tensorflow实现CNN模型垃圾分类算法

上海开始施行垃圾分类啦。那么我们能不能通过平常学习的机器学习和深度学习的算法来实现一个简单的垃圾分类的模型呢？
下面主要用过CNN来实现垃圾的分类。在本数据集中，垃圾的种类有六种（和上海的标准不一样），分为玻璃、纸、硬纸板、塑料、金属、一般垃圾。
该数据集包含了2527个生活垃圾图片。数据集的创建者将垃圾分为了6个类别，分别是：

玻璃（glass）共501个图片
纸（paper）共594个图片
硬纸板（cardboard）共403个图片
塑料（plastic）共482个图片
金属（metal）共410个图片
一般垃圾（trash）共137个图片
物品都是放在白板上在日光/室内光源下拍摄的，压缩后的尺寸为512 * 384。

dataset from https://github.com/garythung/trashnet/tree/master/data
Unzip data/dataset-resized.zip

import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array, array_to_img
from tensorflow.keras.layers import Conv2D, Flatten, MaxPooling2D, Dense
from tensorflow.keras.models import Sequential

import glob, os, random

base_path ='/Users/zhangwenna/Desktop/dataset-resized'
img_list = glob.glob(os.path.join(base_path, '*/*.jpg'))
print(len(img_list))

# 我们总共有2527张图片。我们随机展示其中的6张图片
for i, img_path in enumerate(random.sample(img_list, 6)):
    img = load_img(img_path)
    img = img_to_array(img, dtype=np.uint8)
    
    plt.subplot(2, 3, i+1)
    plt.imshow(img.squeeze())

output1:
2527

image.png

#对数据进行分组
#ImageDataGenerator()是keras.preprocessing.image模块中的图片生成器，可以每一次给模型“喂”一个batch_size大小的样本数据，
#同时也可以在每一个批次中对这batch_size个样本数据进行增强，扩充数据集大小，增强模型的泛化能力。比如进行旋转，变形，归一化等等。

train_datagen = ImageDataGenerator(
    rescale=1./225, shear_range=0.1, zoom_range=0.1,
    width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True,
    vertical_flip=True, validation_split=0.1)
#shear_range 剪切强度（逆时针方向的剪切变换角度)
#validation_split: 保留用于验证的图像的比例（严格在0和1之间）
test_datagen = ImageDataGenerator(
    rescale=1./255, validation_split=0.1)
    
train_generator = train_datagen.flow_from_directory(
    base_path, target_size=(300, 300), batch_size=16,
    class_mode='categorical', subset='training', seed=0)

validation_generator = test_datagen.flow_from_directory(
    base_path, target_size=(300, 300), batch_size=16,
    class_mode='categorical', subset='validation', seed=0)

labels = (train_generator.class_indices)
labels = dict((v,k) for k,v in labels.items())

print(labels)

output2:
Found 2276 images belonging to 6 classes.
Found 251 images belonging to 6 classes.
{0: 'cardboard', 1: 'glass', 2: 'metal', 3: 'paper', 4: 'plastic', 5: 'trash'}

model = Sequential([
    Conv2D(filters=32, kernel_size=3, padding='same', activation='relu', input_shape=(300, 300, 3)),
    MaxPooling2D(pool_size=2),

    Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'),
    MaxPooling2D(pool_size=2),
    
    Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'),
    MaxPooling2D(pool_size=2),
    
    Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'),
    MaxPooling2D(pool_size=2),

    Flatten(),

    Dense(64, activation='relu'),

    Dense(6, activation='softmax')
])
#(交叉熵损失函数) 
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])
model.fit_generator(train_generator, epochs=100, steps_per_epoch=2276//32,validation_data=validation_generator,
                    validation_steps=251//32)

#参数steps_per_epoch是通过把训练图像的数量除以批次大小得出的。例如,有100张图像且批次大小为50,则steps_per_epoch值为2

output3:
Please use Model.fit, which supports generators.

