【Hackathon 8th No.37】Add YOLO11 for PaddleYOLO

configs/yolo11/_base_/optimizer_600e.yml

@@ -0,0 +1,20 @@
+epoch: 600
+
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !YOLOv5LRDecay
+    max_epochs: 600
+    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/yolo11/_base_/optimizer_600e_high.yml

@@ -0,0 +1,20 @@
+epoch: 600
+
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !YOLOv5LRDecay
+    max_epochs: 600
+    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/yolo11/_base_/yolo11_cspdarknet.yml

@@ -0,0 +1,35 @@
+architecture: YOLO11
+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 # default: L version
+width_mult: 1.0
+max_channels: 1024
+
+YOLO11:
+  backbone: YOLO11CSPDarkNet
+  neck: YOLO11CSPPAN
+  yolo_head: YOLO11Head
+  post_process: ~
+
+YOLO11CSPDarkNet:
+  return_idx: [2, 3, 4]
+
+YOLO11Head:
+  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

configs/yolo11/_base_/yolo11_reader.yml

@@ -0,0 +1,45 @@
+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:
+    - Decode: {}
+    - MosaicPerspective: {mosaic_prob: 1.0, boxes_normed: False, target_size: *input_size}
+    - 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: True
+  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: 8
+
+
+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/yolo11/_base_/yolo11_reader_high_aug.yml

@@ -0,0 +1,45 @@
+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:
+    - Decode: {}
+    - MosaicPerspective: {mosaic_prob: 1.0, boxes_normed: False, target_size: *input_size, scale: 0.9, mixup_prob: 0.1, copy_paste_prob: 0.1}
+    - 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: True
+  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/yolo11/yolo11_n_600e_coco.yml

@@ -0,0 +1,18 @@
+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_600e.yml',
+  '_base_/yolo11_cspdarknet.yml',
+  '_base_/yolo11_reader.yml',
+]
+
+depth_mult: 0.5
+width_mult: 0.25
+max_channels: 1024
+
+log_iter: 50
+snapshot_epoch: 1
+weights: output/yolo11_n_600e_coco/model_final
+
+TrainReader:
+  batch_size: 128 # default 1 gpu, total bs = 128

ppdet/modeling/architectures/__init__.py

@@ -24,6 +24,7 @@
 from . import rtmdet
 from . import detr
 from . import yolov10
+from . import yolo11
 
 from .meta_arch import *
 from .yolo import *
@@ -36,4 +37,5 @@
 from .yolov8 import *
 from .rtmdet import *
 from .detr import *
-from .yolov10 import *
+from .yolov10 import *
+from .yolo11 import *

ppdet/modeling/architectures/yolo11.py

@@ -0,0 +1,114 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from ppdet.core.workspace import register, create
+from .meta_arch import BaseArch
+
+__all__ = ["YOLO11"]
+
+
+@register
+class YOLO11(BaseArch):
+    __category__ = "architecture"
+    __inject__ = ["post_process"]
+    __shared__ = ["with_mask"]
+
+    def __init__(
+        self,
+        backbone="YOLO11CSPDarkNet",
+        neck="YOLO11CSPPAN",
+        yolo_head="YOLOv8Head",
+        post_process="BBoxPostProcess",
+        with_mask=False,
+        for_mot=False,
+    ):
+        """
+        YOLOv11
+
+        Args:
+            backbone (nn.Layer): backbone instance
+            neck (nn.Layer): neck instance
+            yolo_head (nn.Layer): anchor_head instance
+            for_mot (bool): whether return other features for multi-object tracking
+                models, default False in pure object detection models.
+        """
+        super().__init__()
+        self.backbone = backbone
+        self.neck = neck
+        self.yolo_head = yolo_head
+        self.post_process = post_process
+        self.for_mot = for_mot
+        self.with_mask = with_mask
+
+    @classmethod
+    def from_config(cls, cfg, *args, **kwargs):
+        # backbone
+        backbone = create(cfg["backbone"])
+
+        # fpn
+        kwargs = {"input_shape": backbone.out_shape}
+        neck = create(cfg["neck"], **kwargs)
+
+        # head
+        kwargs = {"input_shape": neck.out_shape}
+        yolo_head = create(cfg["yolo_head"], **kwargs)
+
+        return {
+            "backbone": backbone,
+            "neck": neck,
+            "yolo_head": yolo_head,
+        }
+
+    def _forward(self):
+        body_feats = self.backbone(self.inputs)
+        neck_feats = self.neck(body_feats, self.for_mot)
+
+        if self.training:
+            yolo_losses = self.yolo_head(neck_feats, self.inputs)
+            return yolo_losses
+        else:
+            yolo_head_outs = self.yolo_head(neck_feats)
+            post_outs = self.yolo_head.post_process(
+                yolo_head_outs,
+                im_shape=self.inputs["im_shape"],
+                scale_factor=self.inputs["scale_factor"],
+                infer_shape=self.inputs["image"].shape[2:],
+            )
+
+            if not isinstance(post_outs, (tuple, list)):
+                # if set exclude_post_process, concat([pred_bboxes, pred_scores]) not scaled to origin
+                # export onnx as torch yolo models
+                return post_outs
+            else:
+                if not self.with_mask:
+                    # if set exclude_nms, [pred_bboxes, pred_scores] scaled to origin
+                    bbox, bbox_num = post_outs  # default for end-to-end eval/infer
+                    output = {"bbox": bbox, "bbox_num": bbox_num}
+                else:
+                    bbox, bbox_num, mask = (
+                        post_outs  # default for end-to-end eval/infer
+                    )
+                    output = {"bbox": bbox, "bbox_num": bbox_num, "mask": mask}
+                    # Note: YOLOv8 Ins models don't support exclude_post_process or exclude_nms
+                return output
+
+    def get_loss(self):
+        return self._forward()
+
+    def get_pred(self):
+        return self._forward()

ppdet/modeling/backbones/__init__.py

@@ -29,6 +29,7 @@
 from . import vit_mae
 from . import hgnet_v2
 from . import yolov10_csp_darknet
+from . import yolo11_csp_darknet
 
 from .resnet import *
 from .darknet import *
@@ -47,3 +48,5 @@
 from .vit_mae import *
 from .hgnet_v2 import *
 from .yolov10_csp_darknet import *
+from .yolo11_csp_darknet import *
+