In addition to the requirements for SS-PRL pre-training, the following packages are used:
- scikit-learn
- Fine-tuning on the COCO dataset:
DATA="./data/coco" # path to the coco dataset WEIGHT="ss-prl.pth.tar" # pre-trained weight bash scropts/linear_coco.sh ${DATA} ${WEIGHT}
- Fine-tuning on the VOC dataset:
DATA="./data/voc" # path to the voc dataset WEIGHT="ss-prl.pth.tar" # pre-trained weight bash scropts/linear_voc.sh ${DATA} ${WEIGHT}
- Semi-Supervised training on the COCO dataset:
DATA="./data/coco" # path to the coco dataset WEIGHT="ss-prl.pth.tar" # pre-trained weight PERC="1" # fine-tune with 1 or 10 percent of labels bash scropts/semisup_coco.sh ${DATA} ${WEIGHT} ${PERC}