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PaddleDetection

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Refine PaddleDetection (#2880) 

Add PaddleDetection docs & some model configs
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README.md
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......@@ -41,7 +41,7 @@ Supported Architectures:
| Faster R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ |
| Faster R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ |
| Mask R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ |
| Mask R-CNN + FPN | ✓ | ✓ | x | ✓ | ✗ | ✗ |
| Mask R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ |
| Cascade R-CNN | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| RetinaNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Yolov3 | ✓ | ✗ | ✗ | ✗ | ✓ | ✓ |
......@@ -61,7 +61,7 @@ Advanced Features:
## Model zoo
Pretrained models are available in the PaddlePaddle [detection model zoo](docs/MODEL_ZOO.md).
Pretrained models are available in the PaddlePaddle [PaddleDetection model zoo](docs/MODEL_ZOO.md).
## Installation
......@@ -106,7 +106,14 @@ Some of the planned features include:
## Updates
#### Initial release (7/3/2019)
#### 7/29/2019
- Update Chinese docs for PaddleDetection
- Fix bug in R-CNN models when train and test at the same time
- Add ResNext101-vd + Mask R-CNN + FPN models
- Add Yolo v3 on VOC models
#### 7/3/2019
- Initial release of PaddleDetection and detection model zoo
- Models included: Faster R-CNN, Mask R-CNN, Faster R-CNN+FPN, Mask
......
README_cn.md 0 → 100644
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# PaddleDetection
PaddleDetection的目的是为工业界和学术界提供大量易使用的目标检测模型。PaddleDetection不仅性能完善，易于部署，同时能够灵活的满足算法研发需求。
<div align="center">
<img src="demo/output/000000570688.jpg" />
</div>
## 简介
特性：
- 易部署:
PaddleDetection的模型中使用的主要算子均通过C++和CUDA实现，配合PaddlePaddle的高性能预测引擎，使得在服务器环境下易于部署。
- 高灵活度：
PaddleDetection各个组件均为功能单元。例如，模型结构，数据预处理流程，用户能够通过修改配置文件轻松实现可定制化。
- 高性能：
在PaddlePaddle底层框架的帮助下，实现了更快的模型训练及更少的显存占用量。值得注意的是，Yolo v3的训练速度远快于其他框架。另外，Mask-RCNN(ResNet50)可以在Tesla V100 16GB环境下以每个GPU4张图片输入实现多卡训练。
支持的模型结构：
| | ResNet | ResNet-vd <sup>[1](#vd)</sup> | ResNeXt-vd | SENet | MobileNet | DarkNet |
|--------------------|:------:|------------------------------:|:----------:|:-----:|:---------:|:-------:|
| Faster R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ |
| Faster R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ |
| Mask R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ |
| Mask R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ |
| Cascade R-CNN | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| RetinaNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Yolov3 | ✓ | ✗ | ✗ | ✗ | ✓ | ✓ |
| SSD | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ |
<a name="vd">[1]</a> [ResNet-vd](https://arxiv.org/pdf/1812.01187) 模型提供了较大的精度提高和较少的性能损失。
扩展特性：
- [x] **Synchronized Batch Norm**: 目前在Yolo v3中使用。
- [x] **Group Norm**: 预训练模型待发布。
- [x] **Modulated Deformable Convolution**: 预训练模型待发布。
- [x] **Deformable PSRoI Pooling**: 预训练模型待发布。
**注意:** Synchronized batch normalization 只能在多GPU环境下使用，不能在CPU环境或者单GPU环境下使用。
## 模型库
基于PaddlePaddle训练的目标检测模型可参考[PaddleDetection模型库](docs/MODEL_ZOO_cn.md).
## 安装
请参考[安装说明文档](docs/INSTALL_cn.md).
## 开始
在预测阶段，可以通过运行以下指令得到可视化结果并保存在`output`目录下。
```bash
export PYTHONPATH=`pwd`:$PYTHONPATH
python tools/infer.py -c configs/mask_rcnn_r50_1x.yml \
-o weights=https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_1x.tar \
--infer_img=demo/000000570688.jpg
```
更多训练及评估流程，请参考[GETTING_STARTED_cn.md](docs/GETTING_STARTED_cn.md).
同时推荐用户参考[IPython Notebook demo](demo/mask_rcnn_demo.ipynb)
其他更多信息可参考以下文档内容：
- [配置流程介绍](docs/CONFIG_cn.md)
- [自定义数据集和预处理流程介绍](docs/DATA_cn.md)
## 未来规划
目前PaddleDetection处在持续更新的状态，接下来将会推出一系列的更新，包括如下特性：
- [ ] 混合精度训练
- [ ] 分布式训练
- [ ] Int8模式预测
- [ ] 用户自定义算子
- [ ] 进一步丰富模型库
## 版本更新
#### 7/22/2019
- 增加检测库中文文档
- 修复R-CNN系列模型训练同时进行评估的问题
- 新增ResNext101-vd + Mask R-CNN + FPN模型
- 新增基于VOC数据集的Yolo v3模型
#### 7/3/2019
- 首次发布PaddleDetection检测库和检测模型库
- 模型包括：Faster R-CNN, Mask R-CNN, Faster R-CNN+FPN, Mask
R-CNN+FPN, Cascade-Faster-RCNN+FPN, RetinaNet, Yolo v3, 和SSD.
## 如何贡献代码
我们非常欢迎你可以为PaddleDetection提供代码，也十分感谢你的反馈。
configs/mask_rcnn_r101_vd_fpn_1x.yml 0 → 100644
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architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
max_iters: 180000
snapshot_iter: 10000
use_gpu: true
log_smooth_window: 20
save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar
weights: output/mask_rcnn_r101_vd_fpn_1x/model_final
metric: COCO
num_classes: 81
MaskRCNN:
backbone: ResNet
fpn: FPN
rpn_head: FPNRPNHead
roi_extractor: FPNRoIAlign
bbox_head: BBoxHead
bbox_assigner: BBoxAssigner
ResNet:
depth: 101
feature_maps: [2, 3, 4, 5]
freeze_at: 2
norm_type: affine_channel
variant: d
FPN:
max_level: 6
min_level: 2
num_chan: 256
spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
FPNRPNHead:
anchor_generator:
aspect_ratios: [0.5, 1.0, 2.0]
variance: [1.0, 1.0, 1.0, 1.0]
anchor_start_size: 32
max_level: 6
min_level: 2
num_chan: 256
rpn_target_assign:
rpn_batch_size_per_im: 256
rpn_fg_fraction: 0.5
rpn_negative_overlap: 0.3
rpn_positive_overlap: 0.7
rpn_straddle_thresh: 0.0
train_proposal:
min_size: 0.0
nms_thresh: 0.7
pre_nms_top_n: 2000
post_nms_top_n: 2000
test_proposal:
min_size: 0.0
nms_thresh: 0.7
pre_nms_top_n: 1000
post_nms_top_n: 1000
FPNRoIAlign:
canconical_level: 4
canonical_size: 224
max_level: 5
min_level: 2
sampling_ratio: 2
box_resolution: 7
mask_resolution: 14
MaskHead:
dilation: 1
num_chan_reduced: 256
num_convs: 4
resolution: 28
BBoxAssigner:
batch_size_per_im: 512
bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
bg_thresh_hi: 0.5
bg_thresh_lo: 0.0
fg_fraction: 0.25
fg_thresh: 0.5
MaskAssigner:
resolution: 28
BBoxHead:
head: TwoFCHead
nms:
keep_top_k: 100
nms_threshold: 0.5
score_threshold: 0.05
TwoFCHead:
num_chan: 1024
LearningRate:
base_lr: 0.01
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [120000, 160000]
- !LinearWarmup
start_factor: 0.1
steps: 1000
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0001
type: L2
MaskRCNNTrainFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/mask_rcnn_x101_vd_64x4d_fpn_1x.yml 0 → 100644
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architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
max_iters: 180000
snapshot_iter: 10000
use_gpu: true
log_smooth_window: 20
save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
weights: output/mask_rcnn_x101_vd_64x4d_fpn_1x/model_final
