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‎configs/datasets/coco_detection.yml

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@@ -12,7 +12,6 @@ EvalDataset:
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name: COCODataSet
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image_dir: val2017
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anno_path: annotations/instances_val2017.json
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#anno_path: annotations/instances_val2017_rand24.json
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dataset_dir: dataset/coco
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TestDataset:

‎configs/datasets/coco_instance.yml

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metric: COCO
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num_classes: 80
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TrainDataset:
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name: COCODataSet
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image_dir: train2017
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anno_path: annotations/instances_train2017.json
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dataset_dir: dataset/coco
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data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_poly', 'is_crowd']
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EvalDataset:
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name: COCODataSet
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image_dir: val2017
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anno_path: annotations/instances_val2017.json
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dataset_dir: dataset/coco
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TestDataset:
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name: ImageFolder
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anno_path: annotations/instances_val2017.json # also support txt (like VOC's label_list.txt)
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dataset_dir: dataset/coco # if set, anno_path will be 'dataset_dir/anno_path'

‎configs/ppyoloe_seg/README.md

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# PP-YOLOE Instance segmentation
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## 模型库
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### 实例分割模型
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| 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 |
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| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
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| PP-YOLOE_seg_s | 640 | 8 | 80e | - | 42.3 | 32.5 | 8.99 | - | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_seg_s_80e_coco.pdparams) | [配置文件](./ppyoloe_seg_s_80e_coco.yml) |
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| PP-YOLOE_seg_m | 640 | 8 | 80e | - | - | - | 26.03 | - | [下载链接]() | [配置文件](./ppyoloe_seg_m_80e_coco.yml) |
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| PP-YOLOE_seg_l | 640 | 8 | 80e | - | - | - | 57.32 | - | [下载链接]() | [配置文件](./ppyoloe_seg_l_80e_coco.yml) |
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| PP-YOLOE_seg_x | 640 | 8 | 80e | - | - | - | 107.27 | - | [下载链接]() | [配置文件](./ppyoloe_seg_x_80e_coco.yml) |

‎configs/ppyoloe_seg/_base_/optimizer_80e.yml

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epoch: 80
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LearningRate:
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base_lr: 0.001
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schedulers:
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- name: CosineDecay
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max_epochs: 96
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- name: LinearWarmup
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start_factor: 0.
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epochs: 5
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OptimizerBuilder:
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optimizer:
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momentum: 0.9
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type: Momentum
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regularizer:
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factor: 0.0005
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type: L2

‎configs/ppyoloe_seg/_base_/ppyoloe_plus_crn_seg.yml

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architecture: PPYOLOE
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norm_type: sync_bn
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use_ema: true
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ema_decay: 0.9998
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ema_black_list: ['proj_conv.weight']
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custom_black_list: ['reduce_mean']
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with_mask: True
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PPYOLOE:
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backbone: CSPResNet
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neck: CustomCSPPAN
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yolo_head: PPYOLOEInsHead
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post_process: ~
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with_mask: True
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CSPResNet:
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layers: [3, 6, 6, 3]
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channels: [64, 128, 256, 512, 1024]
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return_idx: [1, 2, 3]
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use_large_stem: True
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use_alpha: True
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CustomCSPPAN:
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out_channels: [768, 384, 192]
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stage_num: 1
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block_num: 3
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act: 'swish'
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spp: true
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PPYOLOEInsHead:
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fpn_strides: [32, 16, 8]
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grid_cell_scale: 5.0
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grid_cell_offset: 0.5
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static_assigner_epoch: -1 # only use TaskAlignedAssigner
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use_varifocal_loss: True
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loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
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assigner:
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name: TaskAlignedAssigner
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topk: 13
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alpha: 1.0
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beta: 6.0
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nms:
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name: MultiClassNMS
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nms_top_k: 1000
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keep_top_k: 300
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score_threshold: 0.01
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nms_threshold: 0.7
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return_index: True

