wondonghyeon / Protest Detection Violence Estimation
Licence: mit
Implementation of the model used in the paper Protest Activity Detection and Perceived Violence Estimation from Social Media Images (ACM Multimedia 2017)
Stars: ✭ 114
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Protest Activity Detection and Perceived Violence Estimation from Social Media Images
Implementation of the model used in the paper Protest Activity Detection and Perceived Violence Estimation from Social Media Images (ACM Multimedia 2017) [arxiv] by Donghyeon Won, Zachary C. Steinert-Threlkeld, Jungseock Joo.
Requirements
Pytorch
NumPy
pandas
scikit-learn
Usage
Training
python train.py --data_dir UCLA-protest/ --batch_size 32 --lr 0.002 --print_freq 100 --epochs 100 --cuda
Evaluation
python pred.py --img_dir path/to/some/image/directory/ --output_csvpath result.csv --model model_best.pth.tar --cuda
UCLA Protest Image Dataset
You will need to download our UCLA Protest Image Dataset to train the model. Please e-mail me if you want to download our dataset!
Dataset Statistics
# of images: 40,764
# of protest images: 11,659
Protest & Visual Attributes
| Fields | Protest | Sign | Photo | Fire | Police | Children | Group>20 | Group>100 | Flag | Night | Shouting |
|---|---|---|---|---|---|---|---|---|---|---|---|
| # of Images | 11,659 | 9,669 | 428 | 667 | 792 | 347 | 8,510 | 2,939 | 970 | 987 | 548 |
| Positive Rate | 0.286 | 0.829 | 0.037 | 0.057 | 0.068 | 0.030 | 0.730 | 0.252 | 0.083 | 0.085 | 0.047 |
Violence
| Mean | Median | STD |
|---|---|---|
| 0.365 | 0.352 | 0.144 |
Model
Architecture
We fine-tuned ImageNet pretrained ResNet50 to our data. You can download the model I trained from this Dropbox link.
Performance
| Protest | Sign | Photo |
|---|---|---|
 |
 |
 |
| Fire | Police | Children |
|---|---|---|
 |
 |
 |
| Group>20 | Group>100 | Flag |
|---|---|---|
 |
 |
 |
| Night | Shouting | Violence |
|---|---|---|
 |
 |
 |
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