metric: COCO
num_classes: 81
MaskRCNN:
backbone: ResNeXt
fpn: FPN
rpn_head: FPNRPNHead
roi_extractor: FPNRoIAlign
bbox_head: BBoxHead
bbox_assigner: BBoxAssigner
ResNeXt:
depth: 101
feature_maps: [2, 3, 4, 5]
freeze_at: 2
group_width: 4
groups: 64
norm_type: affine_channel
variant: d
FPN:
max_level: 6
min_level: 2
num_chan: 256
spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
FPNRPNHead:
anchor_generator:
aspect_ratios: [0.5, 1.0, 2.0]
variance: [1.0, 1.0, 1.0, 1.0]
anchor_start_size: 32
max_level: 6
min_level: 2
num_chan: 256
rpn_target_assign:
rpn_batch_size_per_im: 256
rpn_fg_fraction: 0.5
rpn_negative_overlap: 0.3
rpn_positive_overlap: 0.7
rpn_straddle_thresh: 0.0
train_proposal:
min_size: 0.0
nms_thresh: 0.7
pre_nms_top_n: 2000
post_nms_top_n: 2000
test_proposal:
min_size: 0.0
nms_thresh: 0.7
pre_nms_top_n: 1000
post_nms_top_n: 1000
FPNRoIAlign:
canconical_level: 4
canonical_size: 224
max_level: 5
min_level: 2
sampling_ratio: 2
box_resolution: 7
mask_resolution: 14
MaskHead:
dilation: 1
num_chan_reduced: 256
num_convs: 4
resolution: 28
BBoxAssigner:
batch_size_per_im: 512
bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
bg_thresh_hi: 0.5
bg_thresh_lo: 0.0
fg_fraction: 0.25
fg_thresh: 0.5
MaskAssigner:
resolution: 28
BBoxHead:
head: TwoFCHead
nms:
keep_top_k: 100
nms_threshold: 0.5
score_threshold: 0.05
TwoFCHead:
num_chan: 1024
LearningRate:
base_lr: 0.01
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [120000, 160000]
- !LinearWarmup
start_factor: 0.1
steps: 1000
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0001
type: L2
MaskRCNNTrainFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/mask_rcnn_x101_vd_64x4d_fpn_2x.yml 0 → 100644
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architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
max_iters: 360000
snapshot_iter: 10000
use_gpu: true
log_smooth_window: 20
save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
weights: output/mask_rcnn_x101_vd_64x4d_fpn_2x/model_final
metric: COCO
num_classes: 81
MaskRCNN:
backbone: ResNeXt
fpn: FPN
rpn_head: FPNRPNHead
roi_extractor: FPNRoIAlign
bbox_head: BBoxHead
bbox_assigner: BBoxAssigner
ResNeXt:
depth: 101
feature_maps: [2, 3, 4, 5]
freeze_at: 2
group_width: 4
groups: 64
norm_type: affine_channel
variant: d
FPN:
max_level: 6
min_level: 2
num_chan: 256
spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
FPNRPNHead:
anchor_generator:
aspect_ratios: [0.5, 1.0, 2.0]
variance: [1.0, 1.0, 1.0, 1.0]
anchor_start_size: 32
max_level: 6
min_level: 2
num_chan: 256
rpn_target_assign:
rpn_batch_size_per_im: 256
rpn_fg_fraction: 0.5
rpn_negative_overlap: 0.3
rpn_positive_overlap: 0.7
rpn_straddle_thresh: 0.0
train_proposal:
min_size: 0.0
nms_thresh: 0.7
pre_nms_top_n: 2000
post_nms_top_n: 2000
test_proposal:
min_size: 0.0
nms_thresh: 0.7
pre_nms_top_n: 1000
post_nms_top_n: 1000
FPNRoIAlign:
canconical_level: 4
canonical_size: 224
max_level: 5
min_level: 2
sampling_ratio: 2
box_resolution: 7
mask_resolution: 14
MaskHead:
dilation: 1
num_chan_reduced: 256
num_convs: 4
resolution: 28
BBoxAssigner:
batch_size_per_im: 512
bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
bg_thresh_hi: 0.5
bg_thresh_lo: 0.0
fg_fraction: 0.25
fg_thresh: 0.5
MaskAssigner:
resolution: 28
BBoxHead:
head: TwoFCHead
nms:
keep_top_k: 100
nms_threshold: 0.5
score_threshold: 0.05
TwoFCHead:
num_chan: 1024
LearningRate:
base_lr: 0.01
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [240000, 320000]
- !LinearWarmup
start_factor: 0.1
steps: 1000
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0001
type: L2
MaskRCNNTrainFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
docs/CONFIG.md
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......@@ -132,7 +132,7 @@ RPNHead:
Example snippet that make use of the `RPNHead` module.
```python
from ppdet.core.worskspace import load_config, merge_config, create
from ppdet.core.workspace import load_config, merge_config, create
load_config('some_config_file.yml')
merge_config(more_config_options_from_command_line)
......
docs/CONFIG_cn.md
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......@@ -124,7 +124,7 @@ RPNHead:
`RPNHead` 模块实际使用代码示例。
```python
from ppdet.core.worskspace import load_config, merge_config, create
from ppdet.core.workspace import load_config, merge_config, create
load_config('some_config_file.yml')
merge_config(more_config_options_from_command_line)
......
docs/DATA.md
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......@@ -28,7 +28,9 @@ Loads `COCO` type datasets with directory structures like this:
```
dataset/coco/
├── annotations
│ ├── instances_train2014.json
│ ├── instances_train2017.json
│ ├── instances_val2014.json
│ ├── instances_val2017.json
| ...
├── train2017
......
docs/GETTING_STARTED.md
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......@@ -20,8 +20,6 @@ python tools/train.py -c configs/faster_rcnn_r50_1x.yml
```bash
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
# or run on CPU with:
# export CPU_NUM=8
python tools/train.py -c configs/faster_rcnn_r50_1x.yml
```
......@@ -32,10 +30,10 @@ python tools/train.py -c configs/faster_rcnn_r50_1x.yml
- To check out hyper parameters used, please refer to the config file.
- RCNN models training on CPU is not supported on PaddlePaddle<=1.5.1 and will be fixed on later version.
Alternating between training epoch and evaluation run is possible, simply pass
in `--eval` to do so (tested with `SSD` detector on Pascal-VOC, not
recommended for two stage models or training sessions on COCO dataset)
Alternating between training epoch and evaluation run is possible, simply pass
in `--eval` to do so and evaluate at each snapshot_iter. If evaluation dataset is large and
causes time-consuming in training, we suggest decreasing evaluation times or evaluating after training.
## Evaluation
......
docs/GETTING_STARTED_cn.md
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......@@ -19,7 +19,7 @@ python tools/train.py -c configs/faster_rcnn_r50_1x.yml
```bash
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
# 若使用CPU，则执行
# or run on CPU with:
# export CPU_NUM=8
python tools/train.py -c configs/faster_rcnn_r50_1x.yml
```
......@@ -31,9 +31,9 @@ python tools/train.py -c configs/faster_rcnn_r50_1x.yml
- 更多参数配置，请参考配置文件。
- RCNN系列模型CPU训练在PaddlePaddle 1.5.1及以下版本暂不支持，将在下个版本修复。
可通过设置`--eval`在训练epoch中交替执行评估（已在在Pascal-VOC数据集上
用`SSD`检测器验证，不推荐在COCO数据集上的两阶段模型上执行交替评估）
可通过设置`--eval`在训练epoch中交替执行评估, 评估在每个snapshot_iter时开始。
如果验证集很大，测试将会比较耗时，影响训练速度，建议减少评估次数，或训练完再进行评估。
## 评估
......
