[Feature] Support Objects365 Dataset

.dev_scripts/gather_models.py

@@ -141,7 +141,9 @@ def get_dataset_name(config):
         VOCDataset='Pascal VOC',
         WIDERFaceDataset='WIDER Face',
         OpenImagesDataset='OpenImagesDataset',
-        OpenImagesChallengeDataset='OpenImagesChallengeDataset')
+        OpenImagesChallengeDataset='OpenImagesChallengeDataset',
+        Objects365V1Dataset='Objects365 v1',
+        Objects365V2Dataset='Objects365 v2')
     cfg = mmcv.Config.fromfile('./configs/' + config)
     return name_map[cfg.dataset_type]
 

configs/_base_/datasets/objects365v1_detection.py

@@ -0,0 +1,49 @@
+# dataset settings
+dataset_type = 'Objects365V1Dataset'
+data_root = 'data/Objects365/Obj365_v1/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    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=(1333, 800),
+        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=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/objects365_train.json',
+        img_prefix=data_root + 'train/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/objects365_val.json',
+        img_prefix=data_root + 'val/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/objects365_val.json',
+        img_prefix=data_root + 'val/',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='bbox')

configs/_base_/datasets/objects365v2_detection.py

@@ -0,0 +1,49 @@
+# dataset settings
+dataset_type = 'Objects365V2Dataset'
+data_root = 'data/Objects365/Obj365_v2/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    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=(1333, 800),
+        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=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/zhiyuan_objv2_train.json',
+        img_prefix=data_root + 'train/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/zhiyuan_objv2_val.json',
+        img_prefix=data_root + 'val/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/zhiyuan_objv2_val.json',
+        img_prefix=data_root + 'val/',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='bbox')

