CropNet：木薯病害检测

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此笔记本展示了如何使用 CropNet 木薯病害分类器模型（来自 TensorFlow Hub）。该模型将木薯叶子的图像分类为 6 类之一：细菌性枯萎病、褐条病、绿螨、花叶病、健康或未知。

此 Colab 演示了如何

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
将木薯叶子的图像分类为 4 种不同的木薯病害类别，或分类为健康或未知。
评估分类器的准确率，并查看将模型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

用于显示示例的辅助函数

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 加载木薯数据集

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

2024-03-09 13:44:07.128854: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected

让我们看一下数据集信息，以了解有关它的更多信息，例如描述和引用，以及有关可用示例数量的信息

info

tfds.core.DatasetInfo(
    name='cassava',
    full_name='cassava/0.1.0',
    description="""
    Cassava consists of leaf images for the cassava plant depicting healthy and
    four (4) disease conditions; Cassava Mosaic Disease (CMD), Cassava Bacterial
    Blight (CBB), Cassava Greem Mite (CGM) and Cassava Brown Streak Disease (CBSD).
    Dataset consists of a total of 9430 labelled images.
    The 9430 labelled images are split into a training set (5656), a test set(1885)
    and a validation set (1889). The number of images per class are unbalanced with
    the two disease classes CMD and CBSD having 72% of the images.
    """,
    homepage='https://www.kaggle.com/c/cassava-disease/overview',
    data_dir='gs://tensorflow-datasets/datasets/cassava/0.1.0',
    file_format=tfrecord,
    download_size=1.26 GiB,
    dataset_size=Unknown size,
    features=FeaturesDict({
        'image': Image(shape=(None, None, 3), dtype=uint8),
        'image/filename': Text(shape=(), dtype=string),
        'label': ClassLabel(shape=(), dtype=int64, num_classes=5),
    }),
    supervised_keys=('image', 'label'),
    disable_shuffling=False,
    splits={
        'test': <SplitInfo num_examples=1885, num_shards=4>,
        'train': <SplitInfo num_examples=5656, num_shards=8>,
        'validation': <SplitInfo num_examples=1889, num_shards=4>,
    },
    citation="""@misc{mwebaze2019icassava,
        title={iCassava 2019Fine-Grained Visual Categorization Challenge},
        author={Ernest Mwebaze and Timnit Gebru and Andrea Frome and Solomon Nsumba and Jeremy Tusubira},
        year={2019},
        eprint={1908.02900},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
    }""",
)

木薯数据集包含木薯叶子的图像，这些图像具有 4 种不同的疾病，以及健康的木薯叶子。该模型可以预测所有这些类别，以及当模型对其预测没有信心时，第六类“未知”。

# 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])

6 classes:
['cbb', 'cbsd', 'cgm', 'cmd', 'healthy', 'unknown']
['Bacterial Blight', 'Brown Streak Disease', 'Green Mite', 'Mosaic Disease', 'Healthy', 'Unknown']

在将数据馈送到模型之前，我们需要进行一些预处理。该模型期望 224 x 224 的图像，RGB 通道值在 [0, 1] 之间。让我们对图像进行归一化和调整大小。

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

让我们看一下数据集中的几个示例

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

png

模型

让我们从 TF Hub 加载分类器，并获得一些预测，并查看模型对几个示例的预测

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)

png

评估和鲁棒性

让我们在数据集的拆分上测量分类器的准确率。我们还可以查看模型的鲁棒性，方法是评估其在非木薯数据集上的性能。对于其他植物数据集（如 iNaturalist 或豆类）的图像，该模型几乎总是应该返回未知。

参数

DATASET = 'cassava' 
DATASET_SPLIT = 'test'
BATCH_SIZE =  32
MAX_EXAMPLES = 1000

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()))

Accuracy on cassava: 0.88
2024-03-09 13:44:27.693415: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence

了解更多

在 TensorFlow Hub 上了解有关该模型的更多信息：https://tfhub.dev/google/cropnet/classifier/cassava_disease_V1/2
了解如何使用 ML Kit 构建在移动电话上运行的自定义图像分类器，以及此模型的 TensorFlow Lite 版本。