siton bug

configs/yolov8/yolov8_n_500e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_500e.yml',
+  '_base_/yolov8_cspdarknet.yml',
+  '_base_/yolov8_reader.yml',
+]
+depth_mult: 0.33
+width_mult: 0.25
+
+log_iter: 50
+snapshot_epoch: 10
+weights: output/yolov8_n_500e_coco/model_final
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128

configs/yolov8/yolov8_s_500e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_500e.yml',
+  '_base_/yolov8_cspdarknet.yml',
+  '_base_/yolov8_reader.yml',
+]
+depth_mult: 0.33
+width_mult: 0.50
+
+log_iter: 50
+snapshot_epoch: 10
+weights: output/yolov8_s_500e_coco/model_final
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128

configs/yolov8/yolov8_x_500e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_500e_high.yml',
+  '_base_/yolov8_cspdarknet.yml',
+  '_base_/yolov8_reader_high_aug.yml',
+]
+depth_mult: 1.0 # not 1.33 as in YOLOv5
+width_mult: 1.25
+
+log_iter: 50
+snapshot_epoch: 10
+weights: output/yolov8_x_500e_coco/model_final
+
+
+YOLOv8CSPDarkNet:
+  last_stage_ch: 512 # The actual channel is int(512 * width_mult), not int(1024 * width_mult) as in YOLOv5
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128

configs/yolov8/yolov8p6_x_500e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_500e_high.yml',
+  '_base_/yolov8p6_cspdarknet.yml',
+  '_base_/yolov8p6_reader.yml',
+]
+depth_mult: 1.0 # not 1.33 as in YOLOv5-P6
+width_mult: 1.25
+
+log_iter: 10
+snapshot_epoch: 10
+weights: output/point_mz
+
+
+YOLOv8CSPDarkNet:
+  last_stage_ch: 512 # The actual channel is int(512 * width_mult), not int(1024 * width_mult) as in YOLOv5-P6
+  last2_stage_ch: 512 # The actual channel is int(512 * width_mult), not int(768 * width_mult) as in YOLOv5-P6
+
+
+TrainReader:
+  batch_size: 4 # default 8 gpus, total bs = 128

configs/yolov8_seg/README.md

+# YOLOv8 Instance segmentation
+
+## 模型库
+
+### 实例分割模型
+
+| 网络网络        | 输入尺寸   | 图片数/GPU | 学习率策略 | TRT-FP16-Latency(ms) | box AP | mask AP | Params(M) | FLOPs(G) |    下载链接       | 配置文件 |
+| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
+| *YOLOv8-n        |  640     |    16      |   500e   |    -   |  36.6  | - |  3.4   | 12.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov8_seg_n_500e_coco.pdparams) | [配置文件](./yolov8_seg_n_500e_coco.yml) |
+| *YOLOv8-s        |  640     |    16      |   500e   |    -   |  44.6  | - |  11.8  | 42.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov8_seg_s_500e_coco.pdparams) | [配置文件](./yolov8_seg_s_500e_coco.yml) |
+| *YOLOv8-m        |  640     |    16      |   500e   |    -   |  49.7  | - |  27.3  | 110.2 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov8_seg_m_500e_coco.pdparams) | [配置文件](./yolov8_seg_m_500e_coco.yml) |
+| *YOLOv8-l        |  640     |    16      |   500e   |    -   |  52.1  | - |  46.0  | 220.5 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov8_seg_l_500e_coco.pdparams) | [配置文件](./yolov8_seg_l_500e_coco.yml) |
+| *YOLOv8-x        |  640     |    16      |   500e   |    -   |  53.4  | - |  71.8  | 344.1 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov8_seg_x_500e_coco.pdparams) | [配置文件](./yolov8_seg_x_500e_coco.yml) |

configs/yolov8_seg/_base_/optimizer_100e.yml

+epoch: 100
+
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !YOLOv5LRDecay
+    max_epochs: 100
+    min_lr_ratio: 0.01
+  - !ExpWarmup
+    epochs: 3
+
+OptimizerBuilder:
+  optimizer:
+    type: Momentum
+    momentum: 0.937
+    use_nesterov: True
+  regularizer:
+    factor: 0.0005
+    type: L2
+  clip_grad_by_value: 10.

