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https://github.com/caiocarneloz/kerfex
Keras generic feature extraction using pre-trained CNN's.
https://github.com/caiocarneloz/kerfex
cnn feature-extraction image-recognition imagenet keras
Last synced: 10 days ago
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Keras generic feature extraction using pre-trained CNN's.
- Host: GitHub
- URL: https://github.com/caiocarneloz/kerfex
- Owner: caiocarneloz
- License: mit
- Created: 2021-03-18T00:27:49.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2021-03-23T20:38:15.000Z (almost 4 years ago)
- Last Synced: 2024-12-15T23:26:05.272Z (16 days ago)
- Topics: cnn, feature-extraction, image-recognition, imagenet, keras
- Language: Python
- Homepage:
- Size: 172 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# kerfex
Generic feature extraction using [keras pre-built CNN's](https://keras.io/api/applications/) with imagenet weights.
## Getting Started
#### Dependencies
You need Python 3.7 or later to run **kerfex**. You can find it at [python.org](https://www.python.org/).
You aso need pandas, numpy, keras and tensorflow packages, which is available from [PyPI](https://pypi.org). If you have PyPI, run:
```
pip install pandas numpy keras tf-nightly
```
#### Installation
Clone this repo to your local machine using:
```
git clone https://github.com/caiocarneloz/kerfex.git
```
Or install it using pip:
```
pip install kerfex
```
### Usage
The [demo.py](https://github.com/caiocarneloz/kerfex/blob/main/demo.py) file shows a simple example using VGG16 with three [Unsplash](https://unsplash.com/) images from the authors [@mybibimbaplife](https://unsplash.com/@mybibimbaplife), [@davidbraud](https://unsplash.com/@davidbraud), and [@analoglugunler](https://unsplash.com/@analoglugunler). The "extract" function requires:
- **CNN instance itself**
- **CNN pre-processing module**
- **List of images**
- **Images shape**
As return, the function will send a pandas Dataframe containing the numerical features extracted from every image, where each line represents a single image and each column represents a single feature:
```
0 1 2 ... 15599 15600 15601
0 0.000000 0.000000 0.000000 ... 0.000000 0.000000 3.754401
1 0.000000 15.284859 37.369953 ... 22.756908 6.398854 0.000000
2 12.172541 0.000000 0.000000 ... 0.000000 0.000000 0.000000
```