configs/objects365/README.md

@@ -0,0 +1,91 @@
+# Objects365 Dataset
+
+> [Objects365 Dataset](https://openaccess.thecvf.com/content_ICCV_2019/papers/Shao_Objects365_A_Large-Scale_High-Quality_Dataset_for_Object_Detection_ICCV_2019_paper.pdf)
+
+<!-- [DATASET] -->
+
+## Abstract
+
+<!-- [ABSTRACT] -->
+
+#### Objects365 Dataset V1
+
+[Objects365 Dataset V1](https://www.objects365.org/overview.html) is a brand new dataset,
+designed to spur object detection research with a focus on diverse objects in the Wild.
+It has 365 object categories over 600K training images. More than 10 million, high-quality bounding boxes are manually labeled through a three-step, carefully designed annotation pipeline. It is the largest object detection dataset (with full annotation) so far and establishes a more challenging benchmark for the community. Objects365 can serve as a better feature learning dataset for localization-sensitive tasks like object detection
+and semantic segmentation.
+
+<!-- [IMAGE] -->
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/48282753/208368046-b7573022-06c9-4a99-af17-a6ac7407e3d8.png" height="400"/>
+</div>
+
+#### Objects365 Dataset V2
+
+[Objects365 Dataset V2](https://www.objects365.org/overview.html) is based on the V1 release of the Objects365 dataset.
+Objects 365 annotated 365 object classes on more than 1800k images, with more than 29 million bounding boxes in the training set, surpassing PASCAL VOC, ImageNet, and COCO datast.
+Objects 365 includes 11 categories of people, clothing, living room, bathroom, kitchen, office/medical, electrical appliances, transportation, food, animals, sports/musical instruments, and each category has dozens of subcategories.
+
+## Citation
+
+```
+@inproceedings{shao2019objects365,
+  title={Objects365: A large-scale, high-quality dataset for object detection},
+  author={Shao, Shuai and Li, Zeming and Zhang, Tianyuan and Peng, Chao and Yu, Gang and Zhang, Xiangyu and Li, Jing and Sun, Jian},
+  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
+  pages={8430--8439},
+  year={2019}
+}
+```
+
+## Prepare Dataset
+
+1. You need to download and extract Objects365 dataset.
+
+2. The directory should be like this:
+
+   ```none
+   mmdetection
+   ├── mmdet
+   ├── tools
+   ├── configs
+   ├── data
+   │   ├── Objects365
+   │   │   ├── Obj365_v1
+   │   │   │   ├── annotations
+   │   │   │   │   ├── objects365_train.json
+   │   │   │   │   ├── objects365_val.json
+   │   │   │   ├── train        # training images
+   │   │   │   ├── val          # validation images
+   │   │   ├── Obj365_v2
+   │   │   │   ├── annotations
+   │   │   │   │   ├── zhiyuan_objv2_train.json
+   │   │   │   │   ├── zhiyuan_objv2_val.json
+   │   │   │   ├── train        # training images
+   │   │   │   │   ├── patch0
+   │   │   │   │   ├── patch1
+   │   │   │   │   ├── ...
+   │   │   │   ├── val          # validation images
+   │   │   │   │   ├── patch0
+   │   │   │   │   ├── patch1
+   │   │   │   │   ├── ...
+   ```
+
+## Results and Models
+
+### Objects365 V1
+
+| Architecture | Backbone |  Style  | Lr schd | Mem (GB) | box AP |                                                             Config                                                              |                                                                                                                                                                                Download                                                                                                                                                                                |
+| :----------: | :------: | :-----: | :-----: | :------: | :----: | :-----------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| Faster R-CNN |   R-50   | pytorch |   1x    |    -     |  19.6  |   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v1.py)    |           [model](https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v1/faster_rcnn_r50_fpn_16x4_1x_obj365v1_20221219_181226-9ff10f95.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v1/faster_rcnn_r50_fpn_16x4_1x_obj365v1_20221219_181226.log.json)           |
+| Faster R-CNN |   R-50   | pytorch |  1350K  |    -     |  22.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/objects365/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1_20220510_142457-337d8965.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1_20220510_142457.log.json) |
+|  Retinanet   |   R-50   | pytorch |   1x    |    -     |  14.8  |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/objects365/retinanet_r50_fpn_1x_obj365v1.py)       |                         [model](https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_1x_obj365v1/retinanet_r50_fpn_1x_obj365v1_20221219_181859-ba3e3dd5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_1x_obj365v1/retinanet_r50_fpn_1x_obj365v1_20221219_181859.log.json)                         |
+|  Retinanet   |   R-50   | pytorch |  1350K  |    -     |  18.0  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/objects365/retinanet_r50_fpn_syncbn_1350k_obj365v1.py)  |     [model](https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_syncbn_1350k_obj365v1/retinanet_r50_fpn_syncbn_1350k_obj365v1_20220513_111237-7517c576.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_syncbn_1350k_obj365v1/retinanet_r50_fpn_syncbn_1350k_obj365v1_20220513_111237.log.json)     |
+
+### Objects365 V2
+
+| Architecture | Backbone |  Style  | Lr schd | Mem (GB) | box AP |                                                           Config                                                           |                                                                                                                                                                      Download                                                                                                                                                                      |
+| :----------: | :------: | :-----: | :-----: | :------: | :----: | :------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| Faster R-CNN |   R-50   | pytorch |   1x    |    -     |  19.8  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v2.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v2/faster_rcnn_r50_fpn_16x4_1x_obj365v2_20221220_175040-5910b015.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v2/faster_rcnn_r50_fpn_16x4_1x_obj365v2_20221220_175040.log.json) |
+|  Retinanet   |   R-50   | pytorch |   1x    |    -     |  16.7  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/objects365/retinanet_r50_fpn_1x_obj365v2.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_1x_obj365v2/retinanet_r50_fpn_1x_obj365v2_20221223_122105-d9b191f1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_1x_obj365v2/retinanet_r50_fpn_1x_obj365v2_20221223_122105.log.json)               |

configs/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v1.py

@@ -0,0 +1,25 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/objects365v1_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(roi_head=dict(bbox_head=dict(num_classes=365)))
+
+data = dict(samples_per_gpu=4)
+
+# Using 32 GPUS while training
+optimizer = dict(type='SGD', lr=0.08, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=1.0 / 1000,
+    step=[8, 11])
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (16 GPUs) x (4 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)

configs/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v2.py

@@ -0,0 +1,25 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/objects365v2_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(roi_head=dict(bbox_head=dict(num_classes=365)))
+
+data = dict(samples_per_gpu=4)
+
+# Using 32 GPUS while training
+optimizer = dict(type='SGD', lr=0.08, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=1.0 / 1000,
+    step=[8, 11])
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (16 GPUs) x (4 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)