configs/yolov8_seg/_base_/optimizer_500e.yml

+epoch: 500
+
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !YOLOv5LRDecay
+    max_epochs: 500
+    min_lr_ratio: 0.01
+  - !ExpWarmup
+    epochs: 3
+
+OptimizerBuilder:
+  optimizer:
+    type: Momentum
+    momentum: 0.937
+    use_nesterov: True
+  regularizer:
+    factor: 0.0005
+    type: L2
+  clip_grad_by_value: 10.

configs/yolov8_seg/_base_/optimizer_500e_high.yml

+epoch: 500
+
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !YOLOv5LRDecay
+    max_epochs: 500
+    min_lr_ratio: 0.1 #
+  - !ExpWarmup
+    epochs: 3
+
+OptimizerBuilder:
+  optimizer:
+    type: Momentum
+    momentum: 0.937
+    use_nesterov: True
+  regularizer:
+    factor: 0.0005
+    type: L2
+  clip_grad_by_value: 10.

configs/yolov8_seg/_base_/yolov8_seg_cspdarknet.yml

+architecture: YOLOv8
+norm_type: sync_bn
+use_ema: True
+ema_decay: 0.9999
+ema_decay_type: "exponential"
+act: silu
+find_unused_parameters: True
+
+with_mask: True
+
+depth_mult: 1.0 # default: L version
+width_mult: 1.0
+
+YOLOv8:
+  backbone: YOLOv8CSPDarkNet
+  neck: YOLOv8CSPPAN
+  yolo_head: YOLOv8InsHead
+  with_mask: True
+  post_process: ~
+
+YOLOv8CSPDarkNet:
+  arch: 'P5'
+  return_idx: [2, 3, 4]
+  last_stage_ch: 1024
+  last2_stage_ch: 512
+
+# use default config
+# YOLOv8CSPPAN:
+
+YOLOv8InsHead:
+  fpn_strides: [8, 16, 32]
+  loss_weight: {class: 0.5, iou: 7.5, dfl: 1.5}
+  assigner:
+    name: TaskAlignedAssigner
+    topk: 10
+    alpha: 0.5
+    beta: 6.0
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 3000
+    keep_top_k: 300
+    score_threshold: 0.001
+    nms_threshold: 0.7
+    return_index: True

configs/yolov8_seg/_base_/yolov8_seg_reader.yml

+input_height: &input_height 640
+input_width: &input_width 640
+input_size: &input_size [*input_height, *input_width]
+mosaic_epoch: &mosaic_epoch 490 # last 10 epochs close mosaic, totally 500 epochs as default
+
+worker_num: 4
+TrainReader:
+  sample_transforms:
+    - DecodeNormResizeMask: {target_size: *input_size, mosaic: True}
+    - MosaicPerspective: {mosaic_prob: 0.0, target_size: *input_size, with_mask: True}
+    - Poly2Mask: {del_poly: True}
+    - RandomHSV: {hgain: 0.015, sgain: 0.7, vgain: 0.4}
+    - RandomFlip: {}
+  batch_transforms:
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+    - PadGT: {}
+  batch_size: 8
+  shuffle: True
+  drop_last: False
+  use_shared_memory: True
+  collate_batch: False
+  mosaic_epoch: *mosaic_epoch
+
+
+EvalReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: *input_size, keep_ratio: True, interp: 1}
+    - Pad: {size: *input_size, fill_value: [114., 114., 114.]}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 1
+
+
+TestReader:
+  inputs_def:
+    image_shape: [3, 640, 640]
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: *input_size, keep_ratio: True, interp: 1}
+    - Pad: {size: *input_size, fill_value: [114., 114., 114.]}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 1
+  fuse_normalize: False

