mirror of https://github.com/open-mmlab/mmyolo.git
Fix copypaste in yolov5-ins l/x config (#756)
* [Fix] fix copypaste in yolov5-ins l/x config * fix * format * update * update --------- Co-authored-by: huanghaian <huanghaian@sensetime.com>pull/778/head
JosonChan
GitHub
parent
887d3ddfdc
commit
e8203150f5
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@ -61,11 +61,13 @@ YOLOv5-l-P6 model structure
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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**Note**:
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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.
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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.
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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.
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### VOC
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@ -1,8 +1,18 @@
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_base_ = './yolov5_ins_m-v61_syncbn_fast_8xb16-300e_coco_instance.py' # noqa
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# This config use refining bbox and `YOLOv5CopyPaste`.
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# Refining bbox means refining bbox by mask while loading annotations and
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# transforming after `YOLOv5RandomAffine`
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# ========================modified parameters======================
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deepen_factor = 1.0
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widen_factor = 1.0
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mixup_prob = 0.1
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copypaste_prob = 0.1
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# =======================Unmodified in most cases==================
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img_scale = _base_.img_scale
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model = dict(
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backbone=dict(
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deepen_factor=deepen_factor,
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@ -13,3 +23,59 @@ model = dict(
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widen_factor=widen_factor,
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),
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bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
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pre_transform = _base_.pre_transform
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albu_train_transforms = _base_.albu_train_transforms
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mosaic_affine_pipeline = [
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dict(
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type='Mosaic',
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img_scale=img_scale,
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pad_val=114.0,
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pre_transform=pre_transform),
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dict(type='YOLOv5CopyPaste', prob=copypaste_prob),
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dict(
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type='YOLOv5RandomAffine',
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max_rotate_degree=0.0,
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max_shear_degree=0.0,
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scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
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border=(-img_scale[0] // 2, -img_scale[1] // 2),
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border_val=(114, 114, 114),
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min_area_ratio=_base_.min_area_ratio,
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max_aspect_ratio=_base_.max_aspect_ratio,
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use_mask_refine=_base_.use_mask2refine),
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]
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# enable mixup
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train_pipeline = [
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*pre_transform,
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*mosaic_affine_pipeline,
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dict(
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type='YOLOv5MixUp',
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prob=mixup_prob,
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pre_transform=[*pre_transform, *mosaic_affine_pipeline]),
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# TODO: support mask transform in albu
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# Geometric transformations are not supported in albu now.
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dict(
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type='mmdet.Albu',
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transforms=albu_train_transforms,
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bbox_params=dict(
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type='BboxParams',
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format='pascal_voc',
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label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
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keymap={
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'img': 'image',
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'gt_bboxes': 'bboxes'
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}),
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dict(type='YOLOv5HSVRandomAug'),
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dict(type='mmdet.RandomFlip', prob=0.5),
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dict(
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type='Polygon2Mask',
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downsample_ratio=_base_.downsample_ratio,
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mask_overlap=_base_.mask_overlap),
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dict(
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type='PackDetInputs',
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meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
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'flip_direction'))
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]
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train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
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@ -4,9 +4,10 @@ _base_ = './yolov5_ins_s-v61_syncbn_fast_8xb16-300e_coco_instance.py' # noqa
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deepen_factor = 0.67
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widen_factor = 0.75
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lr_factor = 0.1
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affine_scale = 0.9
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loss_cls_weight = 0.3
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loss_obj_weight = 0.7
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affine_scale = 0.9
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mixup_prob = 0.1
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# =======================Unmodified in most cases==================
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@ -43,8 +44,8 @@ mosaic_affine_pipeline = [
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type='YOLOv5RandomAffine',
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max_rotate_degree=0.0,
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max_shear_degree=0.0,
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scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
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border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
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scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
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border=(-img_scale[0] // 2, -img_scale[1] // 2),
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border_val=(114, 114, 114),
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min_area_ratio=_base_.min_area_ratio,
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max_aspect_ratio=_base_.max_aspect_ratio,
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@ -88,7 +88,7 @@ train_pipeline = [
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label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
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keymap={
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'img': 'image',
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'gt_bboxes': 'bboxes',
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'gt_bboxes': 'bboxes'
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}),
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dict(type='YOLOv5HSVRandomAug'),
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dict(type='mmdet.RandomFlip', prob=0.5),
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@ -1,4 +1,4 @@
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_base_ = './yolov5_ins_m-v61_syncbn_fast_8xb16-300e_coco_instance.py' # noqa
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_base_ = './yolov5_ins_l-v61_syncbn_fast_8xb16-300e_coco_instance.py' # noqa
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deepen_factor = 1.33
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widen_factor = 1.25
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@ -296,9 +296,9 @@ Models:
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Metrics:
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mask AP: 32.1
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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
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- Name: yolov5_ins_m-v61_syncbn_fast=_8xb16-300e_coco_instance
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- Name: yolov5_ins_m-v61_syncbn_fast_8xb16-300e_coco_instance
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In Collection: YOLOv5
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Config: configs/yolov5/ins_seg/yolov5_ins_m-v61_syncbn_fast=_8xb16-300e_coco_instance.py
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Config: configs/yolov5/ins_seg/yolov5_ins_m-v61_syncbn_fast_8xb16-300e_coco_instance.py
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Metadata:
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Training Memory (GB): 7.3
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Epochs: 300
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@ -312,3 +312,35 @@ Models:
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Metrics:
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mask AP: 37.3
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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
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- Name: yolov5_ins_l-v61_syncbn_fast_8xb16-300e_coco_instance
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In Collection: YOLOv5
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Config: configs/yolov5/ins_seg/yolov5_ins_l-v61_syncbn_fast_8xb16-300e_coco_instance.py
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Metadata:
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Training Memory (GB): 10.7
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Epochs: 300
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Results:
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- Task: Object Detection
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Dataset: COCO
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Metrics:
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box AP: 48.8
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- Task: Instance Segmentation
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Dataset: COCO
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Metrics:
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mask AP: 39.9
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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
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- Name: yolov5_ins_x-v61_syncbn_fast_8xb16-300e_coco_instance
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In Collection: YOLOv5
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Config: configs/yolov5/ins_seg/yolov5_ins_x-v61_syncbn_fast_8xb16-300e_coco_instance.py
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Metadata:
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Training Memory (GB): 15.0
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Epochs: 300
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Results:
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- Task: Object Detection
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Dataset: COCO
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Metrics:
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box AP: 50.6
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- Task: Instance Segmentation
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Dataset: COCO
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Metrics:
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mask AP: 41.4
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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
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