Agriculture Vision 2020
Results Summary
NOTE all our single model’s scores are computed with just single-scale (512x512) and single feed-forward inference without TTA. TTA denotes test time augmentation (e.g. flip and mirror). Ensemble_TTA (checkpoint1,2) denotes two core.net.(checkpoint1, and checkpoint2) ensemble with TTA, and (checkpoint1, 2, 3) denotes three core.net.ensemble.
Models |
mIoU (%) |
Background |
Cloud shadow |
Double plant |
Planter skip |
Standing water |
Waterway |
Weed cluster |
|---|---|---|---|---|---|---|---|---|
MSCG-Net-50 (ckpt1) |
54.7 |
78.0 |
50.7 |
46.6 |
34.3 |
68.8 |
51.3 |
53.0 |
*MSCG-Net-101 (ckpt2)* |
*55.0* |
*79.8* |
*44.8* |
*55.0* |
*30.5* |
*65.4* |
*59.2* |
*50.6* |
MSCG-Net-101_k31 (ckpt3) |
54.1 |
79.6 |
46.2 |
54.6 |
9.1 |
74.3 |
62.4 |
52.1 |
Ensemble_TTA (ckpt1,2) |
59.9 |
80.1 |
50.3 |
57.6 |
52.0 |
69.6 |
56.0 |
53.8 |
Ensemble_TTA (ckpt1,2,3) |
60.8 |
80.5 |
51.0 |
58.6 |
49.8 |
72.0 |
59.8 |
53.8 |
Ensemble_TTA (new_5model) |
62.2 |
80.6 |
48.7 |
62.4 |
58.7 |
71.3 |
60.1 |
53.4 |
Model Size
NOTE all backbones used pretrained weights on ImageNet that can be imported and downloaded from the link. And MSCG-Net-101_k31 has exactly the same architecture wit MSCG-Net-101, while it is trained with extra 1/3 validation set (4,431) instead of just using the official training images (12,901).
Models |
Backbones |
Parameters |
GFLOPs |
Inference time (CPU/GPU ) |
|---|---|---|---|---|
MSCG-Net-50 |
Se_ResNext50_32x4d |
9.59 |
18.21 |
522 / 26 ms |
MSCG-Net-101 |
Se_ResNext101_32x4d |
30.99 |
37.86 |
752 / 45 ms |
MSCG-Net-101_k31 |
Se_ResNext101_32x4d |
30.99 |
37.86 |
752 / 45 ms |