configs/yolov8_seg/_base_/yolov8_seg_reader_high_aug.yml

+input_height: &input_height 640
+input_width: &input_width 640
+input_size: &input_size [*input_height, *input_width]
+mosaic_epoch: &mosaic_epoch 490 # last 10 epochs close mosaic, totally 500 epochs as default
+
+worker_num: 4
+TrainReader:
+  sample_transforms:
+    - DecodeNormResizeMask: {target_size: *input_size, mosaic: True}
+    - MosaicPerspective: {mosaic_prob: 0.0, target_size: *input_size, scale: 0.9, mixup_prob: 0.1, copy_paste_prob: 0.1, with_mask: True}
+    - Poly2Mask: {del_poly: True}
+    - RandomHSV: {hgain: 0.015, sgain: 0.7, vgain: 0.4}
+    - RandomFlip: {}
+  batch_transforms:
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+    - PadGT: {}
+  batch_size: 8
+  shuffle: True
+  drop_last: False
+  use_shared_memory: True
+  collate_batch: False
+  mosaic_epoch: *mosaic_epoch
+
+
+EvalReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: *input_size, keep_ratio: True, interp: 1}
+    - Pad: {size: *input_size, fill_value: [114., 114., 114.]}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 1
+
+
+TestReader:
+  inputs_def:
+    image_shape: [3, 640, 640]
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: *input_size, keep_ratio: True, interp: 1}
+    - Pad: {size: *input_size, fill_value: [114., 114., 114.]}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 1
+  fuse_normalize: False

configs/yolov8_seg/yolov8_seg_l_500e_coco.yml

+_BASE_: [
+  '../datasets/coco_instance.yml',
+  '../runtime.yml',
+  '_base_/optimizer_500e_high.yml',
+  '_base_/yolov8_seg_cspdarknet.yml',
+  '_base_/yolov8_seg_reader_high_aug.yml',
+]
+depth_mult: 1.0
+width_mult: 1.0
+
+log_iter: 50
+snapshot_epoch: 10
+weights: output/yolov8_seg_l_500e_coco/model_final
+
+
+YOLOv8CSPDarkNet:
+  last_stage_ch: 512 # The actual channel is int(512 * width_mult), not int(1024 * width_mult) as in YOLOv5
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128
+
+EvalReader:
+  batch_size: 1

configs/yolov8_seg/yolov8_seg_m_500e_coco.yml

+_BASE_: [
+  '../datasets/coco_instance.yml',
+  '../runtime.yml',
+  '_base_/optimizer_500e_high.yml',
+  '_base_/yolov8_seg_cspdarknet.yml',
+  '_base_/yolov8_seg_reader_high_aug.yml',
+]
+depth_mult: 0.67
+width_mult: 0.75
+
+log_iter: 50
+snapshot_epoch: 10
+weights: output/yolov8_seg_m_500e_coco/model_final
+
+
+YOLOv8CSPDarkNet:
+  last_stage_ch: 768 # The actual channel is int(768 * width_mult), not int(1024 * width_mult) as in YOLOv5
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128
+
+EvalReader:
+  batch_size: 1

configs/yolov8_seg/yolov8_seg_n_500e_coco.yml

+_BASE_: [
+  '../datasets/coco_instance.yml',
+  '../runtime.yml',
+  '_base_/optimizer_100e.yml',
+  '_base_/yolov8_seg_cspdarknet.yml',
+  '_base_/yolov8_seg_reader.yml',
+]
+depth_mult: 0.33
+width_mult: 0.25
+
+log_iter: 50
+snapshot_epoch: 10
+weights: output/yolov8_seg_n_500e_coco/model_final
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128
+
+EvalReader:
+  batch_size: 1

configs/yolov8_seg/yolov8_seg_s_500e_coco.yml

+_BASE_: [
+  '../datasets/coco_instance.yml',
+  '../runtime.yml',
+  '_base_/optimizer_500e.yml',
+  '_base_/yolov8_seg_cspdarknet.yml',
+  '_base_/yolov8_seg_reader.yml',
+]
+depth_mult: 0.33
+width_mult: 0.50
+
+log_iter: 50
+snapshot_epoch: 10
+weights: output/yolov8_seg_s_500e_coco/model_final
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128
+
+EvalReader:
+  batch_size: 1