‎configs/ppyoloe_seg/_base_/ppyoloe_plus_seg_reader.yml

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worker_num: 4
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eval_height: &eval_height 640
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eval_width: &eval_width 640
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eval_size: &eval_size [*eval_height, *eval_width]
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TrainReader:
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sample_transforms:
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- Decode: {}
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- RandomDistort: {}
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- RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
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- RandomCrop: {is_mask_crop: True}
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- RandomFlip: {}
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- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
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- Poly2Mask: {del_poly: True}
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batch_transforms:
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- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
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- Permute: {}
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- PadGT: {}
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batch_size: 8
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shuffle: true
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drop_last: true
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use_shared_memory: true
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collate_batch: False
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EvalReader:
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sample_transforms:
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- Decode: {}
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- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
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- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
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- Permute: {}
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batch_size: 1
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TestReader:
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inputs_def:
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image_shape: [3, *eval_height, *eval_width]
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sample_transforms:
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- Decode: {}
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- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
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- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
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- Permute: {}
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batch_size: 1

‎configs/ppyoloe_seg/ppyoloe_seg_l_80e_coco.yml

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_BASE_: [
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'../datasets/coco_instance.yml',
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'../runtime.yml',
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'./_base_/optimizer_80e.yml',
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'./_base_/ppyoloe_plus_crn_seg.yml',
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'./_base_/ppyoloe_plus_seg_reader.yml',
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]
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log_iter: 50
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snapshot_epoch: 5
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weights: output/ppyoloe_seg_l_80e_coco/model_final
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pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_l_obj365_pretrained.pdparams
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depth_mult: 1.0
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width_mult: 1.0

‎configs/ppyoloe_seg/ppyoloe_seg_m_80e_coco.yml

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_BASE_: [
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'../datasets/coco_instance.yml',
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'../runtime.yml',
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'./_base_/optimizer_80e.yml',
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'./_base_/ppyoloe_plus_crn_seg.yml',
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'./_base_/ppyoloe_plus_seg_reader.yml',
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]
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log_iter: 50
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snapshot_epoch: 5
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weights: output/ppyoloe_seg_m_80e_coco/model_final
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pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_l_obj365_pretrained.pdparams
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depth_mult: 0.67
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width_mult: 0.75

‎configs/ppyoloe_seg/ppyoloe_seg_s_80e_coco.yml

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_BASE_: [
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'../datasets/coco_instance.yml',
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'../runtime.yml',
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'./_base_/optimizer_80e.yml',
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'./_base_/ppyoloe_plus_crn_seg.yml',
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'./_base_/ppyoloe_plus_seg_reader.yml',
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]
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log_iter: 50
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snapshot_epoch: 5
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weights: output/ppyoloe_seg_s_80e_coco/model_final
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pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_s_obj365_pretrained.pdparams
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depth_mult: 0.33
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width_mult: 0.50

‎configs/ppyoloe_seg/ppyoloe_seg_x_80e_coco.yml

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_BASE_: [
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'../datasets/coco_instance.yml',
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'../runtime.yml',
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'./_base_/optimizer_80e.yml',
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'./_base_/ppyoloe_plus_crn_seg.yml',
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'./_base_/ppyoloe_plus_seg_reader.yml',
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]
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log_iter: 50
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snapshot_epoch: 5
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weights: output/ppyoloe_seg_x_80e_coco/model_final
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pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_x_obj365_pretrained.pdparams
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depth_mult: 1.33
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width_mult: 1.25

