CPSC8810 Deep Learning Term Project
Qingbo Lai
Haotian Deng
This project construct and implement Convolutional Neural Networks (CNNs) to classify the bully picture. All codes were implementated and tested on Palmetto www.palmetto.clemson.edu
Deep_learning_Midterm_Report.pdf is our project report
Python3.6; TensorFlow framework 1.12
We have two different networks structure. one is simple three layers CNN model with two fully connected layers which written by ourselves, the another model is based VGG16 with some changes by ourselves.
We used ten categories images to train model. Nine categories of bully images which are laughing, pullinghair, quarrel, slapping, punching, stabbing, gossiping, strangle and isolation. The rest of images are nonbullying category.
Default location of training data : data_bully/training_data
Default location of testing data: data_bully/testing_data
If you want to train model please make directory "data_bully"
python bully_train.py --train_path path-to-training-dataset
python predict.py --img_file path-to-img/xxx.jpg
python test.py --test_path path-to-testing-dataset
Command line: python bully_train.py --train_path "path-to-training-dataset"
Please download pre-trained model from Google drive which provided by TA, because the files are too large to upload to github. After downlaod pre-trained model, please unconpress and put it to "trained_model" directory.
python predict.py --img_file "path-to-img/xxx.jpg"
Testing 10 groups of classified images by the tagged file directory like laughing, pullinghair, quarrel, slapping, punching, stabbing, gossiping, strangle, isolation and nonbullying. The output will be the accuracy of testing files.
Command line: python test.py --test_path "path-to-testing-datase"
[1]Stanford 40 actions. In Stanford 40 Actions. http://vision.stanford.edu/Datasets/40actions.html
[2] Girdhar, R. and Ramanan, D. (2017). Attentional pooling for action recog- nition. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 34{45. Curran Associates, Inc.
[3] Gkioxari, G., Girshick, R., and Malik, J. (2015). Contextual action recogni- tion with r*cnn. In The IEEE International Conference on Computer Vision (ICCV).
[4] palmetto. In https://www.palmetto.clemson.edu/palmetto/.
[5] Qassim, H., Verma, A., and Feinzimer, D. (2018). Compressed residual-vgg16 cnn model for big data places image recognition. In 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), pages 169{175.
[6] Sharif Razavian, A., Azizpour, H., Sullivan, J., and Carlsson, S. (2014). Cnn features o�-the-shelf: An astounding baseline for recognition. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Work- shops.