configs/yolov8_seg/yolov8_seg_x_500e_coco.yml

+_BASE_: [
+  '../datasets/coco_instance.yml',
+  '../runtime.yml',
+  '_base_/optimizer_500e_high.yml',
+  '_base_/yolov8_seg_cspdarknet.yml',
+  '_base_/yolov8_seg_reader_high_aug.yml',
+]
+depth_mult: 1.0 # not 1.33 as in YOLOv5
+width_mult: 1.25
+
+log_iter: 50
+snapshot_epoch: 10
+weights: output/yolov8_seg_x_500e_coco/model_final
+
+
+YOLOv8CSPDarkNet:
+  last_stage_ch: 512 # The actual channel is int(512 * width_mult), not int(1024 * width_mult) as in YOLOv5
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128
+
+EvalReader:
+  batch_size: 1

configs/yolox/README.md

+# YOLOX (YOLOX: Exceeding YOLO Series in 2021)
+
+## 内容
+- [模型库](#模型库)
+- [使用说明](#使用说明)
+- [速度测试](#速度测试)
+- [引用](#引用)
+
+
+## 模型库
+### YOLOX on COCO
+
+| 网络网络        | 输入尺寸   | 图片数/GPU | 学习率策略 | 模型推理耗时(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) |    下载链接       | 配置文件 |
+| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
+| YOLOX-nano     |  416     |    8      |   300e    |     2.3    |  26.1  |  42.0 |  0.91  |  1.08 | [下载链接](https://paddledet.bj.bcebos.com/models/yolox_nano_300e_coco.pdparams) | [配置文件](./yolox_nano_300e_coco.yml) |
+| YOLOX-tiny     |  416     |    8      |   300e    |     2.8    |  32.9  |  50.4 |  5.06  |  6.45 | [下载链接](https://paddledet.bj.bcebos.com/models/yolox_tiny_300e_coco.pdparams) | [配置文件](./yolox_tiny_300e_coco.yml) |
+| YOLOX-s        |  640     |    8      |   300e    |     3.0    |  40.4  |  59.6 |  9.0  |  26.8 | [下载链接](https://paddledet.bj.bcebos.com/models/yolox_s_300e_coco.pdparams) | [配置文件](./yolox_s_300e_coco.yml) |
+| YOLOX-m        |  640     |    8      |   300e    |     5.8    |  46.9  |  65.7 |  25.3  |  73.8 | [下载链接](https://paddledet.bj.bcebos.com/models/yolox_m_300e_coco.pdparams) | [配置文件](./yolox_m_300e_coco.yml) |
+| YOLOX-l        |  640     |    8      |   300e    |     9.3    |  50.1  |  68.8 |  54.2  |  155.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolox_l_300e_coco.pdparams) | [配置文件](./yolox_l_300e_coco.yml) |
+| YOLOX-x        |  640     |    8      |   300e    |     16.6   |  **51.8**  |  **70.6** |  99.1  |  281.9 | [下载链接](https://paddledet.bj.bcebos.com/models/yolox_x_300e_coco.pdparams) | [配置文件](./yolox_x_300e_coco.yml) |
+
+
+| 网络网络        | 输入尺寸   | 图片数/GPU | 学习率策略 | 模型推理耗时(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) |    下载链接       | 配置文件 |
+| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
+| YOLOX-cdn-tiny    |  416     |    8      |   300e    |     1.9    |  32.4  |  50.2 |  5.03 |  6.33  | [下载链接](https://paddledet.bj.bcebos.com/models/yolox_cdn_tiny_300e_coco.pdparams) | [配置文件](./yolox_cdn_tiny_300e_coco.yml) |
+| YOLOX-crn-s     |  640     |    8      |   300e    |     3.0    |  40.4  |  59.6 |  7.7  |  24.69 | [下载链接](https://paddledet.bj.bcebos.com/models/yolox_crn_s_300e_coco.pdparams) | [配置文件](./yolox_crn_s_300e_coco.yml) |
+| YOLOX-ConvNeXt-s|  640     |    8      |   36e     |     -      |  **44.6**  |  **65.3** |  36.2 |  27.52 | [下载链接](https://paddledet.bj.bcebos.com/models/yolox_convnext_s_36e_coco.pdparams) | [配置文件](../convnext/yolox_convnext_s_36e_coco.yml) |
+
+
+**注意:**
+  - YOLOX模型训练使用COCO train2017作为训练集，YOLOX-cdn表示使用与YOLOv5 releases v6.0之后版本相同的主干网络，YOLOX-crn表示使用与PPYOLOE相同的主干网络CSPResNet，YOLOX-ConvNeXt表示使用ConvNeXt作为主干网络；
