cropnet_cassava.ipynb

Kernel: Python 3

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CropNet: Cassava Disease Detection

在 TensorFlow.org 上查看

在 Google Colab 中运行

在 GitHub 上查看源代码

下载笔记本

查看 TF Hub 模型

此笔记本演示如何使用 TensorFlow Hub 中的 CropNet 木薯病虫害分类器模型。该模型可将木薯叶的图像分为 6 类：细菌性枯萎病、褐条病毒病、绿螨、花叶病、健康或未知。

此 Colab 演示了如何执行以下操作：

• 从 TensorFlow Hub 加载 https://tfhub.dev/google/cropnet/classifier/cassava_disease_V1/2 模型

• 从 TensorFlow Datasets (TFDS) 加载木薯数据集

• 将木薯叶图像分为 4 种不同的木薯病虫害类别、健康或未知。

• 评估分类器的准确率，并查看将模型在应用于域外图像时的鲁棒性。

导入和设置

!pip install matplotlib==3.2.2

import numpy as np
import matplotlib.pyplot as plt

import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_hub as hub

#@title Helper function for displaying examples
def plot(examples, predictions=None):
  # Get the images, labels, and optionally predictions
  images = examples['image']
  labels = examples['label']
  batch_size = len(images)
  if predictions is None:
    predictions = batch_size * [None]

  # Configure the layout of the grid
  x = np.ceil(np.sqrt(batch_size))
  y = np.ceil(batch_size / x)
  fig = plt.figure(figsize=(x * 6, y * 7))

  for i, (image, label, prediction) in enumerate(zip(images, labels, predictions)):
    # Render the image
    ax = fig.add_subplot(x, y, i+1)
    ax.imshow(image, aspect='auto')
    ax.grid(False)
    ax.set_xticks([])
    ax.set_yticks([])

    # Display the label and optionally prediction
    x_label = 'Label: ' + name_map[class_names[label]]
    if prediction is not None:
      x_label = 'Prediction: ' + name_map[class_names[prediction]] + '\n' + x_label
      ax.xaxis.label.set_color('green' if label == prediction else 'red')
    ax.set_xlabel(x_label)

  plt.show()

数据集

让我们从 TFDS 中加载木薯数据集

dataset, info = tfds.load('cassava', with_info=True)

我们来查看数据集信息以了解更多内容，例如描述和引用以及有关可用样本量的信息

info

木薯数据集包含涉及 4 种不同病虫害的木薯叶图像以及健康的木薯叶图像。模型可以预测上述五个类，当模型不确定其预测结果时，会将图像分为第六个类，即“未知”类。

# Extend the cassava dataset classes with 'unknown'
class_names = info.features['label'].names + ['unknown']

# Map the class names to human readable names
name_map = dict(
    cmd='Mosaic Disease',
    cbb='Bacterial Blight',
    cgm='Green Mite',
    cbsd='Brown Streak Disease',
    healthy='Healthy',
    unknown='Unknown')

print(len(class_names), 'classes:')
print(class_names)
print([name_map[name] for name in class_names])

将数据馈送至模型之前，我们需要进行一些预处理。模型接受大小为 224 x 224，且 RGB 通道值范围为 [0, 1] 的图像。让我们归一化图像并调整图像大小。

def preprocess_fn(data):
  image = data['image']

  # Normalize [0, 255] to [0, 1]
  image = tf.cast(image, tf.float32)
  image = image / 255.

  # Resize the images to 224 x 224
  image = tf.image.resize(image, (224, 224))

  data['image'] = image
  return data

我们看一下数据集中的一些样本

batch = dataset['validation'].map(preprocess_fn).batch(25).as_numpy_iterator()
examples = next(batch)
plot(examples)

模型

让我们从 TF-Hub 中加载分类器并获取一些预测结果，然后查看模型对一些样本的预测

classifier = hub.KerasLayer('https://tfhub.dev/google/cropnet/classifier/cassava_disease_V1/2')
probabilities = classifier(examples['image'])
predictions = tf.argmax(probabilities, axis=-1)

plot(examples, predictions)

评估和鲁棒性

我们来衡量分类器在拆分数据集上的准确率。我们还可以通过评估模型在非木薯数据集上的性能来评估其鲁棒性。对于 iNaturalist 或豆科植物等其他植物数据集中的图像，模型应几乎始终返回未知。

#@title Parameters {run: "auto"}

DATASET = 'cassava'  #@param {type:"string"} ['cassava', 'beans', 'i_naturalist2017']
DATASET_SPLIT = 'test' #@param {type:"string"} ['train', 'test', 'validation']
BATCH_SIZE =  32 #@param {type:"integer"}
MAX_EXAMPLES = 1000 #@param {type:"integer"}

def label_to_unknown_fn(data):
  data['label'] = 5  # Override label to unknown.
  return data

# Preprocess the examples and map the image label to unknown for non-cassava datasets.
ds = tfds.load(DATASET, split=DATASET_SPLIT).map(preprocess_fn).take(MAX_EXAMPLES)
dataset_description = DATASET
if DATASET != 'cassava':
  ds = ds.map(label_to_unknown_fn)
  dataset_description += ' (labels mapped to unknown)'
ds = ds.batch(BATCH_SIZE)

# Calculate the accuracy of the model
metric = tf.keras.metrics.Accuracy()
for examples in ds:
  probabilities = classifier(examples['image'])
  predictions = tf.math.argmax(probabilities, axis=-1)
  labels = examples['label']
  metric.update_state(labels, predictions)

print('Accuracy on %s: %.2f' % (dataset_description, metric.result().numpy()))

了解更多

• 详细了解 TensorFlow Hub 上的模型：https://tfhub.dev/google/cropnet/classifier/cassava_disease_V1/2

• 了解如何使用模型的 TensorFlow Lite 版本通过 ML Kit 构建在手机上运行的自定义图像分类器。