Tensorflow Keras API
Also the Story published @ https://blog.imaginea.com/text-information-extraction-2/
The CALTECH-101 (subset) dataset
The CALTECH-101 dataset is a dataset of 101 object categories with 40 to 800 images per class.
Most images have approximately 50 images per class.
The goal of the dataset is to train a model capable of predicting the target class.
Prior to the resurgence of neural networks and deep learning, the state-of-the-art accuracy on was only ~65%.
However, by using Convolutional Neural Networks, it’s been possible to achieve 90%+ accuracy (as He et al. demonstrated in their 2014 paper, Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition).
Today we are going to implement a simple yet effective CNN that is capable of achieving 96%+ accuracy, on a 4-class subset of the dataset:
Faces: 436 images Leopards: 201 images Motorbikes: 799 images Airplanes: 801 images
The reason we are using a subset of the dataset is so you can easily follow along with this example and train the network from scratch, even if you do not have a GPU.
Again, the purpose of this tutorial is not meant to deliver state-of-the-art results on CALTECH-101 — it’s instead meant to teach you the fundamentals of how to use Keras’ Conv2D class to implement and train a custom Convolutional Neural Network.
wget http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz tar -zxvf 101_ObjectCategories.tar.gz
How to run ?
python stridednet_train.py --dataset=101_ObjectCategories --plot=stridnet_results.png --epochs=50 # following command will download the Resnet weights from web! python resnet_train.py --dataset=101_ObjectCategories --plot=resnet_results.png --epochs=50
from tensorflow.python.client import device_lib print(device_lib.list_local_devices())
Use above snippet to see whether your current Tensorflow installation has support for GPU
- Resnet Materials