configs/objects365/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1.py

@@ -0,0 +1,31 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/objects365v1_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    backbone=dict(norm_cfg=norm_cfg),
+    roi_head=dict(bbox_head=dict(num_classes=365)))
+
+# Using 8 GPUS while training
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+
+runner = dict(
+    _delete_=True, type='IterBasedRunner', max_iters=1350000)  # 36 epochs
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=1.0 / 1000,
+    step=[900000, 1200000])
+
+checkpoint_config = dict(interval=150000)
+evaluation = dict(interval=150000, metric='bbox')
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)

configs/objects365/metafile.yml

@@ -0,0 +1,101 @@
+- Name: retinanet_r50_fpn_1x_obj365v1
+  In Collection: RetinaNet
+  Config: configs/objects365/retinanet_r50_fpn_1x_obj365v1.py
+  Metadata:
+    Training Memory (GB): 7.4
+    Epochs: 12
+    Training Data: Objects365 v1
+    Training Techniques:
+      - SGD with Momentum
+      - Weight Decay
+  Results:
+  - Task: Object Detection
+    Dataset: Objects365 v1
+    Metrics:
+      box AP: 14.8
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_1x_obj365v1/retinanet_r50_fpn_1x_obj365v1_20221219_181859-ba3e3dd5.pth
+
+- Name: retinanet_r50_fpn_syncbn_1350k_obj365v1
+  In Collection: RetinaNet
+  Config: configs/objects365/retinanet_r50_fpn_syncbn_1350k_obj365v1.py
+  Metadata:
+    Training Memory (GB): 7.6
+    Iterations: 1350000
+    Training Data: Objects365 v1
+    Training Techniques:
+      - SGD with Momentum
+      - Weight Decay
+  Results:
+  - Task: Object Detection
+    Dataset: Objects365 v1
+    Metrics:
+      box AP: 18.0
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_syncbn_1350k_obj365v1/retinanet_r50_fpn_syncbn_1350k_obj365v1_20220513_111237-7517c576.pth
+
+- Name: retinanet_r50_fpn_1x_obj365v2
+  In Collection: RetinaNet
+  Config: configs/objects365/retinanet_r50_fpn_1x_obj365v2.py
+  Metadata:
+    Training Memory (GB): 7.2
+    Epochs: 12
+    Training Data: Objects365 v2
+    Training Techniques:
+      - SGD with Momentum
+      - Weight Decay
+  Results:
+  - Task: Object Detection
+    Dataset: Objects365 v2
+    Metrics:
+      box AP: 16.7
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_1x_obj365v2/retinanet_r50_fpn_1x_obj365v2_20221223_122105-d9b191f1.pth
+
+- Name: faster_rcnn_r50_fpn_16x4_1x_obj365v1
+  In Collection: Faster R-CNN
+  Config: configs/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v1.py
+  Metadata:
+    Training Memory (GB): 11.4
+    Epochs: 12
+    Training Data: Objects365 v1
+    Training Techniques:
+      - SGD with Momentum
+      - Weight Decay
+  Results:
+  - Task: Object Detection
+    Dataset: Objects365 v1
+    Metrics:
+      box AP: 19.6
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v1/faster_rcnn_r50_fpn_16x4_1x_obj365v1_20221219_181226-9ff10f95.pth
+
+- Name: faster_rcnn_r50_fpn_syncbn_1350k_obj365v1
+  In Collection: Faster R-CNN
+  Config: configs/objects365/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1.py
+  Metadata:
+    Training Memory (GB): 8.6
+    Iterations: 1350000
+    Training Data: Objects365 v1
+    Training Techniques:
+      - SGD with Momentum
+      - Weight Decay
+  Results:
+  - Task: Object Detection
+    Dataset: Objects365 v1
+    Metrics:
+      box AP: 22.3
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1_20220510_142457-337d8965.pth
+
+- Name: faster_rcnn_r50_fpn_16x4_1x_obj365v2
+  In Collection: Faster R-CNN
+  Config: configs/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v2.py
+  Metadata:
+    Training Memory (GB): 10.8
+    Epochs: 12
+    Training Data: Objects365 v1
+    Training Techniques:
+      - SGD with Momentum
+      - Weight Decay
+  Results:
+  - Task: Object Detection
+    Dataset: Objects365 v2
+    Metrics:
+      box AP: 19.8
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v2/faster_rcnn_r50_fpn_16x4_1x_obj365v2_20221220_175040-5910b015.pth