Epoch 1/100
71/71 [==============================] - 221s 3s/step - loss: 1.7205 - acc: 0.2553 - val_loss: 1.6083 - val_acc: 0.3750
Epoch 2/100
71/71 [==============================] - 121s 2s/step - loss: 1.5227 - acc: 0.3803 - val_loss: 1.4301 - val_acc: 0.4464
Epoch 3/100
71/71 [==============================] - 123s 2s/step - loss: 1.4023 - acc: 0.4428 - val_loss: 1.5464 - val_acc: 0.4286
Epoch 4/100
71/71 [==============================] - 118s 2s/step - loss: 1.3962 - acc: 0.4489 - val_loss: 1.4306 - val_acc: 0.4286
Epoch 5/100
71/71 [==============================] - 116s 2s/step - loss: 1.3639 - acc: 0.4384 - val_loss: 1.3748 - val_acc: 0.4196
Epoch 6/100
71/71 [==============================] - 118s 2s/step - loss: 1.2870 - acc: 0.4850 - val_loss: 1.2453 - val_acc: 0.5536
Epoch 7/100
71/71 [==============================] - 117s 2s/step - loss: 1.2601 - acc: 0.5133 - val_loss: 1.4683 - val_acc: 0.4464
Epoch 8/100
71/71 [==============================] - 117s 2s/step - loss: 1.2186 - acc: 0.5088 - val_loss: 1.2113 - val_acc: 0.5000
Epoch 9/100
71/71 [==============================] - 115s 2s/step - loss: 1.1850 - acc: 0.5214 - val_loss: 1.3347 - val_acc: 0.4464
Epoch 10/100
71/71 [==============================] - 122s 2s/step - loss: 1.1420 - acc: 0.5423 - val_loss: 1.2093 - val_acc: 0.5536
Epoch 11/100
71/71 [==============================] - 112s 2s/step - loss: 1.0990 - acc: 0.5678 - val_loss: 1.1321 - val_acc: 0.5089
Epoch 12/100
71/71 [==============================] - 112s 2s/step - loss: 1.0840 - acc: 0.5721 - val_loss: 1.1863 - val_acc: 0.5357
Epoch 13/100
71/71 [==============================] - 155s 2s/step - loss: 1.0766 - acc: 0.5979 - val_loss: 1.4430 - val_acc: 0.4554
Epoch 14/100
71/71 [==============================] - 169s 2s/step - loss: 0.9695 - acc: 0.6338 - val_loss: 0.9983 - val_acc: 0.6518
Epoch 15/100
71/71 [==============================] - 119s 2s/step - loss: 0.9901 - acc: 0.6294 - val_loss: 1.1473 - val_acc: 0.5625
Epoch 16/100
71/71 [==============================] - 115s 2s/step - loss: 1.0062 - acc: 0.6406 - val_loss: 1.0303 - val_acc: 0.6250
Epoch 17/100
71/71 [==============================] - 114s 2s/step - loss: 0.9439 - acc: 0.6397 - val_loss: 1.0116 - val_acc: 0.5714
Epoch 18/100
71/71 [==============================] - 116s 2s/step - loss: 0.9797 - acc: 0.6391 - val_loss: 1.1799 - val_acc: 0.5268
Epoch 19/100
71/71 [==============================] - 115s 2s/step - loss: 0.9340 - acc: 0.6459 - val_loss: 1.0967 - val_acc: 0.5804
Epoch 20/100
71/71 [==============================] - 114s 2s/step - loss: 0.8780 - acc: 0.6708 - val_loss: 1.0752 - val_acc: 0.5804
Epoch 21/100
71/71 [==============================] - 114s 2s/step - loss: 0.8546 - acc: 0.6824 - val_loss: 1.1991 - val_acc: 0.5714
Epoch 22/100
71/71 [==============================] - 115s 2s/step - loss: 0.8694 - acc: 0.6628 - val_loss: 1.2398 - val_acc: 0.5357
Epoch 23/100
71/71 [==============================] - 117s 2s/step - loss: 0.8411 - acc: 0.6901 - val_loss: 1.1025 - val_acc: 0.6786
Epoch 24/100
71/71 [==============================] - 117s 2s/step - loss: 0.8107 - acc: 0.7130 - val_loss: 1.1774 - val_acc: 0.5536
Epoch 25/100
71/71 [==============================] - 116s 2s/step - loss: 0.8752 - acc: 0.6673 - val_loss: 0.8081 - val_acc: 0.6696
Epoch 26/100
71/71 [==============================] - 111s 2s/step - loss: 0.8150 - acc: 0.7020 - val_loss: 0.9926 - val_acc: 0.6518
