-
Big table
GPU: 3080 x2
train set: train + val; eval set: test
cut size: (640, 640)
| detector | batch | lr | script | ||
|---|---|---|---|---|---|
| Faster RCNN | 4x2 | 0.04 |
73.44 |
60.78 |
exp/sh/Baseline_TinyPersonV2.sh:1.3 |
| RetinaNet | 4x2 | 0.04 |
72.75 |
59.45 |
exp/sh/Baseline_TinyPersonV2.sh:2.1 |
| Adap RetinaNet | 3x2 | 0.04 | 74.19 | 63.78 | exp/sh/Baseline_TinyPersonV2.sh:2.2 |
| FCOS | 4x2 | 0.04 |
65.08 |
54,37 |
exp/sh/Baseline_TinyPersonV2.sh:3.1 |
| Adap FCOS | 4x2 | 0.04 |
71.59 |
60.98 |
exp/sh/Baseline_TinyPersonV2.sh:3.2 |
| RepPoint | 4x2 | 0.04 |
73.47 |
60.79 |
exp/sh/Baseline_TinyPersonV2.sh:4.1 |
| Adap RepPoint | 4x2 | 0.04 |
75.27 |
64.56 |
exp/sh/Baseline_TinyPersonV2.sh:4.2 |
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目录结构
数据集位置：~/dataset/tiny_set_v2
数据集来源：17suo(基础) + tiny_set_v1
规则：模态/视频号/图片
划分：10: 1 : 10（视频号级别）
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TinyPerson_v2（RGB）
Tab. 1. 目标size ratio统计.
| dataset_v3.0.1 | absolute size | relative size | aspect ratio |
|---|---|---|---|
| TinyPerson_v2 (rgb) | 23.197±9.815 | 0.016±0.007 | 0.731±0.422 |
| TinyPerson_v2 (infra) | 12.614±4.817 | 0.017±0.006 | 0.827±0.422 |
| dataset_v2.0.1 | absolute size | relative size | aspect ratio |
|---|---|---|---|
| TinyPerson_v2 (rgb) | 22.692±12.117 | 0.016±0.008 | 0.746±0.509 |
| TinyPerson_v2 (rgb without ignore) | 22.619±10.849 | 0.016±0.007 | 0.723±0.424 |
| TinyPerson_v2 (xray) | 13.551±5.078 | 0.018±0.007 | 0.874±0.495 |
| TinyPerson_v2 (xray without ignore) | 13.615±5.099 | 0.018±0.007 | 0.862±0.481 |
TinyPerson
Tab. 2. 数据集图片与标注数量.
| TinyPerson_v3.0.1 | Train set | Valid set | Test set | sum |
|---|---|---|---|---|
| #image (rgb) | 4614 | 375 | 3559 | 8548 |
| #image (infra) | 4614 | 375 | 3559 | 8548 |
| #annotation (rgb) | 170383 | 16408 | 275486 | 462277 |
| #annotation (infra) | 172190 | 16447 | 228775 | 417412 |
| TinyPerson_v2.0.1 | Train set | Valid set | Test set | sum |
|---|---|---|---|---|
| #image (rgb) | 5711 | 568 | 5753 (5754?) | 12032 |
| #image (xray) | 8938 | 906 | 8950 | 18794 |
| #annotation (rgb) | 262063 | 42399 | 315165 | 619627 |
| #annotation (xray) | 390356 | 72525 | 291968 | 754849 |
TinyPerson
Fig. 分布.
TinyPerson
[Issue]
- issue: 无法算均值方差，存在annitation的bbox为nan
- issue: 复杂bug（7.9）>> [继续train]
- issue: >> [继续train]
--resume-from+{dir/}+epoch_9.pth
--resume-from ../TOV_mmdetection_cache/work_dir/${CONFIG}/trainval${WH[0]}x${WH[1]}_lr${LR}_clipg10_1x_b${B}${GPU}g/epoch_9.pth
- issue: >> [re-test]
evaluation.do_final_eval=True \
--resume-from+{dir/}+epoch_9.pth
evaluation.do_final_eval=True \
--resume-from ../TOV_mmdetection_cache/work_dir/${CONFIG}/trainval${WH[0]}x${WH[1]}_lr${LR}_1x_b${B}${GPU}g/epoch_12.pth
issue: >> [删除无用Epoch]
$python exp/tools/clear_tmp_pth.py ../TOV_mmdetection_cache/work_dir/-
issue: 发散 >> [clip_g]
梯度过大，loss发散，但仍需训练。可改进网路，增加梯度裁剪。
issue 1: loss不收敛
issue 2: loss NaN，程序退出
FCOS
"""
"configs2/TinyPersonV2/base/fcos_r50_caffe_fpn_gn-head_1x_TinyPersonV2_640.py"
"""
optimizer_config = dict( # wd: 改小grad_clip['max_norm']
_delete_=True, grad_clip=dict(max_norm=15, norm_type=2))
# optimizer_config = dict(
# _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
warmup_iters=500,) # wd: 观察500 iters
RetinaNet
"""
"configs2/TinyPersonV2/base/retinanet_r50_fpn_1x_TinyPersonV2_640.py"
"""
optimizer_config = dict( # wd: 改小grad_clip['max_norm']
_delete_=True, grad_clip=dict(max_norm=15, norm_type=2))
# optimizer_config = dict(
# _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
warmup_iters=500,) # wd: 观察500 iters
# xb exp
python exp/tools/semi_coarse_point_manager.py
python exp/tools/coarse_point_manager.py
