Transfer Learning
We will use the crawler to crop faces and create different image files into a dataset folder.
We have made use of the inception-v3 model trained by google tensorflow researchers.
The model is a Convolutional Neural Network that helps for image recognition.
Our goal will not be to build from scratch but use the model to retrain for detecting shapes.
clone the pre-trained ImageNet data set
git clone https://github.com/tensorflow/models.git
cd models/tutorials/image/imagenet
python classify_image.py
classify_image.py downloads the trained model from google's backend.
If the model runs correctly, the script will produce the following output:
giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca (score = 0.88493)
indri, indris, Indri indri, Indri brevicaudatus (score = 0.00878)
lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens (score = 0.00317)
custard apple (score = 0.00149)
earthstar (score = 0.00127)
Clone the flowing url by using this terminal command:
git clone https://github.com/koflerm/tensorflow-image-classifier.git
Use Fatkun Batch downloader to get datasets. You are required to use this model for retraining.
Make sure to look out for the files in this model.
Create a directory called training_dataset to add all images as necessary.
/
--- /training_dataset
| |
| --- /Circle
| | circle1.jpg
| | circle_small_red.png
| | ...
| |
| --- /Square
| square.jpg
| square3.jpg
| ...
Use the train.sh from the cloned directory to start the retraining process.
Remember that this will allow for 90 % accuracy in prediction which is a good accuracy rate.
The retrained labels, -graphs and the training summary will be saved in a folder named tf_files.
After re-training the model, it’s now time to test the model. Test it by typing:
python classify.py <FileName>.jpg
You should get a result like this:
