metadata
license: mit
library_name: pytorch
pipeline_tag: object-detection
tags:
- object-detection
- instance-segmentation
- keypoint-detection
- semantic-segmentation
- depth-estimation
- oriented-object-detection
- real-time
- linear-attention
GTR: Gated Token Recurrence for Efficient Dense Prediction
Checkpoints for GTR. Code, configs, evaluation and training instructions are in the GitHub repository.
git clone https://github.com/Intellindust-AI-Lab/GTR.git && cd GTR
hf download Phoenix8125/GTR --local-dir weights
python train.py -c configs/det/coco_finetune/gtr_s.yml --test-only -r weights/det/gtr_s_coco.pth
Each task checkpoint is a dict {'model': raw weights, 'ema': {'module': EMA weights, ...}, 'last_epoch': int}.
All numbers below are measured with the EMA weights.
| Folder | Task / benchmark | Metric | S | M | L | X |
|---|---|---|---|---|---|---|
det/ |
detection, COCO val2017 | AP | 53.6 | 57.3 | 58.9 | 59.4 |
seg/ |
instance segmentation, COCO val2017 | mask AP | 45.0 | 47.7 | 49.5 | 49.8 |
pose/ |
human pose, COCO val2017 | AP | 70.1 | 74.1 | 74.7 | 75.5 |
semseg/ |
semantic segmentation, Cityscapes val | mIoU | 81.5 | 83.0 | 83.2 | 83.6 |
depth/ |
monocular depth, NYU Depth V2 (Eigen test) | δ1 / AbsRel | 0.946 / 0.074 | 0.952 / 0.069 | 0.951 / 0.069 | 0.954 / 0.067 |
obb/ |
oriented detection, DOTA-v1.0 test | AP50 | 80.0 | – | – | 81.3 |
obj365/ |
Objects365 pre-trained detectors | – | ✓ | ✓ | ✓ | ✓ |