‎configs/rtmdet/README.md

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- [引用](#引用)
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## 模型库
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### RTMDet on COCO
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### 基础检测模型
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| 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 | AP50 | Params(M) | FLOPs(G) | 下载链接 | 配置文件 |
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| :------------- | :------- | :-------: | :------: | :---------: | :-----: |:-----: | :-----: |:-----: | :-------------: | :-----: |
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| *RTMDet-l | 640 | 32 | 300e | 10.2 | 51.2 | 68.8 | 52.31 | 160.32 |[下载链接](https://paddledet.bj.bcebos.com/models/rtmdet_l_300e_coco.pdparams) | [配置文件](./rtmdet_l_300e_coco.yml) |
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| *RTMDet-x | 640 | 32 | 300e | 18.0 | 52.6 | 70.4 | 94.86 | 283.12 |[下载链接](https://paddledet.bj.bcebos.com/models/rtmdet_x_300e_coco.pdparams) | [配置文件](./rtmdet_x_300e_coco.yml) |
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### 实例分割模型
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| 网络网络 | 输入尺寸 | 图片数/GPU | 学习率策略 | 模型推理耗时(ms) | box AP | mask AP | Params(M) | FLOPs(G) | 下载链接 | 配置文件 |
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| :------------- | :------- | :-------: | :------: | :---------: | :-----: |:-----: | :-----: |:-----: | :-------------: | :-----: |
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| *RTMDet-t | 640 | 32 | 300e | - | 40.5 | - | 5.6 | 11.8 |[下载链接](https://paddledet.bj.bcebos.com/models/rtmdet_ins_t_300e_coco.pdparams) | [配置文件](./rtmdet_ins_t_300e_coco.yml) |
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| *RTMDet-s | 640 | 32 | 300e | - | 44.0 | - | 10.18 | 21.5 |[下载链接](https://paddledet.bj.bcebos.com/models/rtmdet_ins_s_300e_coco.pdparams) | [配置文件](./rtmdet_ins_s_300e_coco.yml) |
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| *RTMDet-m | 640 | 32 | 300e | - | 48.8 | - | 27.58 | 54.13 |[下载链接](https://paddledet.bj.bcebos.com/models/rtmdet_ins_m_300e_coco.pdparams) | [配置文件](./rtmdet_ins_m_300e_coco.yml) |
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| *RTMDet-l | 640 | 32 | 300e | - | 51.2 | - | 57.37 | 106.56 |[下载链接](https://paddledet.bj.bcebos.com/models/rtmdet_ins_l_300e_coco.pdparams) | [配置文件](./rtmdet_ins_l_300e_coco.yml) |
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| *RTMDet-x | 640 | 32 | 300e | - | 52.4 | - | 102.7 | 182.7 |[下载链接](https://paddledet.bj.bcebos.com/models/rtmdet_ins_x_300e_coco.pdparams) | [配置文件](./rtmdet_ins_x_300e_coco.yml) |
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**注意:**
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- RTMDet模型暂未支持完全训练，mAP为部署权重在COCO val2017上的`mAP(IoU=0.5:0.95)`结果，且评估未使用`multi_label`等trick；

‎configs/rtmdet/_base_/rtmdet_ins_cspnext.yml

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architecture: RTMDet
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norm_type: sync_bn
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use_ema: True
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ema_decay: 0.9998
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ema_decay_type: "exponential"
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act: silu
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find_unused_parameters: True
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with_mask: True
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depth_mult: 1.0
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width_mult: 1.0
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RTMDet:
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backbone: CSPNeXt
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neck: CSPNeXtPAFPN
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head: RTMDetInsHead
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with_mask: True
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post_process: ~
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CSPNeXt:
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arch: "P5"
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return_idx: [2, 3, 4]
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# use default config
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# CSPNeXtPAFPN:
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RTMDetInsHead:
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exp_on_reg: False
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fpn_strides: [8, 16, 32]
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grid_cell_offset: 0
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nms:
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name: MultiClassNMS
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nms_top_k: 1000
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keep_top_k: 100
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score_threshold: 0.05
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nms_threshold: 0.6
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return_index: True

‎configs/rtmdet/_base_/rtmdet_reader.yml

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- Pad: {size: [640, 640], fill_value: [114., 114., 114.]}
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- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
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- Permute: {}
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batch_size: 4
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batch_size: 1
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TestReader:

‎configs/rtmdet/rtmdet_ins_l_300e_coco.yml

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_BASE_: [
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'../datasets/coco_instance.yml',
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'../runtime.yml',
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'./_base_/optimizer_300e.yml',
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'./_base_/rtmdet_ins_cspnext.yml',
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'./_base_/rtmdet_reader.yml',
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]
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depth_mult: 1.0
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width_mult: 1.0
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log_iter: 100
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snapshot_epoch: 10
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weights: output/rtmnet_ins_l_300e_coco/model_final

