更换文档检测模型

paddle_detection/configs/convnext/README.md

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+# ConvNeXt (A ConvNet for the 2020s)
+
+## 模型库
+### ConvNeXt on COCO
+
+| 网络网络                  | 输入尺寸 | 图片数/GPU | 学习率策略 | 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 |
+| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |
+| PP-YOLOE-ConvNeXt-tiny | 640 |    16      |   36e    |  44.6  |  63.3 |  33.04  |  13.87 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_convnext_tiny_36e_coco.pdparams) | [配置文件](./ppyoloe_convnext_tiny_36e_coco.yml) |
+| YOLOX-ConvNeXt-s       | 640 |    8       |   36e    |  44.6  |  65.3 |  36.20  |  27.52 | [下载链接](https://paddledet.bj.bcebos.com/models/yolox_convnext_s_36e_coco.pdparams) | [配置文件](./yolox_convnext_s_36e_coco.yml) |
+
+
+## Citations
+```
+@Article{liu2022convnet,
+  author  = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
+  title   = {A ConvNet for the 2020s},
+  journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year    = {2022},
+}
+```

paddle_detection/configs/convnext/ppyoloe_convnext_tiny_36e_coco.yml

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+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '../ppyoloe/_base_/ppyoloe_crn.yml',
+  '../ppyoloe/_base_/ppyoloe_reader.yml',
+]
+depth_mult: 0.25
+width_mult: 0.50
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_convnext_tiny_36e_coco/model_final
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/convnext_tiny_22k_224.pdparams
+
+
+YOLOv3:
+  backbone: ConvNeXt
+  neck: CustomCSPPAN
+  yolo_head: PPYOLOEHead
+  post_process: ~
+
+ConvNeXt:
+  arch: 'tiny'
+  drop_path_rate: 0.4
+  layer_scale_init_value: 1.0
+  return_idx: [1, 2, 3]
+
+
+PPYOLOEHead:
+  static_assigner_epoch: 12
+  nms:
+    nms_top_k: 1000
+    keep_top_k: 300
+    score_threshold: 0.01
+    nms_threshold: 0.7
+
+
+TrainReader:
+  batch_size: 16
+
+
+epoch: 36
+LearningRate:
+  base_lr: 0.0002
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [36]
+    use_warmup: false
+
+OptimizerBuilder:
+  regularizer: false
+  optimizer:
+    type: AdamW
+    weight_decay: 0.0005

paddle_detection/configs/convnext/yolox_convnext_s_36e_coco.yml

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+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '../yolox/_base_/yolox_cspdarknet.yml',
+  '../yolox/_base_/yolox_reader.yml'
+]
+depth_mult: 0.33
+width_mult: 0.50
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/yolox_convnext_s_36e_coco/model_final
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/convnext_tiny_22k_224.pdparams
+
+
+YOLOX:
+  backbone: ConvNeXt
+  neck: YOLOCSPPAN
+  head: YOLOXHead
+  size_stride: 32
+  size_range: [15, 25] # multi-scale range [480*480 ~ 800*800]
+
+ConvNeXt:
+  arch: 'tiny'
+  drop_path_rate: 0.4
+  layer_scale_init_value: 1.0
+  return_idx: [1, 2, 3]
+
+
+TrainReader:
+  batch_size: 8
+  mosaic_epoch: 30
+
+
+YOLOXHead:
+  l1_epoch: 30
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 10000
+    keep_top_k: 1000
+    score_threshold: 0.001
+    nms_threshold: 0.65
+
+
+epoch: 36
+LearningRate:
+  base_lr: 0.0002
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [36]
+    use_warmup: false
+
+OptimizerBuilder:
+  regularizer: false
+  optimizer:
+    type: AdamW
+    weight_decay: 0.0005