Skip to content

Commit

Permalink
update readme and add download links
Browse files Browse the repository at this point in the history
  • Loading branch information
@BIGWangYuDong
BIGWangYuDong committed Oct 28, 2023
1 parent 855da6d commit 20d613f
Show file tree
Hide file tree
Showing 18 changed files with 189 additions and 556 deletions.
56 changes: 0 additions & 56 deletions configs/detection/detector_with_enhance_head/faster-rcnn_r50_fpn_basic-enhance_1x_coco.py
View file

This file was deleted.

60 changes: 11 additions & 49 deletions configs/detection/duo_dataset/README.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -12,57 +12,19 @@ Underwater object detection for robot picking has attracted a lot of interest. H
<img src="https://user-images.githubusercontent.com/48282753/233964524-73b49b46-03c2-48ba-9786-697c9d2c081a.png" height="400"/>
</div>

**Note:** DUO contains URPC2020, the categories of both datasets are same. DUO introduced URPC2020 and other underwater object detection datasets in the paper.

**TODO:**

- [ ] Support DUO Dataset and release models.
- [ ] Unify Dataset name in `LQIT`

## Results and Models

### URPC2020

| Architecture | Backbone | Style | Lr schd | box AP | Config | Download |
| :-----------: | :---------: | :-----: | :-----: | :----: | :----------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| Faster R-CNN | R-50 | pytorch | 1x | 43.5 | [config](./faster-rcnn_r50_fpn_1x_urpc-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/faster-rcnn_r50_fpn_1x_urpc-coco_20220226_105840-09ef8403.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/faster-rcnn_r50_fpn_1x_urpc-coco_20220226_105840.log.json) |
| Faster R-CNN | R-101 | pytorch | 1x | 44.8 | [config](./faster-rcnn_r101_fpn_1x_urpc-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/faster-rcnn_r101_fpn_1x_urpc-coco_20220227_182523-de4a666c.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/faster-rcnn_r101_fpn_1x_urpc-coco_20220227_182523.log.json) |
| Faster R-CNN | X-101-32x4d | pytorch | 1x | 44.6 | [config](./faster-rcnn_x101-32x4d_fpn_1x_urpc-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/faster-rcnn_x101-32x4d_fpn_1x_urpc-coco_20230511_190905-7074a9f7.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/faster-rcnn_x101-32x4d_fpn_1x_urpc-coco_20230511_190905.log.json) |
| Faster R-CNN | X-101-64x4d | pytorch | 1x | 45.3 | [config](./faster-rcnn_x101-64x4d_fpn_1x_urpc-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/faster-rcnn_x101-64x4d_fpn_1x_urpc-coco_20220405_193758-5d2a37e4.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/faster-rcnn_x101-64x4d_fpn_1x_urpc-coco_20220405_193758.log.json) |
| Cascade R-CNN | R-50 | pytorch | 1x | 44.3 | [config](./cascade-rcnn_r50_fpn_1x_urpc-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/cascade-rcnn_r50_fpn_1x_urpc-coco_20220405_160342-044e6858.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/cascade-rcnn_r50_fpn_1x_urpc-coco_20220405_160342.log.json) |
| RetinaNet | R-50 | pytorch | 1x | 40.7 | [config](./retinanet_r50_fpn_1x_urpc-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/retinanet_r50_fpn_1x_urpc-coco_20220405_214951-a39f054e.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/retinanet_r50_fpn_1x_urpc-coco_20220405_214951.log.json) |
| FCOS | R-50 | caffe | 1x | 41.4 | [config](./fcos_r50-caffe_fpn_gn-head_1x_urpc-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/fcos_r50-caffe_fpn_gn-head_1x_urpc-coco_20220227_204555-305ab6aa.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/fcos_r50-caffe_fpn_gn-head_1x_urpc-coco_20220227_204555.log.json) |
| ATSS | R-50 | pytorch | 1x | 44.8 | [config](./atss_r50_fpn_1x_urpc-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/atss_r50_fpn_1x_urpc-coco_20220405_160345-cf776917.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/atss_r50_fpn_1x_urpc-coco_20220405_160345.log.json) |
| TOOD | R-50 | pytorch | 1x | 45.4 | [config](./tood_r50_fpn_1x_urpc-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/tood_r50_fpn_1x_urpc-coco_20220405_164450-1fbf815b.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/tood_r50_fpn_1x_urpc-coco_20220405_164450.log.json) |
| SSD300 | VGG16 | - | 120e | 35.1 | [config](./ssd300_120e_urpc-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/ssd300_120e_urpc-coco_20230426_122625-b6f0b01e.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/ssd512_120e_urpc-coco_20220405_185511.log.json) |
| SSD512 | VGG16 | - | 120e | 38.6 | [config](./ssd300_120e_urpc-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/ssd512_120e_urpc-coco_20220405_185511-88c18764.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc1/ssd512_120e_urpc-coco_20220405_185511.log.json) |