‎configs/rtmdet/rtmdet_ins_m_300e_coco.yml

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_BASE_: [
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'../datasets/coco_instance.yml',
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'../runtime.yml',
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'./_base_/optimizer_300e.yml',
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'./_base_/rtmdet_ins_cspnext.yml',
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'./_base_/rtmdet_reader.yml',
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]
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depth_mult: 0.67
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width_mult: 0.75
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log_iter: 100
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snapshot_epoch: 10
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weights: output/rtmnet_ins_m_300e_coco/model_final

‎configs/rtmdet/rtmdet_ins_s_300e_coco.yml

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_BASE_: [
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'../datasets/coco_instance.yml',
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'../runtime.yml',
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'./_base_/optimizer_300e.yml',
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'./_base_/rtmdet_ins_cspnext.yml',
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'./_base_/rtmdet_reader.yml',
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]
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depth_mult: 0.33
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width_mult: 0.50
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log_iter: 100
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snapshot_epoch: 10
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weights: output/rtmnet_ins_s_300e_coco/model_final
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pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/cspnext_s_pretrained.pdparams

‎configs/rtmdet/rtmdet_ins_t_300e_coco.yml

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_BASE_: [
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'../datasets/coco_instance.yml',
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'../runtime.yml',
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'./_base_/optimizer_300e.yml',
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'./_base_/rtmdet_ins_cspnext.yml',
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'./_base_/rtmdet_reader.yml',
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]
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depth_mult: 0.167 # 0.33 in yolox-tiny
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width_mult: 0.375
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log_iter: 100
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snapshot_epoch: 10
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weights: output/rtmnet_ins_t_300e_coco/model_final
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pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/cspnext_t_pretrained.pdparams

‎configs/rtmdet/rtmdet_ins_x_300e_coco.yml

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_BASE_: [
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'../datasets/coco_instance.yml',
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'../runtime.yml',
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'./_base_/optimizer_300e.yml',
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'./_base_/rtmdet_ins_cspnext.yml',
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'./_base_/rtmdet_reader.yml',
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]
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depth_mult: 1.33
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width_mult: 1.25
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log_iter: 100
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snapshot_epoch: 10
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weights: output/rtmnet_ins_x_300e_coco/model_final

‎configs/yolov5_seg/README.md

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# YOLOv5 Instance segmentation
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## 模型库
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### 实例分割模型
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| 网络网络 | 输入尺寸 | 图片数/GPU | 学习率策略 | 模型推理耗时(ms) | box AP | mask AP | Params(M) | FLOPs(G) | 下载链接 | 配置文件 |
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| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
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| *YOLOv5-n | 640 | 16 | 300e | - | 27.6 | - | 2.0 | 7.1 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_seg_n_300e_coco.pdparams) | [配置文件](./yolov5_seg_n_300e_coco.yml) |
10+
| *YOLOv5-s | 640 | 16 | 300e | - | 37.6 | - | 7.8 | 26.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_seg_s_300e_coco.pdparams) | [配置文件](./yolov5_seg_s_300e_coco.yml) |
11+
| *YOLOv5-m | 640 | 16 | 300e | - | 45.0 | - | 22.0 | 70.8 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_seg_m_300e_coco.pdparams) | [配置文件](./yolov5_seg_m_300e_coco.yml) |
12+
| *YOLOv5-l | 640 | 16 | 300e | - | 48.9 | - | 47.9 | 147.7 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_seg_l_300e_coco.pdparams) | [配置文件](./yolov5_seg_l_300e_coco.yml) |
13+
| *YOLOv5-x | 640 | 16 | 300e | - | 50.6 | - | 88.8 | 265.7 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_seg_x_300e_coco.pdparams) | [配置文件](./yolov5_seg_x_300e_coco.yml) |

‎configs/yolov5_seg/_base_/optimizer_300e.yml

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epoch: 300
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LearningRate:
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base_lr: 0.01
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schedulers:
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- !YOLOv5LRDecay
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max_epochs: 300
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min_lr_ratio: 0.01
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- !ExpWarmup
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epochs: 3
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OptimizerBuilder:
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optimizer:
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type: Momentum
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momentum: 0.937
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use_nesterov: True
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regularizer:
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factor: 0.0005
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type: L2

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