Epoch 27/100
71/71 [==============================] - 113s 2s/step - loss: 0.7882 - acc: 0.7104 - val_loss: 0.9890 - val_acc: 0.6339
Epoch 28/100
71/71 [==============================] - 113s 2s/step - loss: 0.7705 - acc: 0.7201 - val_loss: 1.0953 - val_acc: 0.6071
Epoch 29/100
71/71 [==============================] - 112s 2s/step - loss: 0.7430 - acc: 0.7377 - val_loss: 0.8792 - val_acc: 0.6429
Epoch 30/100
71/71 [==============================] - 112s 2s/step - loss: 0.7626 - acc: 0.7210 - val_loss: 0.8883 - val_acc: 0.6518
Epoch 31/100
71/71 [==============================] - 113s 2s/step - loss: 0.8552 - acc: 0.6815 - val_loss: 1.3025 - val_acc: 0.4732
Epoch 32/100
71/71 [==============================] - 112s 2s/step - loss: 0.7941 - acc: 0.7069 - val_loss: 0.8129 - val_acc: 0.7054
Epoch 33/100
71/71 [==============================] - 109s 2s/step - loss: 0.7429 - acc: 0.7313 - val_loss: 0.8716 - val_acc: 0.6696
Epoch 34/100
71/71 [==============================] - 112s 2s/step - loss: 0.6959 - acc: 0.7536 - val_loss: 1.0984 - val_acc: 0.6250
Epoch 35/100
71/71 [==============================] - 111s 2s/step - loss: 0.7375 - acc: 0.7394 - val_loss: 0.8002 - val_acc: 0.6786
Epoch 36/100
71/71 [==============================] - 111s 2s/step - loss: 0.7072 - acc: 0.7333 - val_loss: 0.7551 - val_acc: 0.7321
Epoch 37/100
71/71 [==============================] - 113s 2s/step - loss: 0.7440 - acc: 0.7403 - val_loss: 1.1043 - val_acc: 0.5982
Epoch 38/100
71/71 [==============================] - 111s 2s/step - loss: 0.7527 - acc: 0.7174 - val_loss: 0.8664 - val_acc: 0.6964
Epoch 39/100
71/71 [==============================] - 111s 2s/step - loss: 0.6643 - acc: 0.7473 - val_loss: 0.8213 - val_acc: 0.7232
Epoch 40/100
71/71 [==============================] - 112s 2s/step - loss: 0.7021 - acc: 0.7456 - val_loss: 0.8613 - val_acc: 0.7143
Epoch 41/100
71/71 [==============================] - 113s 2s/step - loss: 0.6386 - acc: 0.7720 - val_loss: 1.0223 - val_acc: 0.6250
Epoch 42/100
71/71 [==============================] - 111s 2s/step - loss: 0.6568 - acc: 0.7447 - val_loss: 0.9515 - val_acc: 0.6786
Epoch 43/100
71/71 [==============================] - 112s 2s/step - loss: 0.6394 - acc: 0.7852 - val_loss: 0.7870 - val_acc: 0.6786
Epoch 44/100
71/71 [==============================] - 113s 2s/step - loss: 0.6437 - acc: 0.7676 - val_loss: 0.8471 - val_acc: 0.6696
Epoch 45/100
71/71 [==============================] - 111s 2s/step - loss: 0.6337 - acc: 0.7623 - val_loss: 0.8950 - val_acc: 0.6964
Epoch 46/100
71/71 [==============================] - 111s 2s/step - loss: 0.5993 - acc: 0.7820 - val_loss: 1.0203 - val_acc: 0.5625
Epoch 47/100
71/71 [==============================] - 117s 2s/step - loss: 0.6087 - acc: 0.7799 - val_loss: 1.0065 - val_acc: 0.5982
Epoch 48/100
71/71 [==============================] - 138s 2s/step - loss: 0.6285 - acc: 0.7722 - val_loss: 0.8781 - val_acc: 0.6875
Epoch 49/100
71/71 [==============================] - 121s 2s/step - loss: 0.5612 - acc: 0.7984 - val_loss: 1.2335 - val_acc: 0.6429
Epoch 50/100
71/71 [==============================] - 116s 2s/step - loss: 0.5699 - acc: 0.7984 - val_loss: 0.9054 - val_acc: 0.6607
Epoch 51/100
71/71 [==============================] - 110s 2s/step - loss: 0.6030 - acc: 0.7909 - val_loss: 0.9821 - val_acc: 0.6429
Epoch 52/100
71/71 [==============================] - 111s 2s/step - loss: 0.6945 - acc: 0.7412 - val_loss: 0.8685 - val_acc: 0.6786
