HandyNet: A One-stop Solution to Detect, Segment, Localize & Analyze Driver Hands
Keras implementation for training and testing the models described in HandyNet: A One-stop Solution to Detect, Segment, Localize & Analyze Driver Hands. This repository was created by modifying the pre-existing Mask R-CNN implementation found here.
- Clone this repository.
tensorfloware installed. This code has been tested with Keras 2.1.4 and Tensorflow 1.4.1.
2. Split the dataset into separate training and validation folders as below:
└── DATASET_ROOT ├── train | ├── seq... | └── seq... | ... | └── seq... └── val ├── seq... └── seq... ... └── seq...
seq... folder above is a from a separate capture sequence. You can split the sequences into
val as per your requirement.
Make sure you replace
root in this script with the actual path to the dataset.
HandyNet can be trained using this script as follows:
python3 handynet.py train --dataset=/path/to/dataset/ --model=imagenet
An example using the HandyNet for inference and visualization can be seen in this script.
This script can be run as follows:
python3 demo_inference.py /path/to/inference/model /path/to/smooth/depth/mat/file
You can download our trained model using this link.