If an autonomous vehicle can drive itself, I strongly believe we can enable the vision impaired and blinds to see and navigate the world with ease.
Enabling the vision impaired and blinds to see and navigate the world with ease is the main goal of this AI for Mankind's Seeing the World open source project. We want to leverage the power of open source community to build low cost open source image recognition and object detection models to empower the vision impaired and blinds to see and navigate the world. All the models built will be freely available to all across the entire world.
According to WHO, there are 253 million people live with vision impairment. 217 million have moderate to severe vision impairment and 36 million are blind. 81% of people who are blind or have moderate or severe vision impairment are aged 50 years and above.
- Farmer Market
As a baby step, we will start with building model to recognize fruit and vegetable in Farmer Market and expand to other settings. We will contribute back all the models back to Microsoft Seeing AI Microsoft's Seeing AI app
We encourage our members and public to take pictures of different fruit and vegetable whenever they go to Farmer Market and commit back to the repo. We will build model using these pictures.
- Hotel
- Outdoor Space Detection
- Indoor Space Detection
Building Fruit/Vegetable Model via Transfer Learning
Install Docker:
https://docs.docker.com/v17.12/docker-for-mac/install/#install-and-run-docker-for-mac
We use the scripts provided by the following excellent Google’s TensorFlow For Poets codelab. https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0
Clone the repo:
https://github.com/aiformankind/seeing-the-world.git
Go to the repo directory:
cd seeing-the-world
Start Tensorflow docker:
docker run -it --rm -p 8888:8888 -p 6006:6006 -v $PWD:/work -w /work tensorflow/tensorflow bash
Set environment variables:
IMAGE_SIZE=224
ARCHITECTURE="mobilenet_0.50_${IMAGE_SIZE}"
Install Augmentor: https://github.com/mdbloice/Augmentor
apt update
apt install wget
pip install Augmentor
Augment data:
python augment/augment_images.py
Retrain model:
python -m train.retrain --bottleneck_dir=train_output/bottlenecks --how_many_training_steps=500 --model_dir=train_output/models/ --summaries_dir=train_output/training_summaries/"${ARCHITECTURE}" --output_graph=train_output/retrained_graph.pb --output_labels=train_output/retrained_labels.txt --architecture="${ARCHITECTURE}" --image_dir=augment-data
Predict label:
python -m train.label_image --graph=train_output/retrained_graph.pb --image=validation/tomato/tomato-val-1.jpg
Jigar Doshi from CrowdAI