Fix copypaste in yolov5-ins l/x config

configs/yolov5/README.md

@@ -61,11 +61,13 @@ YOLOv5-l-P6 model structure
 |       YOLOv5-s        |  P5  | 640  |  Yes   | Yes |   4.8    |  38.1  |  32.0   |       [config](./ins_seg/yolov5_ins_s-v61_syncbn_fast_8xb16-300e_coco_instance.py)       |                         [model](https://download.openmmlab.com/mmyolo/v0/yolov5/ins_seg/yolov5_ins_s-v61_syncbn_fast_8xb16-300e_coco_instance/yolov5_ins_s-v61_syncbn_fast_8xb16-300e_coco_instance_20230426_012542-3e570436.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/ins_seg/yolov5_ins_s-v61_syncbn_fast_8xb16-300e_coco_instance/yolov5_ins_s-v61_syncbn_fast_8xb16-300e_coco_instance_20230426_012542.log.json)                         |
 | YOLOv5-s(non-overlap) |  P5  | 640  |  Yes   | Yes |   4.8    |  38.0  |  32.1   | [config](./ins_seg/yolov5_ins_s-v61_syncbn_fast_non_overlap_8xb16-300e_coco_instance.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/ins_seg/yolov5_ins_s-v61_syncbn_fast_non_overlap_8xb16-300e_coco_instance/yolov5_ins_s-v61_syncbn_fast_non_overlap_8xb16-300e_coco_instance_20230424_104642-6780d34e.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/ins_seg/yolov5_ins_s-v61_syncbn_fast_non_overlap_8xb16-300e_coco_instance/yolov5_ins_s-v61_syncbn_fast_non_overlap_8xb16-300e_coco_instance_20230424_104642.log.json) |
 |       YOLOv5-m        |  P5  | 640  |  Yes   | Yes |   7.3    |  45.1  |  37.3   |       [config](./ins_seg/yolov5_ins_m-v61_syncbn_fast_8xb16-300e_coco_instance.py)       |                         [model](https://download.openmmlab.com/mmyolo/v0/yolov5/ins_seg/yolov5_ins_m-v61_syncbn_fast_8xb16-300e_coco_instance/yolov5_ins_m-v61_syncbn_fast_8xb16-300e_coco_instance_20230424_111529-ef5ba1a9.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/ins_seg/yolov5_ins_m-v61_syncbn_fast_8xb16-300e_coco_instance/yolov5_ins_m-v61_syncbn_fast_8xb16-300e_coco_instance_20230424_111529.log.json)                         |
+|       YOLOv5-l        |  P5  | 640  |  Yes   | Yes |   10.7   |  48.8  |  39.9   |       [config](./ins_seg/yolov5_ins_l-v61_syncbn_fast_8xb16-300e_coco_instance.py)       |                         [model](https://download.openmmlab.com/mmyolo/v0/yolov5/ins_seg/yolov5_ins_l-v61_syncbn_fast_8xb16-300e_coco_instance/yolov5_ins_l-v61_syncbn_fast_8xb16-300e_coco_instance_20230508_104049-daa09f70.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/ins_seg/yolov5_ins_l-v61_syncbn_fast_8xb16-300e_coco_instance/yolov5_ins_l-v61_syncbn_fast_8xb16-300e_coco_instance_20230508_104049.log.json)                         |
+|       YOLOv5-x        |  P5  | 640  |  Yes   | Yes |   15.0   |  50.6  |  41.4   |       [config](./ins_seg/yolov5_ins_x-v61_syncbn_fast_8xb16-300e_coco_instance.py)       |                         [model](https://download.openmmlab.com/mmyolo/v0/yolov5/ins_seg/yolov5_ins_x-v61_syncbn_fast_8xb16-300e_coco_instance/yolov5_ins_x-v61_syncbn_fast_8xb16-300e_coco_instance_20230508_103925-a260c798.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/ins_seg/yolov5_ins_x-v61_syncbn_fast_8xb16-300e_coco_instance/yolov5_ins_x-v61_syncbn_fast_8xb16-300e_coco_instance_20230508_103925.log.json)                         |
 
