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https://github.com/conan7882/GoogLeNet-Inception

TensorFlow implementation of GoogLeNet and Inception for image classification.
https://github.com/conan7882/GoogLeNet-Inception

cifar googlenet image-classification inception pre-trained tensorflow

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TensorFlow implementation of GoogLeNet and Inception for image classification.

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README 

        
# GoogLeNet for Image Classification



- TensorFlow implementation of [Going Deeper with Convolutions](https://research.google.com/pubs/pub43022.html) (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](https://www.cs.toronto.edu/~kriz/cifar.html) dataset (93.64% accuracy on testing set). The pre-trained model on CIFAR-10 can be download from [here](https://www.dropbox.com/sh/kab0bzpy0zymljx/AAD2YCVm0J1Qmlor8EoPzgQda?dl=0).

- Architecture of GoogLeNet from the paper:

![googlenet](fig/arch.png)



## Requirements

- Python 3.3+

- [TensorFlow 1.9+](https://www.tensorflow.org/)

- [Numpy](http://www.numpy.org/)

- [Scipy](https://www.scipy.org/)



## Implementation Details



- The GoogLeNet model is defined in [`src/nets/googlenet.py`](src/nets/googlenet.py).

- Inception module is defined in [`src/models/inception_module.py`](src/models/inception_module.py).

- An example of image classification using pre-trained model is in [`examples/inception_pretrained.py`](examples/inception_pretrained.py).

- An example of train a network from scratch on CIFAR-10 is in [`examples/inception_cifar.py`](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](https://arxiv.org/abs/1502.03167) 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](https://www.dropbox.com/sh/axnbpd1oe92aoyd/AADpmuFIJTtxS7zkL_LZrROLa?dl=0). This is original from [here](http://www.deeplearningmodel.net/).

- Setup path in [`examples/inception_pretrained.py`](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](https://www.cs.toronto.edu/~kriz/cifar.html)

- Setup path in [`examples/inception_cifar.py`](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](https://www.dropbox.com/sh/kab0bzpy0zymljx/AAD2YCVm0J1Qmlor8EoPzgQda?dl=0).



#### 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](http://www.image-net.org/).

- Result of VGG19 for the same images can be found [here](https://github.com/conan7882/VGG-tensorflow#results). 

**The pre-processing of images for both experiments are the same.** 



*Data Source* | *Image* | *Result* |

|:--|:--:|:--|

[COCO](http://cocodataset.org/#home) || 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](http://cocodataset.org/#home) || 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](http://cocodataset.org/#home) ||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](http://cocodataset.org/#home) ||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](http://www.image-net.org/) ||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](https://github.com/conan7882/VGG-cifar-tf/blob/master/README.md#train-the-network-from-scratch-on-cifar-10) is a similar experiment using VGG19.



learning curve for training set 



![train_lc](fig/train_lc.png)



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.



![valid_lc](fig/valid_lc.png)



## Author

Qian Ge