This repo contains PatchNet model description in PyTorch and its training/evaluation with SiameseFC. It heavily relies on siamfc-pytorch.
From left to right: Skip frame - interleave siamfc with patchnet for speedup. Boosted - combine siamfc's prediction scores and patchnet's local similarity scores for more robust tracking. SiamFC - the base line.
All three methods are tested with PyTorch-1.4 on Nvidia Jetson Nano.
- Python 3
- PyTorch >= 1.2
- GOT-10k dataset (for training only)
Both trained models of SiamFC and PatchNet have already been stored under ./pretrained/
Demo
python3 scripts/demo_skipframe.py
Interleave SiamFC and PatchNet on OTB2015 dataset
python3 scripts/test_<name>.py
Interleave RFCN ResNet-101 and PatchNet
python3 scripts/vod_track.py data/VID/ILSVRC2015_val_00142000 data/VID/rfcn_res101_val_00142000.txt --visualize --conf 0.5
Train PatchNet model
python3 scripts/train_patchnet.py --dataset <got_10k_root> --save pretrained/<my_fancy_model>