 **Note**:
 
 1. `Non-overlap` refers to the instance-level masks being stored in the format (num_instances, h, w) instead of (h, w). Storing masks in overlap format consumes less memory and GPU memory.
-2. We found that the mAP of the N/S/M model is higher than the official version, but the L/X model is lower than the official version. We will resolve this issue as soon as possible.
+2. For the M model, the `affine_scale` parameter should be 0.9, but due to some reason, we set it to 0.5 and found that the mAP did not change. Therefore, the released M model has an `affine_scale` parameter of 0.5, which is inconsistent with the value of 0.9 in the configuration.
 
 ### VOC
 

configs/yolov5/ins_seg/yolov5_ins_l-v61_syncbn_fast_8xb16-300e_coco_instance.py

@@ -1,8 +1,18 @@
 _base_ = './yolov5_ins_m-v61_syncbn_fast_8xb16-300e_coco_instance.py'  # noqa
 
+# This config use refining bbox and `YOLOv5CopyPaste`.
+# Refining bbox means refining bbox by mask while loading annotations and
+# transforming after `YOLOv5RandomAffine`
+# ========================modified parameters======================
 deepen_factor = 1.0
 widen_factor = 1.0
 
+mixup_prob = 0.1
+copypaste_prob = 0.1
+
+# =======================Unmodified in most cases==================
+img_scale = _base_.img_scale
+
 model = dict(
     backbone=dict(
         deepen_factor=deepen_factor,
@@ -13,3 +23,59 @@
         widen_factor=widen_factor,
     ),
     bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
+
+pre_transform = _base_.pre_transform
+albu_train_transforms = _base_.albu_train_transforms
+mosaic_affine_pipeline = [
+    dict(
+        type='Mosaic',
+        img_scale=img_scale,
+        pad_val=114.0,
+        pre_transform=pre_transform),
+    dict(type='YOLOv5CopyPaste', prob=copypaste_prob),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
+        border=(-img_scale[0] // 2, -img_scale[1] // 2),
+        border_val=(114, 114, 114),
+        min_area_ratio=_base_.min_area_ratio,
+        max_aspect_ratio=_base_.max_aspect_ratio,
+        use_mask_refine=_base_.use_mask2refine),
+]
+
+# enable mixup
+train_pipeline = [
+    *pre_transform,
+    *mosaic_affine_pipeline,
+    dict(
+        type='YOLOv5MixUp',
+        prob=mixup_prob,
+        pre_transform=[*pre_transform, *mosaic_affine_pipeline]),
+    # TODO: support mask transform in albu
+    # Geometric transformations are not supported in albu now.
+    dict(
+        type='mmdet.Albu',
+        transforms=albu_train_transforms,
+        bbox_params=dict(
+            type='BboxParams',
+            format='pascal_voc',
+            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
+        keymap={
+            'img': 'image',
+            'gt_bboxes': 'bboxes'
+        }),
+    dict(type='YOLOv5HSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(
+        type='Polygon2Mask',
+        downsample_ratio=_base_.downsample_ratio,
+        mask_overlap=_base_.mask_overlap),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                   'flip_direction'))
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))

configs/yolov5/ins_seg/yolov5_ins_m-v61_syncbn_fast_8xb16-300e_coco_instance.py

@@ -4,9 +4,10 @@
 deepen_factor = 0.67
 widen_factor = 0.75
 lr_factor = 0.1
-affine_scale = 0.9
 loss_cls_weight = 0.3
 loss_obj_weight = 0.7
+
+affine_scale = 0.9
 mixup_prob = 0.1
 
