- pytorch 1.4
- loguru
- scipy
- sklearn
CIMON_Pytorch
optional arguments:
-h, --help show this help message and exit
-d DATASET, --dataset DATASET
Dataset name.
-r ROOT, --root ROOT Path of dataset
-c CODE_LENGTH, --code-length CODE_LENGTH
Binary hash code length.(default: 12)
-T MAX_ITER, --max-iter MAX_ITER
Number of iterations.(default: 50)
-l LR, --lr LR Learning rate.(default: 1e-3)
-q NUM_QUERY, --num-query NUM_QUERY
Number of query data points.(default: 10000)
-t NUM_TRAIN, --num-train NUM_TRAIN
Number of training data points.(default: 5000)
-w NUM_WORKERS, --num-workers NUM_WORKERS
Number of loading data threads.(default: 16)
-b BATCH_SIZE, --batch-size BATCH_SIZE
Batch size.(default: 24)
-a ARCH, --arch ARCH CNN architecture.(default: vgg16)
-k TOPK, --topk TOPK Calculate map of top k.(default: -1)
-v, --verbose Print log.
--train Training mode.
--evaluate Evaluate mode.
-g GPU, --gpu GPU Using gpu.(default: False)
-e EVALUATE_INTERVAL, --evaluate-interval EVALUATE_INTERVAL
Interval of evaluation.(default: 500)
--num_class Number of cluster for spectral clustering.(default:70)
--eta Hyper-parameter for weight balance. (default:0.3)
--temperature Hyper-parameter in SimCLR.(default:0.5)
cifar10: 10000 query images, 5000 training images. Return MAP@ALL(50000) The same setting with DistillHash (19' CVPR )
How to train: python run.py --train
How to evalutate: python run.py --evaluate
Our model will be restored in the checkpoints.