### DUO
## Results

Coming soon

## Citation

- If you use `URPC2020` or other `URPC` series dataset in your research, please cite it as below:

**Note:** The URL may not be valid, but this link is cited by many papers.

```latex
@online{urpc,
title = {Underwater Robot Professional Contest},
url = {http://uodac.pcl.ac.cn/},
}
```

- If you use `DUO` dataset in your research, please cite it as below:

```latex
@inproceedings{liu2021dataset,
title={A dataset and benchmark of underwater object detection for robot picking},
author={Liu, Chongwei and Li, Haojie and Wang, Shuchang and Zhu, Ming and Wang, Dong and Fan, Xin and Wang, Zhihui},
booktitle={2021 IEEE International Conference on Multimedia \& Expo Workshops (ICMEW)},
pages={1--6},
year={2021},
organization={IEEE}
}
```
```latex
@inproceedings{liu2021dataset,
title={A dataset and benchmark of underwater object detection for robot picking},
author={Liu, Chongwei and Li, Haojie and Wang, Shuchang and Zhu, Ming and Wang, Dong and Fan, Xin and Wang, Zhihui},
booktitle={2021 IEEE International Conference on Multimedia \& Expo Workshops (ICMEW)},
pages={1--6},
year={2021},
organization={IEEE}
}
```
13 changes: 11 additions & 2 deletions configs/detection/edffnet/README.md
View file
Original file line number Diff line number Diff line change
@@ -1,18 +1,27 @@
# Edge-Guided Dynamic Feature Fusion Network for Object Detection under Foggy Conditions

> [Edge-Guided Dynamic Feature Fusion Network for Object Detection under Foggy Conditions](https://link.springer.com/article/10.1007/s11760-022-02410-0)
<!-- [ALGORITHM] -->

## Abstract

Hazy images are often subject to blurring, low contrast and other visible quality degradation, making it challenging to solve object detection tasks. Most methods solve the domain shift problem by deep domain adaptive technology, ignoring the inaccurate object classification and localization caused by quality degradation. Different from common methods, we present an edge-guided dynamic feature fusion network (EDFFNet), which formulates the edge head as a guide to the localization task. Despite the edge head being straightforward, we demonstrate that it makes the model pay attention to the edge of object instances and improves the generalization and localization ability of the network. Considering the fuzzy details and the multi-scale problem of hazy images, we propose a dynamic fusion feature pyramid network (DF-FPN) to enhance the feature representation ability of the whole model. A unique advantage of DF-FPN is that the contribution to the fused feature map will dynamically adjust with the learning of the network. Extensive experiments verify that EDFFNet achieves 2.4% AP and 3.6% AP gains over the ATSS baseline on RTTS and Foggy Cityscapes, respectively.

