first init

configs/yolov8/openimagev7/yolov8_n_100e_oiv7.yml

+_BASE_: [
+  '../../datasets/openimagesv7_detection.yml',
+  '../../runtime.yml',
+  '../_base_/optimizer_100e.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_100e_oiv7/model_final
+
+
+YOLOv8Head:
+  customized_c3: 100
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128

configs/yolov8/openimagev7/yolov8_s_100e_oiv7.yml

+_BASE_: [
+  '../../datasets/openimagesv7_detection.yml',
+  '../../runtime.yml',
+  '../_base_/optimizer_100e.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_100e_oiv7/model_final
+
+
+YOLOv8Head:
+  customized_c3: 128
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128

configs/yolov8/openimagev7/yolov8_x_100e_oiv7.yml

+_BASE_: [
+  '../../datasets/openimagesv7_detection.yml',
+  '../../runtime.yml',
+  '../_base_/optimizer_100e.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_100e_oiv7/model_final
+
+
+YOLOv8CSPDarkNet:
+  last_stage_ch: 512 # The actual channel is int(512 * width_mult), not int(1024 * width_mult) as in YOLOv5
+
+YOLOv8Head:
+  customized_c3: 320
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128

configs/yolov8/yolov8_l_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
+width_mult: 1.0
+
+log_iter: 50
+snapshot_epoch: 10
+weights: output/yolov8_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

configs/yolov8/yolov8_m_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: 0.67
+width_mult: 0.75
+
+log_iter: 50
+snapshot_epoch: 10
+weights: output/yolov8_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

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: 50
+snapshot_epoch: 10
+weights: output/yolov8p6_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-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: 16 # 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