Release OpenImages model

detection/configs/_base_/datasets/openimages_detection.py

@@ -35,7 +35,7 @@ data = dict(
     train=dict(
         type=dataset_type,
         ann_file=data_root + 'annotations/oidv6-train-annotations-bbox.csv',
-        img_prefix=data_root + 'OpenImages/train/',
+        img_prefix=data_root + 'train/',
         label_file=data_root + 'annotations/class-descriptions-boxable.csv',
         hierarchy_file=data_root +
                        'annotations/bbox_labels_600_hierarchy.json',
@@ -43,7 +43,7 @@ data = dict(
     val=dict(
         type=dataset_type,
         ann_file=data_root + 'annotations/validation-annotations-bbox.csv',
-        img_prefix=data_root + 'OpenImages/validation/',
+        img_prefix=data_root + 'validation/',
         label_file=data_root + 'annotations/class-descriptions-boxable.csv',
         hierarchy_file=data_root +
         'annotations/bbox_labels_600_hierarchy.json',
@@ -54,7 +54,7 @@ data = dict(
     test=dict(
         type=dataset_type,
         ann_file=data_root + 'annotations/validation-annotations-bbox.csv',
-        img_prefix=data_root + 'OpenImages/validation/',
+        img_prefix=data_root + 'validation/',
         label_file=data_root + 'annotations/class-descriptions-boxable.csv',
         hierarchy_file=data_root +
         'annotations/bbox_labels_600_hierarchy.json',

detection/configs/openimages/README.md

+# OpenImages
+
+## Introduction
+
+OpenImages V6 is a large-scale dataset , consists of 9 million training images, 41,620 validation samples, and 125,456 test samples. It is a partially annotated dataset, with 9,600 trainable classes.
+
+## Model Zoo
+
+### DINO + CB-InternImage
+
+|     backbone     |  pretrain  | mAP (ss) | #param |                               Config                                |                                                        Download                                                         |
+| :--------------: | :--------: | :------: | :----: | :-----------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------: |
+| CB-InternImage-H | Objects365 |   74.1   | 2.18B  | [config](./dino_4scale_cbinternimage_h_objects365_openimages_ss.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/dino_4scale_cbinternimage_h_objects365_openimages.pth) |

detection/configs/openimages/dino_4scale_cbinternimage_h_objects365_openimages_ss.py

