https://github.com/aitikgupta/violence_detection
This is a Computer Vision project which aims to detect violence in realtime.
https://github.com/aitikgupta/violence_detection
Last synced: about 13 hours ago
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This is a Computer Vision project which aims to detect violence in realtime.
- Host: GitHub
- URL: https://github.com/aitikgupta/violence_detection
- Owner: aitikgupta
- Created: 2020-03-16T17:12:44.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-05-25T03:44:11.000Z (about 4 years ago)
- Last Synced: 2025-04-05T05:24:15.122Z (3 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 9.74 MB
- Stars: 10
- Watchers: 2
- Forks: 2
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
# Violence Detection using VGG16 network
This is a Computer Vision project which aims to classify images containing violence.
Major Tech Stack:
->TensorFlow 2.1.0
->OpenCV
# How to use?
Note: To maintain the ennvironments, I highly recommend using [conda](https://anaconda.org/).
```
git clone https://github.com/aitikgupta/violence_detection.git
cd violence_detection
conda env create -f environment.yml
conda activate {environment name, for eg. conda activate cv}
jupyter notebook Training_Model.ipynb {or you can use my trained model, link is below}
jupyter notebook Violence_Detection.ipynb
```
## Model link:
[https://drive.google.com/file/d/1ib6zg_8kWmRQhkVFRszi3Y6r1fB5jR1a/view?usp=sharing](https://drive.google.com/file/d/1ib6zg_8kWmRQhkVFRszi3Y6r1fB5jR1a/view?usp=sharing)
Note: If you choose to download my trained model, place the model.h5 file in the root directory.
## About the model:
The model is built using TensorFlow 2.1.0 with 1.5 hours training on GeForce GTX-1650 GPU. Last 4 layers of the VGG16 pretrained network were fine tuned, along with fully-connected layers.
#### Application of VGG16 network:
Given image → find object name in the image
It can detect any one of 1000 images
It takes input image of size 224 * 224 * 3 (RGB image)
Built using:
Convolutions layers (used only 3*3 size )
Max pooling layers (used only 2*2 size)
Fully connected layers at end
Total 16 layers
#### Model size:
528MB
#### Pre trained model(Tensorflow):
[VGG16-weights](https://www.cs.toronto.edu/~frossard/vgg16/vgg16_weights.npz)
#### Built by:
Visual Geometry Group [VGG Homepage](http://www.robots.ox.ac.uk/~vgg/)
#### Description of layers:
Convolution using 64 filters
Convolution using 64 filters + Max pooling
Convolution using 128 filters
Convolution using 128 filters + Max pooling
Convolution using 256 filters
Convolution using 256 filters
Convolution using 256 filters + Max pooling
Convolution using 512 filters
Convolution using 512 filters
Convolution using 512 filters + Max pooling
Convolution using 512 filters
Convolution using 512 filters
Convolution using 512 filters + Max pooling
Fully connected with 4096 nodes
Fully connected with 4096 nodes
Output layer with Softmax activation with 1000 nodes
#### Full view at image level:
