TensorFlow: training on my own image

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.

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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:

1. Create a list containing the filenames of the images and a corresponding list of labels
2. Create a tf.data.Dataset reading these filenames and labels
3. Preprocess the data
4. 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.

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Old answer (with TensorFlow queues)

To sum it up you have multiple steps:

1. Create a list of filenames (ex: the paths to your images)
2. Create a TensorFlow filename queue
3. Read and decode each image, resize them to a fixed size (necessary for batching)
4. Output a batch of these images

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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)