+_base_ = [
+    '../_base_/datasets/openimages_detection.py',
+    '../_base_/default_runtime.py'
+]
+load_from = 'https://huggingface.co/OpenGVLab/InternImage/resolve/main/dino_4scale_cbinternimage_h_objects365_80classes.pth'
+model = dict(
+    type='CBDINO',
+    backbone=dict(
+        type='CBInternImage',
+        core_op='DCNv3',
+        channels=320,
+        depths=[6, 6, 32, 6],
+        groups=[10, 20, 40, 80],
+        mlp_ratio=4.,
+        drop_path_rate=0.5,
+        norm_layer='LN',
+        layer_scale=None,
+        offset_scale=1.0,
+        post_norm=False,
+        dw_kernel_size=5,  # for InternImage-H/G
+        res_post_norm=True,  # for InternImage-H/G
+        level2_post_norm=True,  # for InternImage-H/G
+        level2_post_norm_block_ids=[5, 11, 17, 23, 29],  # for InternImage-H/G
+        center_feature_scale=True,  # for InternImage-H/G
+        with_cp=True,
+        out_indices=[(0, 1, 2, 3), (1, 2, 3)],
+        init_cfg=None,
+    ),
+    neck=[dict(
+        type='CBChannelMapper',
+        in_channels=[640, 1280, 2560],
+        kernel_size=1,
+        out_channels=256,
+        act_cfg=None,
+        norm_cfg=dict(type='GN', num_groups=32),
+        num_outs=4)],
+    bbox_head=dict(
+        type='CBDINOHead',
+        num_query=900,
+        num_classes=601,
+        in_channels=2048,  # TODO
+        sync_cls_avg_factor=True,
+        as_two_stage=True,
+        with_box_refine=True,
+        dn_cfg=dict(
+            type='CdnQueryGenerator',
+            noise_scale=dict(label=0.5, box=1.0),  # 0.5, 0.4 for DN-DETR
+            group_cfg=dict(dynamic=True, num_groups=None, num_dn_queries=1000)),
+        transformer=dict(
+            type='DinoTransformer',
+            two_stage_num_proposals=900,
+            encoder=dict(
+                type='DetrTransformerEncoder',
+                num_layers=6,
+                transformerlayers=dict(
+                    type='BaseTransformerLayer',
+                    attn_cfgs=dict(
+                        type='MultiScaleDeformableAttention',
+                        embed_dims=256,
+                        dropout=0.0),  # 0.1 for DeformDETR
+                    feedforward_channels=2048,  # 1024 for DeformDETR
+                    ffn_cfgs=dict(
+                        type='FFN',
+                        embed_dims=256,
+                        feedforward_channels=2048,
+                        num_fcs=2,
+                        ffn_drop=0.,
+                        use_checkpoint=True,
+                        act_cfg=dict(type='ReLU', inplace=True),),
+                    ffn_dropout=0.0,  # 0.1 for DeformDETR
+                    operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
+            decoder=dict(
+                type='DinoTransformerDecoder',
+                num_layers=6,
+                return_intermediate=True,
+                transformerlayers=dict(
+                    type='DetrTransformerDecoderLayer',
+                    attn_cfgs=[
+                        dict(
+                            type='MultiheadAttention',
+                            embed_dims=256,
+                            num_heads=8,
+                            dropout=0.0),  # 0.1 for DeformDETR
+                        dict(
+                            type='MultiScaleDeformableAttention',
+                            num_levels=4,
+                            embed_dims=256,
+                            dropout=0.0),  # 0.1 for DeformDETR
+                    ],
+                    feedforward_channels=2048,  # 1024 for DeformDETR
+                    ffn_cfgs=dict(
+                        type='FFN',
+                        embed_dims=256,
+                        feedforward_channels=2048,
+                        num_fcs=2,
+                        ffn_drop=0.,
+                        use_checkpoint=True,
+                        act_cfg=dict(type='ReLU', inplace=True),),
+                    ffn_dropout=0.0,  # 0.1 for DeformDETR
+                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
+                                     'ffn', 'norm')))),
+        positional_encoding=dict(
+            type='SinePositionalEncoding',
+            num_feats=128,
+            temperature=20,
+            normalize=True),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),  # 2.0 in DeformDETR
+        loss_bbox=dict(type='L1Loss', loss_weight=5.0),
+        loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='HungarianAssigner',
+            cls_cost=dict(type='FocalLossCost', weight=2.0),
+            reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
+            iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0)),
+        snip_cfg=dict(
+            type='v3',
+            weight=0.1)),
+    test_cfg=dict(max_per_img=300))  # TODO: Originally 100
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
+# from the default setting in mmdet.
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, denorm_bbox=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Resize',
+         img_scale=[(2000, 600), (2000, 1200)],
+         multiscale_mode='range',
+         keep_ratio=True),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2000, 1000),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=1,
+    workers_per_gpu=2,
+    train_dataloader=dict(class_aware_sampler=dict(num_sample_class=1)),
+    train=dict(filter_empty_gt=True, pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(
+    type='AdamW', lr=0.0001, weight_decay=0.0001,
+    constructor='CustomLayerDecayOptimizerConstructor',
+    paramwise_cfg=dict(num_layers=50, layer_decay_rate=0.94,
+                       depths=[6, 6, 32, 6], offset_lr_scale=1e-3))
+optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[])
+runner = dict(type='IterBasedRunner', max_iters=100000)
+checkpoint_config = dict(interval=2000, max_keep_ckpts=3)
+evaluation = dict(
+    interval=2000, metric='mAP', save_best='auto')