38 lines
2.6 KiB
Markdown
38 lines
2.6 KiB
Markdown
# FCOS (Fully Convolutional One-Stage Object Detection)
|
||
|
||
## Model Zoo on COCO
|
||
|
||
| 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | 下载 | 配置文件 |
|
||
| :------------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
|
||
| ResNet50-FPN | FCOS | 2 | 1x | ---- | 39.6 | [download](https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_1x_coco.pdparams) | [config](./fcos_r50_fpn_1x_coco.yml) |
|
||
| ResNet50-FPN | FCOS + iou | 2 | 1x | ---- | 40.0 | [download](https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_iou_1x_coco.pdparams) | [config](./fcos_r50_fpn_iou_1x_coco.yml) |
|
||
| ResNet50-FPN | FCOS + DCN | 2 | 1x | ---- | 44.3 | [download](https://paddledet.bj.bcebos.com/models/fcos_dcn_r50_fpn_1x_coco.pdparams) | [config](./fcos_dcn_r50_fpn_1x_coco.yml) |
|
||
| ResNet50-FPN | FCOS + multiscale_train | 2 | 2x | ---- | 41.8 | [download](https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_multiscale_2x_coco.pdparams) | [config](./fcos_r50_fpn_multiscale_2x_coco.yml) |
|
||
| ResNet50-FPN | FCOS + multiscale_train + iou | 2 | 2x | ---- | 42.6 | [download](https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_iou_multiscale_2x_coco.pdparams) | [config](./fcos_r50_fpn_iou_multiscale_2x_coco.yml) |
|
||
|
||
**注意:**
|
||
- `+ iou` 表示与原版 FCOS 相比，不使用 `centerness` 而是使用 `iou` 来参与计算loss。
|
||
- 基于 FCOS 的半监督检测方法 `DenseTeaher` 可以参照[DenseTeaher](../semi_det/denseteacher)去使用，结合无标签数据可以进一步提升检测性能。
|
||
- PaddleDetection中默认使用`R50-vb`预训练，如果使用`R50-vd`结合[SSLD](../../../docs/feature_models/SSLD_PRETRAINED_MODEL.md)的预训练模型，可进一步显著提升检测精度，同时backbone部分配置也需要做出相应更改，如：
|
||
```python
|
||
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_v2_pretrained.pdparams
|
||
ResNet:
|
||
depth: 50
|
||
variant: d
|
||
norm_type: bn
|
||
freeze_at: 0
|
||
return_idx: [1, 2, 3]
|
||
num_stages: 4
|
||
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
|
||
```
|
||
|
||
## Citations
|
||
```
|
||
@inproceedings{tian2019fcos,
|
||
title = {{FCOS}: Fully Convolutional One-Stage Object Detection},
|
||
author = {Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},
|
||
booktitle = {Proc. Int. Conf. Computer Vision (ICCV)},
|
||
year = {2019}
|
||
}
|
||
```
|