# Image-Retrieval

**Repository Path**: lin3150/Image-Retrieval

## Basic Information

- **Project Name**: Image-Retrieval
- **Description**: Image retrieval program made in Tensorflow supporting VGG16, VGG19, InceptionV3 and InceptionV4 pretrained networks and own trained Convolutional autoencoder.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-07-27
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Image-Retrieval
Image retrieval program made in Tensorflow supporting the pretrained networks VGG16, VGG19, InceptionV3 and InceptionV4 and own trained Convolutional Autoencoder that you can train with this tool.


### Requirements
* Python 3.*
* Tensorflow
* Pillow
* tqdm
* Pretrained VGG16, VGG19, InceptionV3 or InceptionV4 network or own trained Convolutional Autoencoder.


### Usage
Firstly put your images in the **images** folder.

**Embedding images and saving them**

**Get embedding from trained Convolutional autoencoder**
To train a Convolutional autoencoder to vectorize images do this command:
```
python3 autoencoder_training.py
```
You can get a look at the hyperparameters using.
```
python3 autoencoder_training.py --help
```
The same principles follow in all the other scripts.

**Embedding with autoencoder**
Just do this command.
```
python3 vectorize_autoencoder.py
```


**Get embedding from pretrained models**
Just do this command.
```
python3 vectorize_pretrained.py --model_path=<model_path> --model_type=<model_type> --layer_to_extract=<layer_to_extract>
```
What does these arguments mean?

**model_path**: Path to pretrained model. e.g ./inception_v4.ckpt

**model_type**: Type of model, either VGG16, VGG19, InceptionV3 or InceptionV4. e.g InceptionV4

**layer_to_extract**: Which layer to take vector from. e.g Mixed_7a

This command will save the vectors in a file in the vectors folder and will print out the path to the vectors for later
use or evaluation at the end of the program.


**Evaluating**
To evaluate your vectors you can do this command.
```
python3 evaluation.py --vectors_path=<vectors_path> --image_path=<image_path>
```
What does these arguments mean?

**vectors_path**: Where vectors are saved. e.g vectors/vectors_1

**image_path**: Image to evaluate on, i.e the image to check nearest neighbour on. e.g img.jpg


### Todos
* Dimensionality reduction using PCA.
* More ways of doing NN search.
* Clean the code!
* Adversarial loss on autoencoder.


### Other
Made by Oliver Edholm, 14 years old.