docs/INSTALL.md
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......@@ -27,7 +27,7 @@ of your PaddlePaddle is not lower than required. Verify with the following comma
```
# To check PaddlePaddle installation in your Python interpreter
>>> import paddle.fluid as fluid
>>> import paddle.fluid as fluid
>>> fluid.install_check.run_check()
# To check PaddlePaddle version
......
docs/INSTALL_cn.md
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# Installation
# 安装文档
---
## Table of Contents
## 目录
- [简介](#introduction)
- [PaddlePaddle](#paddlepaddle)
......@@ -26,7 +26,7 @@ PaddleDetection的相关信息，请参考[README.md](../README.md).
```
# 在您的Python解释器中确认PaddlePaddle安装成功
>>> import paddle.fluid as fluid
>>> import paddle.fluid as fluid
>>> fluid.install_check.run_check()
# 确认PaddlePaddle版本
......
docs/MODEL_ZOO.md
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......@@ -50,8 +50,11 @@ The backbone models pretrained on ImageNet are available. All backbone models ar
| ResNet101-FPN | Mask | 1 | 1x | 39.5 | 35.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r101_fpn_1x.tar) |
| ResNet101-vd-FPN | Faster | 1 | 1x | 40.5 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_vd_fpn_1x.tar) |
| ResNet101-vd-FPN | Faster | 1 | 2x | 40.8 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_vd_fpn_2x.tar) |
| ResNet101-vd-FPN | Mask | 1 | 1x | 41.4 | 36.8 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r101_vd_fpn_1x.tar) |
| ResNeXt101-vd-FPN | Faster | 1 | 1x | 42.2 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_x101_vd_64x4d_fpn_1x.tar) |
| ResNeXt101-vd-FPN | Faster | 1 | 2x | 41.7 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_x101_vd_64x4d_fpn_2x.tar) |
| ResNeXt101-vd-FPN | Mask | 1 | 1x | 42.9 | 37.9 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_x101_vd_64x4d_fpn_1x.tar) |
| ResNeXt101-vd-FPN | Mask | 1 | 2x | 42.6 | 37.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_x101_vd_64x4d_fpn_2x.tar) |
| SENet154-vd-FPN | Faster | 1 | 1.44x | 42.9 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_se154_vd_fpn_s1x.tar) |
| SENet154-vd-FPN | Mask | 1 | 1.44x | 44.0 | 38.7 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_se154_vd_fpn_s1x.tar) |
......
