpy as np import pandas as pd import matplotlib.pyplot as plt import os  dataset_path = os.listdir('combined')  print (dataset_path) #what kinds of classes are in this dataset  print("Types of classes labels found: ", len(dataset_path))  class_labels = []  for item in dataset_path:  all_classes = os.listdir('combined'   '/'  item)  for room in all_classes:  class_labels.append((item, str('dataset_path'   '/'  item)   '/'   room))   print(class_labels)  df = pd.DataFrame(data=class_labels, columns=['Labels', 'image']) print(df.head()) print(df.tail())  print("Total number of images in the dataset: ", len(df))  label_count = df['Labels'].value_counts() print(label_count)  import cv2 path = 'combined/' dataset_path = os.listdir('combined')  im_size = 224  images = [] labels = []  for i in dataset_path:  data_path = path   str(I)  filenames = [i for i in os.listdir(data_path) ]  print(data_path)  for f in filenames:  img = cv2.imread(data_path   '/'   f)  img = cv2.resize(img, (im_size, im_size))  images.append(img)  labels.append(i)   images = np.array(images) print(images.shape) images = images.astype('float32') / 255.0  images = preprocess_input(images) print(images.shape)  from sklearn.preprocessing import LabelEncoder , OneHotEncoder y=df['Labels'].values print(y) print(len(y)) print(list(set(y))) y_labelencoder = LabelEncoder () y = y_labelencoder.fit_transform (y) print(y) print(list(set(y)))  y=y.reshape(-1,1)  from sklearn.compose import ColumnTransformer ct = ColumnTransformer([('my_ohe', OneHotEncoder(), [0])], remainder='passthrough') Y = ct.fit_transform(y) #.toarray() print(Y[:5]) print(Y[35:])  from sklearn.utils import shuffle from sklearn.model_selection import train_test_split  images, Y = shuffle(images, Y, random_state=1)  train_x, test_x, train_y, test_y = train_test_split(images, Y, test_size=0.2, random_state=0)  from tensorflow.keras import layers from tensorflow.keras.applications import EfficientNetB0  NUM_CLASSES = 3 IMG_SIZE = 224 size = (IMG_SIZE, IMG_SIZE)  inputs = layers.Input(shape=(IMG_SIZE, IMG_SIZE, 3))  outputs = EfficientNetB0(include_top=True, weights=None, classes=NUM_CLASSES)(inputs)  model = tf.keras.Model(inputs, outputs)  model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"] )  model.summary()  hist = model.fit(train_x, train_y, epochs=30, verbose=2)     preds = model.evaluate(test_x, test_y) print ("Loss = "   str(preds[0])) print ("Test Accuracy = "   str(preds[1]))     from matplotlib.pyplot import imread from matplotlib.pyplot import imshow from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.imagenet_utils import decode_predictions from tensorflow.keras.applications.imagenet_utils import preprocess_input  img_path = '/content/combined/vehicles/al_khalid_l1.jpg'  img = cv2.imread(img_path) img = cv2.resize(img, (224, 224)) img_array = image.img_to_array(img)  img_batch = np.expand_dims(img_array, axis=0)  img_preprocessed = preprocess_input(img_batch)  my_image = imread(img_path) imshow(my_image)  preds=model.predict(img_preprocessed)  joined = preds[0] joined[np.where(joined==np.max(joined)) ] = 1 joined[np.where(joined!=np.max(joined)) ] = 0 joined = list(map(int,joined)) print(joined) label = ['vehicle', 'army', 'general'] zipped = zip(joined, label) for i in list(zipped):  if i[0] == 1:  print(i[1])  

Результаты прогнозирования являются: [0, 1, 0] армия