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https://github.com/vascofazza/indoor-outdoor-image-classifier
A CNN-based image classifier capable of detecting if a scene is indoors or outdoors.
https://github.com/vascofazza/indoor-outdoor-image-classifier
Last synced: 27 days ago
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A CNN-based image classifier capable of detecting if a scene is indoors or outdoors.
- Host: GitHub
- URL: https://github.com/vascofazza/indoor-outdoor-image-classifier
- Owner: vascofazza
- License: mit
- Created: 2019-09-29T10:46:09.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-03-24T23:47:21.000Z (over 1 year ago)
- Last Synced: 2024-02-18T17:35:03.436Z (4 months ago)
- Language: Python
- Homepage:
- Size: 10.2 MB
- Stars: 11
- Watchers: 3
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-stars - vascofazza/indoor-outdoor-image-classifier - A CNN-based image classifier capable of detecting if a scene is indoors or outdoors. (Python)
README
# indoor-outdoor-image-classifier
A CNN-based image classifier capable of detecting if a scene is indoors or outdoors.
This repository contains scripts for downloading videos corresponding to a particular or a few categories of [youtube-8m dataset](https://research.google.com/youtube8m/index.html).
## Dependencies
Python dependencies for the classification task
* Numpy
* Sklearn
* Tensorflow
* Keras
* tqdm
* PyYaml
* pytest
Dependencies for downloading youtube videos from ids
* [youtube-dl](https://github.com/rg3/youtube-dl#installation)
Dependencies for generation of frames from videos
* [ffmpeg](https://www.ffmpeg.org/download.html)
Dependences for image preprocessing and resizing
* [imagemagick](https://imagemagick.org/index.php)
## Install Python dependencies
```
$ pip3 install -r requirements.txt
```
## Test the pre-trained model
This CLI will run the pretrained model on a provided image.
```
bash classify.sh config/train_params.yml data/test/indoor_test.jpg
```
### Download and extract the train/test data
An example dataset composed by 600 YouTube videos, for a total of 60.000 video frames, is availale [here](http://insidecode.it/indoor-outdoor-data_64.zip).
The videos belong to different category, according to the [youtube-8m dataset](https://research.google.com/youtube8m/index.html).
The dataset contains instances from the following categories, labeled as follows:
```
Living_room indoor
Bedroom indoor
Dining_room indoor
Garden outdoor
Outdoor_recreation outdoor
Hiking outdoor
```
Prepare the test environment downloading the provided dataset excerpt:
```
$ cd indoor-outdoor-image-classifier
$ wget http://insidecode.it/indoor-outdoor-data_64.zip
$ unzip indoor-outdoor-data_64.zip
```
### Evaluate the model on the test split
This will test the performance of the model on the test split and run a simple unit test on two benchmark images.
```
$ bash evaluate.sh config/train_params.yml indoor-outdoor-data_64/frames
```
### Train model
It is possible to tune the model parameters defining a new configuration file (following the default one in config/train_params.yml) and train the new model using the following command:
```
$ bash train.sh
```