+  - YOLOX模型训练过程中默认使用8 GPUs进行混合精度训练，默认每卡batch_size为8，默认lr为0.01为8卡总batch_size=64的设置，如果**GPU卡数**或者每卡**batch size**发生了改变，你需要按照公式 **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)** 调整学习率；
+  - 为保持高mAP的同时提高推理速度，可以将[yolox_cspdarknet.yml](_base_/yolox_cspdarknet.yml)中的`nms_top_k`修改为`1000`，将`keep_top_k`修改为`100`，将`score_threshold`修改为`0.01`，mAP会下降约0.1~0.2%；
+  - 为快速的demo演示效果，可以将[yolox_cspdarknet.yml](_base_/yolox_cspdarknet.yml)中的`score_threshold`修改为`0.25`，将`nms_threshold`修改为`0.45`，但mAP会下降较多；
+  - YOLOX模型推理速度测试采用单卡V100，batch size=1进行测试，使用**CUDA 10.2**, **CUDNN 7.6.5**，TensorRT推理速度测试使用**TensorRT 6.0.1.8**。
+  - 参考[速度测试](#速度测试)以复现YOLOX推理速度测试结果，速度为**tensorRT-FP16**测速后的最快速度，**不包含数据预处理和模型输出后处理(NMS)**的耗时。
+  - 如果你设置了`--run_benchmark=True`, 你首先需要安装以下依赖`pip install pynvml psutil GPUtil`。
+
+## 使用教程
+
+### 1.训练
+执行以下指令使用混合精度训练YOLOX
+```bash
+python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/yolox/yolox_s_300e_coco.yml --amp --eval
+```
+**注意:**
+- `--amp`表示开启混合精度训练以避免显存溢出，`--eval`表示边训边验证。
+
+### 2.评估
+执行以下命令在单个GPU上评估COCO val2017数据集
+```bash
+CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/yolox/yolox_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolox_s_300e_coco.pdparams
+```
+
+### 3.推理
+使用以下命令在单张GPU上预测图片，使用`--infer_img`推理单张图片以及使用`--infer_dir`推理文件中的所有图片。
+```bash
+# 推理单张图片
+CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/yolox/yolox_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolox_s_300e_coco.pdparams --infer_img=demo/000000014439_640x640.jpg
+
+# 推理文件中的所有图片
+CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/yolox/yolox_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolox_s_300e_coco.pdparams --infer_dir=demo
+```
+
+### 4.导出模型
+YOLOX在GPU上推理部署或benchmark测速等需要通过`tools/export_model.py`导出模型。
+
+当你**使用Paddle Inference但不使用TensorRT**时，运行以下的命令导出模型
+
+```bash
+python tools/export_model.py -c configs/yolox/yolox_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolox_s_300e_coco.pdparams
+```
+
+当你**使用Paddle Inference且使用TensorRT**时，需要指定`-o trt=True`来导出模型。
+
+```bash
+python tools/export_model.py -c configs/yolox/yolox_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolox_s_300e_coco.pdparams trt=True
+```
+
+如果你想将YOLOX模型导出为**ONNX格式**，参考
+[PaddleDetection模型导出为ONNX格式教程](../../deploy/EXPORT_ONNX_MODEL.md)，运行以下命令：
+
+```bash
+
+# 导出推理模型
+python tools/export_model.py -c configs/yolox/yolox_s_300e_coco.yml --output_dir=output_inference -o weights=https://paddledet.bj.bcebos.com/models/yolox_s_300e_coco.pdparams
+
+# 安装paddle2onnx
+pip install paddle2onnx
+
+# 转换成onnx格式
+paddle2onnx --model_dir output_inference/yolox_s_300e_coco --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 11 --save_file yolox_s_300e_coco.onnx
+```
+
+**注意：** ONNX模型目前只支持batch_size=1
+
+
+### 5.推理部署
+YOLOX可以使用以下方式进行部署：
+  - Paddle Inference [Python](../../deploy/python) & [C++](../../deploy/cpp)
+  - [Paddle-TensorRT](../../deploy/TENSOR_RT.md)
+  - [PaddleServing](https://github.com/PaddlePaddle/Serving)
+  - [PaddleSlim模型量化](../slim)
+
+运行以下命令导出模型
+
+```bash
+python tools/export_model.py -c configs/yolox/yolox_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolox_s_300e_coco.pdparams trt=True
+```
+
+**注意：**
