/Learning-Loss-for-Active-Learning

An unofficial PyTorch implementation of "Learning Loss for Active Learning"

Primary LanguagePython

Learning Loss for Active Learning

An unofficial PyTorch implementation of the paper Learning Loss for Active Learning.

Requirements

torch
torchvision
matplotlib
tqdm
cv2
imageio
imutils

Usage

python main.py --task {clf OR detection OR hpe}
  • task
    • clf: image classification
    • detection: object detection
    • hpe: human pose estimation

image classification

  • dataset: CIFAR10 & CIFAR100
  • model: ResNet
  • metric: Accuracy
python main.py --task clf --dataset CIFAR10 --subset 10000 --num_epoch 200 --batch_size 128 --lr 0.1 --epoch_loss 120 --weights 1.0 --milestone 160

object detection

  • dataset: PASCAL VOC2007 & 2012
  • model: SSD (Single Shot Multibox Detector)
  • metric: mAP
python main.py --task detection --dataset VOC0712 --num_epoch 300 --batch_size 32 --lr 0.001 --epoch_loss 240 --weights 1.0 --milestone 240

human pose estimation

  • dataset: MPII
  • model: SHN (Stacked Hourglass Networks)
  • metric: PCKh@0.5
python main.py --task hpe --dataset mpii --subset 5000 --num_epoch 125 --batch_size 6 --wdecay 0 --lr 0.00025 --epoch_loss 75 --weights 0.0001 --milestone 100

References