GoogLeNet for Image Classification

TensorFlow implementation of Going Deeper with Convolutions (CVPR'15).

This repository contains the examples of natural image classification using pre-trained model as well as training a Inception network from scratch on CIFAR-10 dataset (93.64% accuracy on testing set). The pre-trained model on CIFAR-10 can be download from here.
Architecture of GoogLeNet from the paper:

Requirements

Python 3.3+
TensorFlow 1.9+
Numpy
Scipy

Implementation Details

The GoogLeNet model is defined in src/nets/googlenet.py.
Inception module is defined in src/models/inception_module.py.
An example of image classification using pre-trained model is in examples/inception_pretrained.py.
An example of train a network from scratch on CIFAR-10 is in examples/inception_cifar.py.

For testing the pre-trained model

Images are rescaled so that the smallest side equals 224 before fed into the model. This is not the same as the original paper which is an ensemble of 7 similar models using 144 224x224 crops per image for testing. So the performance will not be as good as the original paper.

For training from scratch on CIFAR-10

All the LRN layers are removed from the convolutional layers.
Batch normalization and ReLU activation are used in all the convolutional layers including the layers in Inception structure except the output layer.
Two auxiliary classifiers are used as mentioned in the paper, though 512 instead of 1024 hidden units are used in the two fully connected layers to reduce the computation. However, I found the results are almost the same on CIFAR-10 with and without auxiliary classifiers.
Since the 32 x 32 images are down-sampled to 1 x 1 before fed into inception_5a, this makes the multi-scale structure of inception layers less useful and harm the performance (around 80% accuracy). To make full use of the multi-scale structures, the stride of the first convolutional layer is reduced to 1 and the first two max pooling layers are removed. The the feature map (32 x 32 x channels) will have almost the same size as described in table 1 (28 x 28 x channel) in the paper before fed into inception_3a. I have also tried only reduce the stride or only remove one max pooling layer. But I found the current setting provides the best performance on the testing set.
During training, dropout with keep probability 0.4 is applied to two fully connected layers and weight decay with 5e-4 is used as well.
The network is trained through Adam optimizer. Batch size is 128. The initial learning rate is 1e-3, decays to 1e-4 after 30 epochs, and finally decays to 1e-5 after 50 epochs.
Each color channel of the input images are subtracted by the mean value computed from the training set.

Usage

ImageNet Classification

Preparation

Download the pre-trained parameters here. This is original from here.
Setup path in examples/inception_pretrained.py: PRETRINED_PATH is the path for pre-trained model. DATA_PATH is the path to put testing images.

Run

Go to examples/ and put test image in folder DATA_PATH, then run the script:

python inception_pretrained.py --im_name PART_OF_IMAGE_NAME

--im_name is the option for image names you want to test. If the testing images are all png files, this can be png. The default setting is .jpg.
The output will be the top-5 class labels and probabilities.

Train the network on CIFAR-10

Preparation

Download CIFAR-10 dataset from here
Setup path in examples/inception_cifar.py: DATA_PATH is the path to put CIFAR-10. SAVE_PATH is the path to save or load summary file and trained model.

Train the model

Go to examples/ and run the script:

python inception_cifar.py --train 
  --lr LEARNING_RATE 
  --bsize BATCH_SIZE 
  --keep_prob KEEP_PROB_OF_DROPOUT 
  --maxepoch MAX_TRAINING_EPOCH

Summary and model will be saved in SAVE_PATH. One pre-trained model on CIFAR-10 can be downloaded from here.

Evaluate the model

Go to examples/ and put the pre-trained model in SAVE_PATH. Then run the script:

python inception_cifar.py --eval 
  --load PRE_TRAINED_MODEL_ID

The pre-trained ID is epoch ID shown in the save modeled file name. The default value is 99, which indicates the one I uploaded.
The output will be the accuracy of training and testing set.

Results

Image classification using pre-trained model

Top five predictions are shown. The probabilities are shown keeping two decimal places. Note that the pre-trained model are trained on ImageNet.
Result of VGG19 for the same images can be found here. The pre-processing of images for both experiments are the same.

Data Source	Image	Result
COCO		1: probability: 1.00, label: brown bear, bruin, Ursus arctos 2: probability: 0.00, label: ice bear, polar bear 3: probability: 0.00, label: hyena, hyaena 4: probability: 0.00, label: chow, chow chow 5: probability: 0.00, label: American black bear, black bear
COCO		1: probability: 0.79, label: street sign 2: probability: 0.06, label: traffic light, traffic signal, stoplight 3: probability: 0.03, label: parking meter 4: probability: 0.02, label: mailbox, letter box 5: probability: 0.01, label: balloon
COCO		1: probability: 0.94, label: trolleybus, trolley coach 2: probability: 0.05, label: passenger car, coach, carriage 3: probability: 0.00, label: fire engine, fire truck 4: probability: 0.00, label: streetcar, tram, tramcar, trolley 5: probability: 0.00, label: minibus
COCO		1: probability: 0.35, label: burrito 2: probability: 0.17, label: potpie 3: probability: 0.14, label: mashed potato 4: probability: 0.10, label: plate 5: probability: 0.03, label: pizza, pizza pie
ImageNet		1: probability: 1.00, label: goldfish, Carassius auratus 2: probability: 0.00, label: rock beauty, Holocanthus tricolor 3: probability: 0.00, label: puffer, pufferfish, blowfish, globefish 4: probability: 0.00, label: tench, Tinca tinca 5: probability: 0.00, label: anemone fish
Self Collection		1: probability: 0.32, label: Egyptian cat 2: probability: 0.30, label: tabby, tabby cat 3: probability: 0.05, label: tiger cat 4: probability: 0.02, label: mouse, computer mouse 5: probability: 0.02, label: paper towel
Self Collection		1: probability: 1.00, label: streetcar, tram, tramcar, trolley, trolley car 2: probability: 0.00, label: passenger car, coach, carriage 3: probability: 0.00, label: trolleybus, trolley coach, trackless trolley 4: probability: 0.00, label: electric locomotive 5: probability: 0.00, label: freight car