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Note: About create_template.py at line:138 'get_axis_aligned_rect' not exist， please comment get_axis_aigned_rect function
if reset: #reset=1 (default)
#gt_rect = get_axis_aligned_rect(ground_truth[frame])#x，y，w，h
rect=ground_truth[frame] #topx,topy,w,h
gt_rect=np.array([rect[0]-1,rect[1]-1,rect[2],rect[3]])#0-based x,y,w,h
iou = overlap_ratio(gt_rect, res)
if iou<=0:# you can choose iou<0.2, iou<0.3, iou<0.4
break
├── bin
├── dasiamrpn
├── data
├── datasets
├── models
├── results
├── toolkit
└── updatenet
How to produce templates ? You can choose iou<0.2, iou<0.3, iou<0.4
python ./updatenet/create_template.py
SiamRPNBIG.model password: b3b6
BaiduYun password: 1iii
# step=1,2,3
python ./updatenet/train_upd.py
python ./bin/my_test.py
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My result VOT2018 EAO=0.403, original result VOT2018 EAO=0.393
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How to train UpdateNet on VOT2018 ?
-
Stage 1.1
Generate templates by linear update, train from scratch
you can try learning rate Lr5-6 , Lr6-7, Lr7-8
checpoint1 EAO xxx
...
checkpoint50 EAO xxx
- Stage 1.2
Load pretrained model(the best checkpoint from stage 1.1), train from checkpoint
you can try learning rate Lr7-8 , Lr8-9, Lr9-10
checpoint1 EAO xxx
...
checkpoint50 EAO xxx
- Stage 2.1
Generate templates by UpdateNet model (choose best checkpoint from stage 1.2) , train from scratch
you can try learning rate Lr5-6 , Lr6-7, Lr7-8
checpoint1 EAO xxx
...
checkpoint50 EAO xxx
- Stage 2.2
Load pretrained model(choose best checkpoint from stage 2.1),train from checkpoint
you can try learning rate Lr7-8 , Lr8-9, Lr9-10
checpoint1 EAO xxx
...
checkpoint50 EAO xxx
- Stage 3.1
Generate templates by UpdateNet model (choose best checkpoint from stage 2.2) , train from scratch
you can try learning rate Lr5-6 , Lr6-7, Lr7-8
checpoint1 EAO xxx
...
checkpoint50 EAO xxx
- Stage 3.2
Load pretrained model(choose best checkpoint from stage 3.1), train from checkpoint
you can try learning rate Lr7-8 , Lr8-9, Lr9-10
checpoint1 EAO xxx
...
checkpoint50 EAO xxx
- My results
step1.1 lr6-7
--------------------------------------------------------------
| Tracker Name | Accuracy | Robustness | Lost Number | EAO |
--------------------------------------------------------------
| checkpoint30 | 0.582 | 0.286 | 61.0 | 0.367 |
--------------------------------------------------------------
step1.2 lr9-10（load checkpoint30 model from step1.1）
--------------------------------------------------------------
| Tracker Name | Accuracy | Robustness | Lost Number | EAO |
--------------------------------------------------------------
| checkpoint30 | 0.585 | 0.272 | 58.0 | 0.373|
--------------------------------------------------------------
step2.1 lr5-6 （load checkpoint30 model from step1.2）
--------------------------------------------------------------
| Tracker Name | Accuracy | Robustness | Lost Number | EAO |
--------------------------------------------------------------
| checkpoint36 | 0.586 | 0.295 | 63.0 | 0.366 |
--------------------------------------------------------------
step2.2 lr8-9 （load checkpoint36 model from step2.1）
--------------------------------------------------------------
| Tracker Name | Accuracy | Robustness | Lost Number | EAO |
--------------------------------------------------------------
| checkpoint29 | 0.584 | 0.258 | 55.0 | 0.386 |
--------------------------------------------------------------
step3.1 lr6-7 (load checkpoint29 model from step2.2)
--------------------------------------------------------------
| Tracker Name | Accuracy | Robustness | Lost Number | EAO |
--------------------------------------------------------------
| checkpoint22 | 0.583 | 0.253 | 54.0 | 0.390 |
--------------------------------------------------------------
step3.2 lr8-9 (load checkpoint22 model from step3.1)
--------------------------------------------------------------
| Tracker Name | Accuracy | Robustness | Lost Number | EAO |
--------------------------------------------------------------
| checkpoint13 | 0.583 | 0.225 | 48.0 | 0.403 |
--------------------------------------------------------------
Honglin Chu
Zeyu Xi
[1] Zhang L, Gonzalez-Garcia A, Weijer J, et al. Learning the Model Update for Siamese Trackers. Proceedings of the IEEE International Conference on Computer Vision. 2019: 4010-4019.