 # =======================Unmodified in most cases==================
@@ -43,8 +44,8 @@
         type='YOLOv5RandomAffine',
         max_rotate_degree=0.0,
         max_shear_degree=0.0,
-        scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
-        border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
+        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
+        border=(-img_scale[0] // 2, -img_scale[1] // 2),
         border_val=(114, 114, 114),
         min_area_ratio=_base_.min_area_ratio,
         max_aspect_ratio=_base_.max_aspect_ratio,

configs/yolov5/ins_seg/yolov5_ins_s-v61_syncbn_fast_8xb16-300e_coco_instance.py

@@ -88,7 +88,7 @@
             label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
         keymap={
             'img': 'image',
-            'gt_bboxes': 'bboxes',
+            'gt_bboxes': 'bboxes'
         }),
     dict(type='YOLOv5HSVRandomAug'),
     dict(type='mmdet.RandomFlip', prob=0.5),

configs/yolov5/ins_seg/yolov5_ins_x-v61_syncbn_fast_8xb16-300e_coco_instance.py

@@ -1,4 +1,4 @@
-_base_ = './yolov5_ins_m-v61_syncbn_fast_8xb16-300e_coco_instance.py'  # noqa
+_base_ = './yolov5_ins_l-v61_syncbn_fast_8xb16-300e_coco_instance.py'  # noqa
 
 deepen_factor = 1.33
 widen_factor = 1.25

configs/yolov5/metafile.yml

@@ -296,9 +296,9 @@ Models:
         Metrics:
           mask AP: 32.1
     Weights: https://download.openmmlab.com/mmyolo/v0/yolov5/ins_seg/yolov5_ins_s-v61_syncbn_fast_non_overlap_8xb16-300e_coco_instance/yolov5_ins_s-v61_syncbn_fast_non_overlap_8xb16-300e_coco_instance_20230424_104642-6780d34e.pth
-  - Name: yolov5_ins_m-v61_syncbn_fast=_8xb16-300e_coco_instance
+  - Name: yolov5_ins_m-v61_syncbn_fast_8xb16-300e_coco_instance
     In Collection: YOLOv5
-    Config: configs/yolov5/ins_seg/yolov5_ins_m-v61_syncbn_fast=_8xb16-300e_coco_instance.py
+    Config: configs/yolov5/ins_seg/yolov5_ins_m-v61_syncbn_fast_8xb16-300e_coco_instance.py
     Metadata:
       Training Memory (GB): 7.3
       Epochs: 300
@@ -312,3 +312,35 @@ Models:
         Metrics:
           mask AP: 37.3
     Weights: https://download.openmmlab.com/mmyolo/v0/yolov5/ins_seg/yolov5_ins_m-v61_syncbn_fast_8xb16-300e_coco_instance/yolov5_ins_m-v61_syncbn_fast_8xb16-300e_coco_instance_20230424_111529-ef5ba1a9.pth
+  - Name: yolov5_ins_l-v61_syncbn_fast_8xb16-300e_coco_instance
+    In Collection: YOLOv5
+    Config: configs/yolov5/ins_seg/yolov5_ins_l-v61_syncbn_fast_8xb16-300e_coco_instance.py
+    Metadata:
+      Training Memory (GB): 10.7
+      Epochs: 300
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 48.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.9
+    Weights: https://download.openmmlab.com/mmyolo/v0/yolov5/ins_seg/yolov5_ins_l-v61_syncbn_fast_8xb16-300e_coco_instance/yolov5_ins_l-v61_syncbn_fast_8xb16-300e_coco_instance_20230508_104049-daa09f70.pth
+  - Name: yolov5_ins_x-v61_syncbn_fast_8xb16-300e_coco_instance
+    In Collection: YOLOv5
+    Config: configs/yolov5/ins_seg/yolov5_ins_x-v61_syncbn_fast_8xb16-300e_coco_instance.py
+    Metadata:
+      Training Memory (GB): 15.0
+      Epochs: 300
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 50.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 41.4
+    Weights: https://download.openmmlab.com/mmyolo/v0/yolov5/ins_seg/yolov5_ins_x-v61_syncbn_fast_8xb16-300e_coco_instance/yolov5_ins_x-v61_syncbn_fast_8xb16-300e_coco_instance_20230508_103925-a260c798.pth