Shuffling the training dataset with Tensorflow object detection api

Question

I'm working on a logo detection algorithm using the Faster-RCNN model with the Tensorflow object detection api. My dataset is alphabetically ordered (so there are a hundred adidas logo, then hundred apple logo etc.). And i would like it to be shuffled while training.

I've put some values in the config file:

train_input_reader:{
          shuffle: true
          queue_capacity: some value
          min_after_dequeue : some other value}

However whatever are the values, I'm putting in, algorithm is at first training on all of the a's logos (adidas, apple and so on) and only a lapse of time after starting to see the b's logos (bmw etc.) and the c's one etc.

Of course I could just shuffle my input dataset directly, but I would like to understand the logic behind it.

PS: I've seen this post about shuffling and min_after_dequeue, but I still dont quite get it. My batch size is 1 so it shouldn't be using tf.train.shuffle_batch() but only tf.RandomShuffleQueue

My training dataset size is 5000 and if I write min_after_dequeue: 4000 or 5000 it is still not shuffled right. Why though?

---

Update: @AllenLavoie It's a bit hard for me; as there is a lot of dependencies and I'm new to Tensorflow. But in the end the queue is constructed by

tf.contrib.slim.parallel_reader.parallel_read(    _, string_tensor = parallel_reader.parallel_read(
        config.input_path,
        reader_class=tf.TFRecordReader,
        num_epochs=(input_reader_config.num_epochs
                    if input_reader_config.num_epochs else None),
        num_readers=input_reader_config.num_readers,
        shuffle=input_reader_config.shuffle,
        dtypes=[tf.string, tf.string],
        capacity=input_reader_config.queue_capacity,
        min_after_dequeue=input_reader_config.min_after_dequeue)

It seems that when I'm putting num_readers = 1 in the config file the dataset is finally shuffling as I want, (at least in the beginning), but when there are more somehow on the start the logos are getting in the alphabetical order.

Answer 1

I recommend shuffling the dataset prior to training. The way shuffling currently happens is imperfect and my guess at what is happening is that at the beginning the queue starts off empty and only gets examples that start with 'A' --- after a while it may be more shuffled, but there is no getting around the beginning part when the queue hasn't been filled yet.