GoogLeNet for Image Classification

• TensorFlow implementation of Going Deeper with Convolutions (CVPR'15).
• 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.
• When testing the pre-trained model, images are rescaled so that the shorter dimension is 224. This is not the same as the original paper which is an ensemle of 7 similar models using 144 224x224 crops per image for testing. So the performance will not be as good as the original paper.

Usage

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Download pre-trained model

Download the pre-trained parameters here. This is original from here.

Config path

All directories are setup in example/setup_env.py.

• PARA_DIR is the path of the pre-trained model.
• SAVE_DIR is the directory to save graph summary for tensorboard.
• DATA_DIR is the directory to put testing images.

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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 vgg 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.

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

Author

Qian Ge