<!-- [IMAGE] -->

<div align=center>
<img src="https://github.com/BIGWangYuDong/lqit/assets/48282753/82087e24-4ef6-40b4-ae95-a5893e293c1e"/>
</div>

## Results and Analysis
## Results on RTTS

Coming soon
| Architecture | Neck | Lr schd | Edge Head | lr | box AP | Config | Download |
| :----------: | :---: | :-----: | :-------: | :--: | :----: | :------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| ATSS | FPN | 1x | - | 0.01 | 48.2 | [config](../rtts_dataset/atss_r50_fpn_1x_rtts-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2/atss_r50_fpn_1x_rtts-coco_20231023_210916-98b5356b.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2/atss_r50_fpn_1x_rtts-coco_20231023_210916.log.json) |
| ATSS | FPN | 1x | - | 0.02 | 49.6 | [config](./atss_r50_fpn_1x_rtts-coco_lr002.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2/atss_r50_fpn_1x_rtts-coco_lr002_20231028_104029-114517ae.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2/atss_r50_fpn_1x_rtts-coco_lr002_20231028_104029.log.json) |
| ATSS | DFFPN | 1x | - | 0.02 | 50.3 | [config](./atss_r50_dffpn_1x_rtts-coco_lr002.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2/atss_r50_dffpn_1x_rtts-coco_lr002_20231028_104638-8f22abd9.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2/atss_r50_dffpn_1x_rtts-coco_lr002_20231028_104638.log.json) |
| ATSS | DFFPN | 1x | Y | 0.02 | 50.8 | [config](./edffnet_atss_r50_dffpn_1x_rtts-coco_lr002.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2/edffnet_atss_r50_dffpn_1x_rtts-coco_lr002_20231028_111154-89311078.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2/edffnet_atss_r50_dffpn_1x_rtts-coco_lr002_20231028_111154.log.json) |

## Citation

Expand Down
12 changes: 12 additions & 0 deletions configs/detection/edffnet/atss_r50_dffpn_1x_rtts-coco_lr002.py
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,12 @@
_base_ = ['./atss_r50_fpn_1x_rtts-coco_lr002.py']

# model settings
model = dict(
neck=dict(
type='lqit.DFFPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_input',
shape_level=2,
num_outs=5))
14 changes: 14 additions & 0 deletions ...tion/edffnet/atss_r50_fpn_1x_2xb8_rtts.py → ...dffnet/atss_r50_fpn_1x_rtts-coco_lr002.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -67,5 +67,19 @@
nms=dict(type='nms', iou_threshold=0.6),
max_per_img=100))

# optimizer
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001))

param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
end=1000),
dict(
type='MultiStepLR',
begin=0,
end=12,
by_epoch=True,
milestones=[8, 11],
gamma=0.1)
]
34 changes: 6 additions & 28 deletions configs/detection/edffnet/edffnet.py → ...fnet_atss_r50_dffpn_1x_rtts-coco_lr002.py
View file
Original file line number Diff line number Diff line change
@@ -1,17 +1,10 @@
_base_ = '../edffnet/atss_r50_fpn_1x_2xb8_rtts.py'
_base_ = ['./atss_r50_dffpn_1x_rtts-coco_lr002.py']

model = dict(
_delete_=True,
type='lqit.EDFFNet',
backbone=dict(norm_eval=True),
neck=dict(
type='lqit.DFFPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_input',
shape_level=2,
num_outs=5),
enhance_head=dict(
detector={{_base_.model}},
edge_head=dict(
_scope_='lqit',
type='EdgeHead',
in_channels=256,
Expand All @@ -23,7 +16,8 @@
mean=[128],
std=[57.12],
pad_size_divisor=32,
element_name='edge')))
element_name='edge')),
vis_enhance=False)

# dataset settings
train_pipeline = [
Expand All @@ -41,19 +35,3 @@
dict(type='lqit.PackInputs', )
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))

optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001))

param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
end=1000),
dict(
type='MultiStepLR',
begin=0,
end=12,
by_epoch=True,
milestones=[8, 11],
gamma=0.1)
]
40 changes: 40 additions & 0 deletions configs/detection/rtts_dataset/README.md
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,40 @@
# Benchmarking single-image dehazing and beyond

