resnet-finetune-demo
A simple experiment with finetuning Resnet-152 in Keras for classifying indoor places images from MIT Indoor-67 dataset. This method achieves 73% accuracy on the test set.
Detailed description of this experiment can be found here: http://www.cta.ai/en/publications/02
The code is built on Keras 2.0 (ver. 2.0.4) on TensorFlow backend (ver. 1.2.0-rc2) using Python 3.6.
To run this experiment you should download the data package which is available here. Unpack it in your home directory for compliance with the code. The data package consists of several elements:
- Indoor-67 images, split into train, val and test subsets
- ImageNet-trained Resnet-152 weights, acquired from here
- Text file mapping ImageNet ids to class names, acquired from here
- Cached features from Resnet-152 model and labels for images in train, val and test subsets
Method summary
This method simply freezes the entire Resnet-152 model and retrains the last fully-connected classification layer from scratch. It achieves 73% accuracy, which is only 6% less than state-of-the-art as for 2016 (see the chart below).
References
[1] Quattoni, Ariadna, and Antonio Torralba. "Recognizing indoor scenes." Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009.
[2] Zhou, Bolei, et al. "Learning deep features for scene recognition using places database." Advances in neural information processing systems. 2014.
[3] Khan, Salman H., et al. "A discriminative representation of convolutional features for indoor scene recognition." IEEE Transactions on Image Processing 25.7 (2016): 3372-3383.
[4] Herranz, Luis, Shuqiang Jiang, and Xiangyang Li. "Scene recognition with CNNs: objects, scales and dataset bias." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
Modules outline:
- resnet-demo.py - demo app testing ImageNet trained Resnet-152 on images from clipboard
- build_features.py - creates a cache of Resnet features over the dataset
- train.py - builds fully-connected classification layer and trains it over the cached features
- test.py - test the trained classifier
- test-demo.py - demo app classifying test images one by one and showing outputs
- helper.py - some helping functions
- resnet/resnet152.py - Resnet-152 model acquired from here