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tensorflow
GitHub Repository: tensorflow/docs-l10n
 Path: blob/master/site/en-snapshot/hub/tutorials/cropnet_cassava.ipynb
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Kernel: Python 3
# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

CropNet: Cassava Disease Detection

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:

  • Load the https://tfhub.dev/google/cropnet/classifier/cassava_disease_V1/2 model from TensorFlow Hub

  • Load the cassava dataset from TensorFlow Datasets (TFDS)

  • Classify images of cassava leaves into 4 distinct cassava disease categories or as healthy or unknown.

  • Evaluate the accuracy of the classifier and look at how robust the model is when applied to out of domain images.

Imports and setup

!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()

Dataset

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 it's 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)

Model

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)

Evaluation & robustness

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()))

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