Epoch 53/100
71/71 [==============================] - 113s 2s/step - loss: 0.5679 - acc: 0.7905 - val_loss: 0.8510 - val_acc: 0.6875
Epoch 54/100
71/71 [==============================] - 113s 2s/step - loss: 0.6023 - acc: 0.7835 - val_loss: 0.8247 - val_acc: 0.6518
Epoch 55/100
71/71 [==============================] - 112s 2s/step - loss: 0.5590 - acc: 0.7896 - val_loss: 0.7802 - val_acc: 0.7321
Epoch 56/100
71/71 [==============================] - 114s 2s/step - loss: 0.5679 - acc: 0.8028 - val_loss: 0.7660 - val_acc: 0.6964
Epoch 57/100
71/71 [==============================] - 110s 2s/step - loss: 0.5839 - acc: 0.8028 - val_loss: 0.7611 - val_acc: 0.7321
Epoch 58/100
71/71 [==============================] - 111s 2s/step - loss: 0.5590 - acc: 0.7967 - val_loss: 1.0786 - val_acc: 0.6071
Epoch 59/100
71/71 [==============================] - 116s 2s/step - loss: 0.5194 - acc: 0.8275 - val_loss: 0.7342 - val_acc: 0.7321
Epoch 60/100
71/71 [==============================] - 110s 2s/step - loss: 0.4677 - acc: 0.8185 - val_loss: 0.9167 - val_acc: 0.6786
Epoch 61/100
71/71 [==============================] - 109s 2s/step - loss: 0.4906 - acc: 0.8052 - val_loss: 0.7638 - val_acc: 0.7321
Epoch 62/100
71/71 [==============================] - 112s 2s/step - loss: 0.5267 - acc: 0.8081 - val_loss: 1.2296 - val_acc: 0.5982
Epoch 63/100
71/71 [==============================] - 111s 2s/step - loss: 0.5880 - acc: 0.7909 - val_loss: 0.8299 - val_acc: 0.6964
Epoch 64/100
71/71 [==============================] - 110s 2s/step - loss: 0.5203 - acc: 0.8203 - val_loss: 0.6984 - val_acc: 0.7589
Epoch 65/100
71/71 [==============================] - 110s 2s/step - loss: 0.5617 - acc: 0.8007 - val_loss: 0.8506 - val_acc: 0.6964
Epoch 66/100
71/71 [==============================] - 112s 2s/step - loss: 0.4157 - acc: 0.8530 - val_loss: 0.9649 - val_acc: 0.6786
Epoch 67/100
71/71 [==============================] - 110s 2s/step - loss: 0.4726 - acc: 0.8363 - val_loss: 0.7467 - val_acc: 0.7411
Epoch 68/100
71/71 [==============================] - 115s 2s/step - loss: 0.4825 - acc: 0.8247 - val_loss: 0.9306 - val_acc: 0.6964
Epoch 69/100
71/71 [==============================] - 110s 2s/step - loss: 0.4757 - acc: 0.8363 - val_loss: 1.1517 - val_acc: 0.6161
Epoch 70/100
71/71 [==============================] - 111s 2s/step - loss: 0.4948 - acc: 0.8185 - val_loss: 0.8304 - val_acc: 0.7054
Epoch 71/100
71/71 [==============================] - 110s 2s/step - loss: 0.5174 - acc: 0.8096 - val_loss: 0.8679 - val_acc: 0.6518
Epoch 72/100
71/71 [==============================] - 111s 2s/step - loss: 0.4799 - acc: 0.8274 - val_loss: 0.8524 - val_acc: 0.7321
Epoch 73/100
71/71 [==============================] - 112s 2s/step - loss: 0.4212 - acc: 0.8495 - val_loss: 1.0715 - val_acc: 0.6875
Epoch 74/100
71/71 [==============================] - 111s 2s/step - loss: 0.5003 - acc: 0.8078 - val_loss: 0.8279 - val_acc: 0.7143
Epoch 75/100
71/71 [==============================] - 111s 2s/step - loss: 0.4267 - acc: 0.8425 - val_loss: 0.7447 - val_acc: 0.7500
Epoch 76/100
71/71 [==============================] - 111s 2s/step - loss: 0.4268 - acc: 0.8371 - val_loss: 0.8244 - val_acc: 0.7500
Epoch 77/100
71/71 [==============================] - 111s 2s/step - loss: 0.4720 - acc: 0.8247 - val_loss: 0.8961 - val_acc: 0.6786
Epoch 78/100
71/71 [==============================] - 112s 2s/step - loss: 0.4979 - acc: 0.8204 - val_loss: 0.8691 - val_acc: 0.6429
