Install pip (Python 3) dependencies:
pip install -r requirements.txt
Install Make, CMake, python3x-devel and g++ (gcc).
Download the pre-trained Places-CNN: http://places2.csail.mit.edu/models_places365/resnet50_places365.pth.tar . Then, move it to data folder.
Ajust the correct path of this project in your computer by setting the value of path in config.json.
Download the dataset from: http://web.mit.edu/torralba/www/indoor.html
Extract the image folder content into data/MITImages
Move TrainImages.txt to data
Move TestImages.txt to data
Download the dataset from: https://vision.cs.princeton.edu/projects/2010/SUN/
Extract the image folder content into data/SUN397
Extract the partitions folder content into data/SUNPartitions
Configure config.json file:
{
"path":"{path/to/msc}",
"arch_pam":"yolo3_darknet53_coco", (YOLO architecture)
"arch_scene":"resnet-50-Places", (Places-CNN)
"arch_obj":"resnet-18-ImageNet", (ImageNet-CNN)
"dataset":"{indoor||sun397}", (select dataset)
"sun397_it": 1, (SUN 397 partition id: 1-10)
"alpha":0.5, (alpha value)
"pam_threshold":0.5, (YOLO threshold)
"hidden_units": 1000, (number of hidden units in MLP)
"activation": "{logistic||relu||tanh}", (select MLP activation function)
"optimizer": "{adam||sgd}", (select MLP optimization function)
"kernel": "rbf", (select SVM kernel)
"it": 1, (number of evaluations)
"exp_a": 0, (bool for run Experiment A)
"exp_b": 1 (bool for run Experiment B)
}To run with intepreter: python3 main.py
To run with Cython: ./configure && ./main
Once trained, the train files (Memories) is saved into data.
The execution takes ~6Gb of RAM for MIT Indoor 37 and ~20Gb for SUN 397.
Running in background: rm -rf /tmp/msc.log && nohup python3 -u main.py >> /tmp/msc.log 2>&1 &.