Image-Object-Localization

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Image-Object-Localization

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Image Object Localization by ResNet-18 using tensorflow, keras and pytorch

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### Description

Given a picture with a bird, we are supposed to box the bird.

In src/data directory, ```images.txt``` is the index of all images, ```bouding_boxex.txt``` is the label box of all images and  ```images``` contains all images. Box data make up of 4 data: the top left corner coordinate of box, width of box and height of box.

### Neural Network

For traditional CNN and FC, it will meet degeneration problems when layers go deep.

In paper ```Deep Residual Learning for Image Recognition```, they try to solve this problem by using a Residual Block:

These blocks compose ResNet:

I use ResNet-18 in this project by adding a 4-dimension layer after ResNet-18 to predict box's x, y ,w and h.

Loss: smooth l1 loss

Metric: IoU of groound truth and prediction, threshold=0.75

### Train

Resize all images to ```224*224*3```

Then normalize and standardize all pixel channel.

Split all data into 9000 training data and 2788 tesing data. Train network on training data using ```batch size=128```, ```epoch=100``` and ```validation split ratio=0.1```

Training result:

Testing result:

### Examples

Red box represents ground truth and green box is the prediction of network.

Failed example:

### Attention

You should keep the directory structure.

### Dependency

python 3.6

tensorflow 1.3.0

keras 2.1.0

numpy

PIL

pickle

matplotlib

### Run

In ```src``` directory:

```python getdata.py``` to preprocess data.

If you want to train model, ```python train.py```

If you want to test on trained model(if you had trained model), ```python test.py```

### Reference

Deep Residual Learning for Image Recognition: https://arxiv.org/pdf/1512.03385.pdf

### Author

ckczzj

### Licence

MIT

Image-Object-Localization

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