I have been studying transfer learning with models like inception_v4 and inception_resnet_v2. Found some projects that uses bottleneck and some uses tfrecords to store the training images. When retraining the inception_v4 model with the same data using those two methods bottleneck gave 95% accuracy and tfrecord only gave 75%. But, all the new projects seems to use tfrecords for data and .ckpt format to store the model. Can someone explain me whats the difference and which one is better in which case
If you are working with large datasets, using a binary file format for storage of your data can have a significant impact on the performance of your import pipeline. Hence, it will affect your training time of the model.
By using TFRecords, it is possible to store sequence data. For e.g, a series of data. Besides, it easy to combine multiple datasets and integrates seamlessly with the data import and preprocessing functionality provided by the library.
For more information about TFrecords, please refer this link.