+- trt=True表示**使用Paddle Inference且使用TensorRT**进行测速，速度会更快，默认不加即为False，表示**使用Paddle Inference但不使用TensorRT**进行测速。
+- 如果是使用Paddle Inference在TensorRT FP16模式下部署，需要参考[Paddle Inference文档](https://www.paddlepaddle.org.cn/inference/master/user_guides/download_lib.html#python)，下载并安装与你的CUDA, CUDNN和TensorRT相应的wheel包。
+
+#### 5.1.Python部署
+`deploy/python/infer.py`使用上述导出后的Paddle Inference模型用于推理和benchnark测速，如果设置了`--run_benchmark=True`, 首先需要安装以下依赖`pip install pynvml psutil GPUtil`。
+
+```bash
+# Python部署推理单张图片
+python deploy/python/infer.py --model_dir=output_inference/yolox_s_300e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu
+
+# 推理文件夹下的所有图片
+python deploy/python/infer.py --model_dir=output_inference/yolox_s_300e_coco --image_dir=demo/ --device=gpu
+```
+
+#### 5.2. C++部署
+`deploy/cpp/build/main`使用上述导出后的Paddle Inference模型用于C++推理部署, 首先按照[docs](../../deploy/cpp/docs)编译安装环境。
+```bash
+# C++部署推理单张图片
+./deploy/cpp/build/main --model_dir=output_inference/yolox_s_300e_coco/ --image_file=demo/000000014439_640x640.jpg --run_mode=paddle --device=GPU --threshold=0.5 --output_dir=cpp_infer_output/yolox_s_300e_coco
+```
+
+
+## 速度测试
+
+为了公平起见，在[模型库](#模型库)中的速度测试结果均为不包含数据预处理和模型输出后处理(NMS)的数据(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)，需要在导出模型时指定`-o exclude_nms=True`。测速需设置`--run_benchmark=True`, 首先需要安装以下依赖`pip install pynvml psutil GPUtil`。
+
+**使用Paddle Inference但不使用TensorRT**进行测速，执行以下命令：
+
+```bash
+# 导出模型
+python tools/export_model.py -c configs/yolox/yolox_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolox_s_300e_coco.pdparams exclude_nms=True
+
+# 速度测试，使用run_benchmark=True
+python deploy/python/infer.py --model_dir=output_inference/yolox_s_300e_coco --image_file=demo/000000014439_640x640.jpg --run_mode=paddle --device=gpu --run_benchmark=True
+```
+
+**使用Paddle Inference且使用TensorRT**进行测速，执行以下命令：
+
+```bash
+# 导出模型，使用trt=True
+python tools/export_model.py -c configs/yolox/yolox_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolox_s_300e_coco.pdparams exclude_nms=True trt=True
+
+# 速度测试，使用run_benchmark=True
+python deploy/python/infer.py --model_dir=output_inference/yolox_s_300e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_benchmark=True
+
+# tensorRT-FP32测速
+python deploy/python/infer.py --model_dir=output_inference/yolox_s_300e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_benchmark=True --run_mode=trt_fp32
+
+# tensorRT-FP16测速
+python deploy/python/infer.py --model_dir=output_inference/yolox_s_300e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_benchmark=True --run_mode=trt_fp16
+```
+**注意:**
+- 导出模型时指定`-o exclude_nms=True`仅作为测速时用，这样导出的模型其推理部署预测的结果不是最终检出框的结果。
+- [模型库](#模型库)中的速度测试结果为**tensorRT-FP16**测速后的最快速度，为**不包含数据预处理和模型输出后处理(NMS)**的耗时。
+
+## FAQ
+
+<details>
+<summary>如何计算模型参数量</summary>
+可以将以下代码插入：[trainer.py](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/engine/trainer.py#L154) 来计算参数量。
+```python
+params = sum([
+    p.numel() for n, p in self.model.named_parameters()
+    if all([x not in n for x in ['_mean', '_variance']])
+]) # exclude BatchNorm running status
+print('Params: ', params)
+```
+</details>
+
+
+## Citations
+```
+ @article{yolox2021,
+  title={YOLOX: Exceeding YOLO Series in 2021},
+  author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
+  journal={arXiv preprint arXiv:2107.08430},
+  year={2021}
+}
+```

