Googlenet Inception

TensorFlow implementation of GoogLeNet and Inception for image classification.
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GoogLeNet for Image Classification

  • 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: googlenet


Implementation Details

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.


ImageNet Classification


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


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

python --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


  • Download CIFAR-10 dataset from here
  • Setup path in examples/ 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 --train \
  --bsize BATCH_SIZE \
  --keep_prob KEEP_PROB_OF_DROPOUT \
  • 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 --eval \
  • 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.


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

Train the network from scratch on CIFAR-10

  • Here is a similar experiment using VGG19.

learning curve for training set


learning curve for testing set

  • The accuracy on testing set is 93.64% around 100 epochs. We can observe the slightly over-fitting behavior at the end of training.



Qian Ge

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