Epoch 79/100
71/71 [==============================] - 111s 2s/step - loss: 0.4445 - acc: 0.8461 - val_loss: 1.0964 - val_acc: 0.5982
Epoch 80/100
71/71 [==============================] - 112s 2s/step - loss: 0.4660 - acc: 0.8283 - val_loss: 0.9248 - val_acc: 0.6607
Epoch 81/100
71/71 [==============================] - 111s 2s/step - loss: 0.4824 - acc: 0.8222 - val_loss: 1.2059 - val_acc: 0.6339
Epoch 82/100
71/71 [==============================] - 110s 2s/step - loss: 0.4382 - acc: 0.8354 - val_loss: 0.8243 - val_acc: 0.6875
Epoch 83/100
71/71 [==============================] - 111s 2s/step - loss: 0.3791 - acc: 0.8603 - val_loss: 1.3547 - val_acc: 0.5804
Epoch 84/100
71/71 [==============================] - 112s 2s/step - loss: 0.4175 - acc: 0.8468 - val_loss: 1.1149 - val_acc: 0.7321
Epoch 85/100
71/71 [==============================] - 113s 2s/step - loss: 0.6471 - acc: 0.7740 - val_loss: 1.0958 - val_acc: 0.6250
Epoch 86/100
71/71 [==============================] - 113s 2s/step - loss: 0.4434 - acc: 0.8504 - val_loss: 0.8250 - val_acc: 0.6696
Epoch 87/100
71/71 [==============================] - 112s 2s/step - loss: 0.3719 - acc: 0.8559 - val_loss: 0.8524 - val_acc: 0.7589
Epoch 88/100
71/71 [==============================] - 109s 2s/step - loss: 0.3978 - acc: 0.8532 - val_loss: 0.8410 - val_acc: 0.7321
Epoch 89/100
71/71 [==============================] - 111s 2s/step - loss: 0.4387 - acc: 0.8398 - val_loss: 0.8426 - val_acc: 0.7232
Epoch 90/100
71/71 [==============================] - 110s 2s/step - loss: 0.4056 - acc: 0.8594 - val_loss: 0.8563 - val_acc: 0.7232
Epoch 91/100
71/71 [==============================] - 111s 2s/step - loss: 0.3897 - acc: 0.8592 - val_loss: 0.7448 - val_acc: 0.7321
Epoch 92/100
71/71 [==============================] - 110s 2s/step - loss: 0.3947 - acc: 0.8541 - val_loss: 0.7799 - val_acc: 0.7321
Epoch 93/100
71/71 [==============================] - 109s 2s/step - loss: 0.4416 - acc: 0.8488 - val_loss: 0.9649 - val_acc: 0.6518
Epoch 94/100
71/71 [==============================] - 116s 2s/step - loss: 0.3962 - acc: 0.8550 - val_loss: 1.2210 - val_acc: 0.6607
Epoch 95/100
71/71 [==============================] - 124s 2s/step - loss: 0.4087 - acc: 0.8577 - val_loss: 1.0710 - val_acc: 0.6607
Epoch 96/100
71/71 [==============================] - 117s 2s/step - loss: 0.3748 - acc: 0.8671 - val_loss: 0.8149 - val_acc: 0.7589
Epoch 97/100
71/71 [==============================] - 114s 2s/step - loss: 0.3882 - acc: 0.8550 - val_loss: 1.1649 - val_acc: 0.6875
Epoch 98/100
71/71 [==============================] - 115s 2s/step - loss: 0.3485 - acc: 0.8719 - val_loss: 0.9793 - val_acc: 0.6786
Epoch 99/100
71/71 [==============================] - 118s 2s/step - loss: 0.4128 - acc: 0.8477 - val_loss: 1.0489 - val_acc: 0.6964
Epoch 100/100
71/71 [==============================] - 115s 2s/step - loss: 0.3668 - acc: 0.8644 - val_loss: 1.1848 - val_acc: 0.6250

结果展示下面我们随机抽取validation中的16张图片，展示图片以及其标签，并且给予我们的预测。我们发现预测的准确度还是蛮高的，对于大部分图片，都能识别出其类别。

test_x, test_y = validation_generator.__getitem__(1)

preds = model.predict(test_x)

plt.figure(figsize=(16, 16))
for i in range(16):
    plt.subplot(4, 4, i+1)
    plt.title('pred:%s / truth:%s' % (labels[np.argmax(preds[i])], labels[np.argmax(test_y[i])]))
    plt.imshow(test_x[i])

output4:

image.png

实际例子代码下载：https://github.com/wennaz/Deep_Learning

最后编辑于：2021.04.06 10:00:07

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