configs/yolox/_base_/optimizer_300e.yml

+epoch: 300
+
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !CosineDecay
+    max_epochs: 300
+    min_lr_ratio: 0.05
+    last_plateau_epochs: 15
+  - !ExpWarmup
+    epochs: 5
+
+OptimizerBuilder:
+  optimizer:
+    type: Momentum
+    momentum: 0.9
+    use_nesterov: True
+  regularizer:
+    factor: 0.0005
+    type: L2

configs/yolox/_base_/yolox_cspdarknet.yml

+architecture: YOLOX
+norm_type: sync_bn
+use_ema: True
+ema_decay: 0.9999
+ema_decay_type: "exponential"
+act: silu
+find_unused_parameters: True
+
+depth_mult: 1.0
+width_mult: 1.0
+
+YOLOX:
+  backbone: CSPDarkNet
+  neck: YOLOCSPPAN
+  head: YOLOXHead
+  size_stride: 32
+  size_range: [15, 25] # multi-scale range [480*480 ~ 800*800]
+
+CSPDarkNet:
+  arch: "X"
+  return_idx: [2, 3, 4]
+  depthwise: False
+
+YOLOCSPPAN:
+  depthwise: False
+
+YOLOXHead:
+  l1_epoch: 285
+  depthwise: False
+  loss_weight: {cls: 1.0, obj: 1.0, iou: 5.0, l1: 1.0}
+  assigner:
+    name: SimOTAAssigner
+    candidate_topk: 10
+    use_vfl: False
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 10000
+    keep_top_k: 1000
+    score_threshold: 0.001
+    nms_threshold: 0.65
+    # For speed while keep high mAP, you can modify 'nms_top_k' to 1000 and 'keep_top_k' to 100, the mAP will drop about 0.1%.
+    # For high speed demo, you can modify 'score_threshold' to 0.25 and 'nms_threshold' to 0.45, but the mAP will drop a lot.

configs/yolox/_base_/yolox_reader.yml

+worker_num: 4
+TrainReader:
+  sample_transforms:
+    - Decode: {}
+    - Mosaic:
+        prob: 1.0
+        input_dim: [640, 640]
+        degrees: [-10, 10]
+        scale: [0.1, 2.0]
+        shear: [-2, 2]
+        translate: [-0.1, 0.1]
+        enable_mixup: True
+        mixup_prob: 1.0
+        mixup_scale: [0.5, 1.5]
+    - AugmentHSV: {is_bgr: False, hgain: 5, sgain: 30, vgain: 30}
+    - PadResize: {target_size: 640}
+    - RandomFlip: {}
+  batch_transforms:
+    - Permute: {}
+  batch_size: 8
+  shuffle: True
+  drop_last: True
+  collate_batch: False
+  mosaic_epoch: 285
+
+
+EvalReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [640, 640], keep_ratio: True, interp: 1}
+    - Pad: {size: [640, 640], fill_value: [114., 114., 114.]}
+    - Permute: {}
+  batch_size: 4
+
+
+TestReader:
+  inputs_def:
+    image_shape: [3, 640, 640]
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [640, 640], keep_ratio: True, interp: 1}
+    - Pad: {size: [640, 640], fill_value: [114., 114., 114.]}
+    - Permute: {}
+  batch_size: 1