Question :
I am new to TensorFlow. I am looking for the help on the image recognition where I can train my own image dataset.
Is there any example for training the new dataset?
Answer #1:
If you are interested in how to input your own data in TensorFlow, you can look at this tutorial.
I’ve also written a guide with best practices for CS230 at Stanford here.
New answer (with tf.data
) and with labels
With the introduction of tf.data
in r1.4
, we can create a batch of images without placeholders and without queues. The steps are the following:
- Create a list containing the filenames of the images and a corresponding list of labels
- Create a
tf.data.Dataset
reading these filenames and labels - Preprocess the data
- Create an iterator from the
tf.data.Dataset
which will yield the next batch
The code is:
# step 1
filenames = tf.constant(['im_01.jpg', 'im_02.jpg', 'im_03.jpg', 'im_04.jpg'])
labels = tf.constant([0, 1, 0, 1])
# step 2: create a dataset returning slices of `filenames`
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
# step 3: parse every image in the dataset using `map`
def _parse_function(filename, label):
image_string = tf.read_file(filename)
image_decoded = tf.image.decode_jpeg(image_string, channels=3)
image = tf.cast(image_decoded, tf.float32)
return image, label
dataset = dataset.map(_parse_function)
dataset = dataset.batch(2)
# step 4: create iterator and final input tensor
iterator = dataset.make_one_shot_iterator()
images, labels = iterator.get_next()
Now we can run directly sess.run([images, labels])
without feeding any data through placeholders.
Old answer (with TensorFlow queues)
To sum it up you have multiple steps:
- Create a list of filenames (ex: the paths to your images)
- Create a TensorFlow filename queue
- Read and decode each image, resize them to a fixed size (necessary for batching)
- Output a batch of these images
The simplest code would be:
# step 1
filenames = ['im_01.jpg', 'im_02.jpg', 'im_03.jpg', 'im_04.jpg']
# step 2
filename_queue = tf.train.string_input_producer(filenames)
# step 3: read, decode and resize images
reader = tf.WholeFileReader()
filename, content = reader.read(filename_queue)
image = tf.image.decode_jpeg(content, channels=3)
image = tf.cast(image, tf.float32)
resized_image = tf.image.resize_images(image, [224, 224])
# step 4: Batching
image_batch = tf.train.batch([resized_image], batch_size=8)
Answer #2:
Based on @olivier-moindrot’s answer, but for Tensorflow 2.0+:
# step 1
filenames = tf.constant(['im_01.jpg', 'im_02.jpg', 'im_03.jpg', 'im_04.jpg'])
labels = tf.constant([0, 1, 0, 1])
# step 2: create a dataset returning slices of `filenames`
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
def im_file_to_tensor(file, label):
def _im_file_to_tensor(file, label):
path = f"../foo/bar/{file.numpy().decode()}"
im = tf.image.decode_jpeg(tf.io.read_file(path), channels=3)
im = tf.cast(image_decoded, tf.float32) / 255.0
return im, label
return tf.py_function(_im_file_to_tensor,
inp=(file, label),
Tout=(tf.float32, tf.uint8))
dataset = dataset.map(im_file_to_tensor)
If you are hitting an issue similar to:
ValueError: Cannot take the length of Shape with unknown rank
when passing tf.data.Dataset tensors to model.fit, then take a look at https://github.com/tensorflow/tensorflow/issues/24520. A fix for the code snippet above would be:
def im_file_to_tensor(file, label):
def _im_file_to_tensor(file, label):
path = f"../foo/bar/{file.numpy().decode()}"
im = tf.image.decode_jpeg(tf.io.read_file(path), channels=3)
im = tf.cast(image_decoded, tf.float32) / 255.0
return im, label
file, label = tf.py_function(_im_file_to_tensor,
inp=(file, label),
Tout=(tf.float32, tf.uint8))
file.set_shape([192, 192, 3])
label.set_shape([])
return (file, label)
Answer #3:
2.0 Compatible Answer using Tensorflow Hub: Tensorflow Hub
is a Provision/Product Offered by Tensorflow
, which comprises the Models developed by Google, for Text and Image Datasets.
It saves Thousands of Hours of Training Time and Computational Effort
, as it reuses the Existing Pre-Trained Model.
If we have an Image Dataset, we can take the Existing Pre-Trained Models from TF Hub and can adopt it to our Dataset.
Code for Re-Training our Image Dataset using the Pre-Trained Model, MobileNet, is shown below:
import itertools
import os
import matplotlib.pylab as plt
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
module_selection = ("mobilenet_v2_100_224", 224) #@param ["("mobilenet_v2_100_224"", 224)"", ""(""inception_v3"", 299)""] {type:""raw"", allow-input: true}