docs/MODEL_ZOO_cn.md 0 → 100644
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# 模型库和基线
- Python 2.7.1
- PaddlePaddle 1.5
- CUDA 9.0
- CUDNN 7.4
- NCCL 2.1.2
## 通用设置
- SSD模型在VOC数据集中训练和测试，其余所有模型均在COCO17数据集中训练和测试。
- 除非特殊说明，所有ResNet骨干网络采用[ResNet-B](https://arxiv.org/pdf/1812.01187)结构。
- 对于RCNN和RetinaNet系列模型，训练阶段仅使用水平翻转作为数据增强，测试阶段不使用数据增强。
## 训练策略
- 我们采用和[Detectron](https://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#training-schedules)相同的训练策略。
- 1x 策略表示：在总batch size为16时，初始学习率为0.02，在6万轮和8万轮后学习率分别下降10倍，最终训练9万轮。在总batch size为8时，初始学习率为0.01，在12万轮和16万轮后学习率分别下降10倍，最终训练18万轮。
- 2x 策略为1x策略的两倍，同时学习率调整位置也为1x的两倍。
## ImageNet预训练模型
Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型均通过标准的Imagenet-1k数据集训练得到。[下载链接](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification#supported-models-and-performances)
- 注：ResNet50模型通过余弦学习率调整策略训练得到。[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar)
## 基线
### Faster & Mask R-CNN
| 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 | Box AP | Mask AP | 下载 |
| :------------------- | :------------- | :-----: | :-----: | :----: | :-----: | :----------------------------------------------------------: |
| ResNet50 | Faster | 1 | 1x | 35.2 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_1x.tar) |
| ResNet50 | Faster | 1 | 2x | 37.1 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_2x.tar) |
| ResNet50 | Mask | 1 | 1x | 36.5 | 32.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_1x.tar) |
| ResNet50 | Mask | 1 | 2x | 38.2 | 33.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_2x.tar) |
| ResNet50-vd | Faster | 1 | 1x | 36.4 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_1x.tar) |
| ResNet50-FPN | Faster | 2 | 1x | 37.2 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_fpn_1x.tar) |
| ResNet50-FPN | Faster | 2 | 2x | 37.7 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_fpn_2x.tar) |
| ResNet50-FPN | Mask | 1 | 1x | 37.9 | 34.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_fpn_1x.tar) |
| ResNet50-FPN | Mask | 1 | 2x | 38.7 | 34.7 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_fpn_2x.tar) |
| ResNet50-FPN | Cascade Faster | 2 | 1x | 40.9 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_r50_fpn_1x.tar) |
| ResNet50-vd-FPN | Faster | 2 | 2x | 38.9 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_2x.tar) |
| ResNet50-vd-FPN | Mask | 1 | 2x | 39.8 | 35.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_2x.tar) |
| ResNet101 | Faster | 1 | 1x | 38.3 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_1x.tar) |
| ResNet101-FPN | Faster | 1 | 1x | 38.7 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_1x.tar) |
| ResNet101-FPN | Faster | 1 | 2x | 39.1 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_2x.tar) |
| ResNet101-FPN | Mask | 1 | 1x | 39.5 | 35.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r101_fpn_1x.tar) |
| ResNet101-vd-FPN | Faster | 1 | 1x | 40.5 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_vd_fpn_1x.tar) |
| ResNet101-vd-FPN | Faster | 1 | 2x | 40.8 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_vd_fpn_2x.tar) |
| ResNet101-vd-FPN | Mask | 1 | 1x | 41.4 | 36.8 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r101_vd_fpn_1x.tar) |
| ResNeXt101-vd-FPN | Faster | 1 | 1x | 42.2 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_x101_vd_64x4d_fpn_1x.tar) |
| ResNeXt101-vd-FPN | Faster | 1 | 2x | 41.7 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_x101_vd_64x4d_fpn_2x.tar) |
| ResNeXt101-vd-FPN | Mask | 1 | 1x | 42.9 | 37.9 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_x101_vd_64x4d_fpn_1x.tar) |
| ResNeXt101-vd-FPN | Mask | 1 | 2x | 42.6 | 37.6 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_x101_vd_64x4d_fpn_2x.tar) |
| SENet154-vd-FPN | Faster | 1 | 1.44x | 42.9 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_se154_vd_fpn_s1x.tar) |
| SENet154-vd-FPN | Mask | 1 | 1.44x | 44.0 | 38.7 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_se154_vd_fpn_s1x.tar) |
