FGVR Tensorflow Baseline

This repo contains training code for FGVR classification using TensorFlow. This implementation achieves comparable state-of-the-art results.

Checkout the Wiki for more technical discussion.

FVGR is a classification task where intra category visual differences are small and can be overwhelmed by factors such as pose, viewpoint, or location of the object in the image. For instance, the following image shows a California gull (left) and a Ringed-beak gull (Right). The beak pattern difference is the key for a correct classification. Such a difference is tiny when compared to the intra-category variations like pose and illumination. FVGR dataset typically involve animal species, models of cars or aircrafts. The following table list six well-known FGVR datasets.

Dataset	Num Classes	Avg samples Per Class	Train Size	Val Size	Test Size
Flowers-102	102	10	1020	1020	6149
CUB-200-2011	200	29.97	5994	N/A	5794
Stanford Cars	196	41.55	8144	N/A	8041
NABirds	550	43.5	23929	N/A	24633
Aircrafts	100	33.34	3334	3333	3333
Stanford Dogs	120	100	12000	N/A	8580

Requirements

• Python 3+ [Tested on 3.4.7]
• Tensorflow 1+ [Tested on 1.8]

Datasets

I prepare my datasets in an un-conviention way. dataset_sample folder provides an example for the cars dataset. Instead of caffe style, listing files and labels in txt file, I use csv file. Reading dataset content in Excel is more appealing, to me, than txt file. To use caffe txt dataset style, make sure to modify CarsTupleLoader and BaseTupleLoader. This should be trivial since these classes return a list of filenames and labels

Preliminary Results

Augmentation using random cropping and horizontal flipping on. No color distortion, vertical flipping or any complex augmentation is employed. The results are preliminary because I didn't wait for till max_iters. Other datasets results and other models like resnet50 will be added later.

Dataset	DenseNet161	ResNet50 V2
Flowers-102	93.39	85.59
CUB-200-2011	82.2	69.43
Stanford Cars	91.13	86.84
NABirds	78.80	65.06
Aircrafts	88.65	83.49
Stanford Dogs	81.60	70.36

Running

base_config contains all the parameters needed to train the model. The main function in fgvr_train.py shows how to set these parameters.

Run python fgvr_train.py

Credits:

The following deserve credit for the tips and help provided to finish this code and achieve the reported results

• Accumulated Gradient in Tensorflow.
• Training and Evaluating at the same time.
• Batch Level Augmentation.
• Pretrained-DenseNet

TODO LIST

• [Done] Write a Wiki about Accumulated Gradient in Tensorflow
• [Done] Write a Wiki about Batch normalization and how to train and evaluate concurrently.
• [Done] Report results of these fgvr datasets
• [Done] Add ResNet implementation
• [Done] Add other dataset loaders
• Add separate evaluation.py to eval a trained model

Contributor list

1. Ahmed Taha

Both tips to improve the code and pull requests to contribute are very welcomed