site/en/hub/tutorials/cropnet_cassava.ipynb
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This notebook shows how to use the CropNet cassava disease classifier model from TensorFlow Hub. The model classifies images of cassava leaves into one of 6 classes: bacterial blight, brown streak disease, green mite, mosaic disease, healthy, or unknown.
This colab demonstrates how to:
!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()
Let's load the cassava dataset from TFDS
dataset, info = tfds.load('cassava', with_info=True)
Let's take a look at the dataset info to learn more about it, like the description and citation and information about how many examples are available
info
The cassava dataset has images of cassava leaves with 4 distinct diseases as well as healthy cassava leaves. The model can predict all of these classes as well as sixth class for "unknown" when the model is not confident in its prediction.
# 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])
Before we can feed the data to the model, we need to do a bit of preprocessing. The model expects 224 x 224 images with RGB channel values in [0, 1]. Let's normalize and resize the images.
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
Let's take a look at a few examples from the dataset
batch = dataset['validation'].map(preprocess_fn).batch(25).as_numpy_iterator()
examples = next(batch)
plot(examples)
Let's load the classifier from TF Hub and get some predictions and see the predictions of the model is on a few examples
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)
Let's measure the accuracy of our classifier on a split of the dataset. We can also look at the robustness of the model by evaluating its performance on a non-cassava dataset. For image of other plant datasets like iNaturalist or beans, the model should almost always return unknown.
#@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()))