### Yolo v3
| 骨架网络 | 输入尺寸 | 每张GPU图片个数 | 学习率策略 | Box AP | 下载 |
| :----------- | :--: | :-----: | :-----: | :----: | :-------: |
| DarkNet53 | 608 | 8 | 270e | 38.9 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) |
| DarkNet53 | 416 | 8 | 270e | 37.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) |
| DarkNet53 | 320 | 8 | 270e | 34.8 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) |
| MobileNet-V1 | 608 | 8 | 270e | 29.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) |
| MobileNet-V1 | 416 | 8 | 270e | 29.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) |
| MobileNet-V1 | 320 | 8 | 270e | 27.1 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) |
| ResNet34 | 608 | 8 | 270e | 36.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) |
| ResNet34 | 416 | 8 | 270e | 34.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) |
| ResNet34 | 320 | 8 | 270e | 31.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) |
### Yolo v3 基于Pasacl VOC数据集
| 骨架网络 | 输入尺寸 | 每张GPU图片个数 | 学习率策略 | Box AP | 下载 |
| :----------- | :--: | :-----: | :-----: | :----: | :-------: |
| DarkNet53 | 608 | 8 | 270e | 83.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet_voc.tar) |
| DarkNet53 | 416 | 8 | 270e | 83.6 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet_voc.tar) |
| DarkNet53 | 320 | 8 | 270e | 82.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet_voc.tar) |
| MobileNet-V1 | 608 | 8 | 270e | 76.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar) |
| MobileNet-V1 | 416 | 8 | 270e | 76.7 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar) |
| MobileNet-V1 | 320 | 8 | 270e | 75.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar) |
| ResNet34 | 608 | 8 | 270e | 82.6 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar) |
| ResNet34 | 416 | 8 | 270e | 81.9 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar) |
| ResNet34 | 320 | 8 | 270e | 80.1 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar) |
**注意事项:** Yolo v3在8卡，总batch size为64下训练270轮。数据增强包括：mixup, 随机颜色失真，随机剪裁，随机扩张，随机插值法，随机翻转。Yolo v3在训练阶段对minibatch采用随机reshape，可以采用相同的模型测试不同尺寸图片，我们分别提供了尺寸为608/416/320大小的测试结果。
### RetinaNet
| 骨架网络 | 每张GPU图片个数 | 学习率策略 | Box AP | 下载 |
| :----------- | :-----: | :-----: | :----: | :-------: |
| ResNet50-FPN | 2 | 1x | 36.0 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/retinanet_r50_fpn_1x.tar) |
| ResNet101-FPN | 2 | 1x | 37.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/retinanet_r101_fpn_1x.tar) |
**注意事项:** RetinaNet系列模型中，在总batch size为16下情况下，初始学习率改为0.01。
### SSD on Pascal VOC
| 骨架网络 | 输入尺寸 | 每张GPU图片个数 | 学习率策略 | Box AP | 下载 |
| :----------- | :--: | :-----: | :-----: | :----: | :-------: |
| MobileNet v1 | 300 | 32 | 120e | 73.13 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ssd_mobilenet_v1_voc.tar) |
**注意事项:** SSD在2卡，总batch size为64下训练120轮。数据增强包括：随机颜色失真，随机剪裁，随机扩张，随机翻转。
ppdet/utils/checkpoint.py
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......@@ -28,7 +28,11 @@ from .download import get_weights_path
import logging
logger = logging.getLogger(__name__)
__all__ = ['load_checkpoint', 'load_and_fusebn', 'save']
__all__ = [
'load_checkpoint',
'load_and_fusebn',
'save',
]
def is_url(path):
......
ppdet/utils/eval_utils.py
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......@@ -90,12 +90,12 @@ def eval_run(exe, compile_program, pyreader, keys, values, cls):
return results
def eval_results(results,
feed,
metric,
def eval_results(results,
feed,
metric,
num_classes,
resolution=None,
is_bbox_normalized=False,
resolution=None,
is_bbox_normalized=False,
output_file=None):
"""Evaluation for evaluation program results"""
if metric == 'COCO':
......@@ -122,5 +122,5 @@ def eval_results(results,
res = np.mean(results[-1]['accum_map'][0])
logger.info('mAP: {:.2f}'.format(res * 100.))
elif 'bbox' in results[0]:
voc_bbox_eval(results, num_classes,
is_bbox_normalized=is_bbox_normalized)
voc_bbox_eval(
results, num_classes, is_bbox_normalized=is_bbox_normalized)
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