# Photo Aesthetics Ranking Network with Attributes and Content Adaptation
### Code, demo and model for our project of deep image aesthetics analysis
We are releasing our codes/demo/dataset used in our project of image aesthetics analysis. This project is jointed done in Adobe Research and UCI. A patent is filed.
![alt text](http://www.ics.uci.edu/~skong2/img/aestheticsDemoFigure.png "display")
The AADB dataset is large (datasetImages.zip, 2.0GB), so we attach it to [google drive](https://drive.google.com/open?id=0BxeylfSgpk1MOVduWGxyVlJFUHM) from where a smaller version of AADB can also be downloaded with resized images (256x256 pixel resolution, datasetImages_warp256.zip, 130MB). Please note that all the images are downloaded from flickr with Creative Commons license, so the dataset is for research purpose only.
Technically, the rank loss is implemented in caffe. The modified caffe (named "caffeCustom.zip") can be downloaded in the google drive. An example prototxt to train the model can also be found there, named "mergedNetRank.prototxt".
Some models are also released along with a demo interface. Running the demo can give you a clear way on how to load/interpret the model. The model can be downloaded from the google drive as well. As well, the models are for research purpose only as patent has been filed by Adobe.
Besides, a model trained on AVA dataset is released and stored in google drive, named "AVA_modelRelease.zip". There are matlab code pieces to test the model. Note that the matcaffe path might need to change, but it's trivial.
For further questions, please refer to our ECCV2016 paper or send me email through (skong2 AT uci DOT edu)
If you find our model/method/dataset useful, please cite our work:
@inproceedings{kong2016aesthetics,
title={Photo Aesthetics Ranking Network with Attributes and Content Adaptation},
author={Kong, Shu and Shen, Xiaohui and Lin, Zhe and Mech, Radomir and Fowlkes, Charless},
booktitle={ECCV},
year={2016}
}
Latest edit
Mar. 21, 2017
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deepImageAestheticsAnalysis-master.zip (37个子文件)
deepImageAestheticsAnalysis-master
README.md 2KB
demoRatingImages
tmp2.jpg 208KB
matlab
CMakeLists.txt 3KB
+caffe
Layer.m 841B
Blob.m 3KB
Net.m 5KB
set_mode_cpu.m 97B
reset_all.m 172B
set_device.m 250B
run_tests.m 393B
io.m 2KB
private
caffe_.mexa64 7.46MB
is_valid_handle.m 935B
CHECK_FILE_EXIST.m 118B
caffe_.cpp 20KB
CHECK.m 71B
Solver.m 2KB
imagenet
ilsvrc_2012_mean.mat 593KB
+test
test_io.m 648B
test_solver.m 1KB
test_net.m 4KB
get_solver.m 298B
get_net.m 1KB
set_mode_gpu.m 97B
hdf5creation
.gitignore 14B
store2hdf5.m 3KB
demo.m 2KB
demo
classification_demo.m 5KB
main_demo4_suggestion.m 21KB
mean_AADB_regression_warp256.binaryproto 768KB
tmp1.jpg 438KB
readme.txt 587B
AADBstatistics.m 2KB
AADBinfo.mat 175KB
mean_AADB_regression_warp256.binaryproto 768KB
mean_AADB_warp256.binaryproto 768KB
python
caffe_prediction.py 3KB
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