> [Benchmarking single-image dehazing and beyond](https://ieeexplore.ieee.org/abstract/document/8451944)
<!-- [DATASET] -->

We present a comprehensive study and evaluation of existing single-image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single-Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics to no-reference metrics and to subjective evaluation, and the novel task-driven evaluation. Experiments on RESIDE shed light on the comparisons and limitations of the state-of-the-art dehazing algorithms, and suggest promising future directions.

<!-- [IMAGE] -->

<div align=center>
<img src="https://github.com/BIGWangYuDong/lqit/assets/48282753/263606fa-5c92-4c6c-baad-b42c0998c7d8" height="400"/>
</div>

## Results

| Architecture | Backbone | Style | Lr schd | box AP | Config | Download |
| :-----------: | :------: | :-----: | :-----: | :----: | :----------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| Faster R-CNN | R-50 | pytorch | 1x | 48.1 | [config](./faster-rcnn_r50_fpn_1x_rtts-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2faster-rcnn_r50_fpn_1x_rtts-coco_20231023_211050-81f577b7.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2faster-rcnn_r50_fpn_1x_rtts-coco_20231023_211050.log.json) |
| Cascade R-CNN | R-50 | pytorch | 1x | 50.8 | [config](./cascade-rcnn_r50_fpn_1x_rtts-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2cascade-rcnn_r50_fpn_1x_rtts-coco_20231023_211029-ebfd7705.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2cascade-rcnn_r50_fpn_1x_rtts-coco_20231023_211029.log.json) |
| RetinaNet | R-50 | pytorch | 1x | 33.7 | [config](./retinanet_r50_fpn_1x_rtts-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2retinanet_r50_fpn_1x_rtts-coco_20231023_211252-594f407a.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2retinanet_r50_fpn_1x_rtts-coco_20231023_211252.log.json) |
| FCOS | R-50 | caffe | 1x | 41.0 | [config](./fcos_r50-caffe_fpn_gn-head_1x_rtts-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2fcos_r50-caffe_fpn_gn-head_1x_rtts-coco_20231023_211216-b7e2e105.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2fcos_r50-caffe_fpn_gn-head_1x_rtts-coco_20231023_211216.log.json) |
| ATSS | R-50 | pytorch | 1x | 48.2 | [config](./atss_r50_fpn_1x_rtts-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2atss_r50_fpn_1x_rtts-coco_20231023_210916-98b5356b.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2atss_r50_fpn_1x_rtts-coco_20231023_210916.log.json) |
| TOOD | R-50 | pytorch | 1x | 50.8 | [config](./tood_r50_fpn_1x_rtts-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2tood_r50_fpn_1x_rtts-coco_20231023_211348-6339a1f6.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2tood_r50_fpn_1x_rtts-coco_20231023_211348.log.json) |
| PAA | R-50 | pytorch | 1x | 49.3 | [config](./paa_r50_fpn_1x_rtts-coco.py) | [model](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2paa_r50_fpn_1x_rtts-coco_20231024_001806-04ca4793.pth) \| [log](https://github.com/BIGWangYuDong/lqit/releases/download/v0.0.1rc2paa_r50_fpn_1x_rtts-coco_20231024_001806.log.json) |

## Citation

```latex
@article{li2018benchmarking,
title={Benchmarking single-image dehazing and beyond},
author={Li, Boyi and Ren, Wenqi and Fu, Dengpan and Tao, Dacheng and Feng, Dan and Zeng, Wenjun and Wang, Zhangyang},
journal={IEEE Transactions on Image Processing},
volume={28},
number={1},
pages={492--505},
year={2018},
publisher={IEEE}
}
```
Loading

0 comments on commit 20d613f

Please sign in to comment.