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README.md

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 license: cc-by-nc-sa-4.0
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+
+---
+
+---
+
+
+
+
+
+
+# Model Card for Oriented R-CNN pretrained on DOTA 1.0
+
+<!-- Provide a quick summary of what the model is/does. [Optional] -->
+The original paper is [Oriented R-CNN for Object Detection](https://openaccess.thecvf.com/content/ICCV2021/papers/Xie_Oriented_R-CNN_for_Object_Detection_ICCV_2021_paper.pdf). 
+
+This implementation of this model has been developed by [OpenMMLab](https://openmmlab.com/) in the [MMRotate](https://github.com/open-mmlab/mmrotate) framework.
+
+The model has been trained on [DOTA 1.0](https://captain-whu.github.io/DOTA/)
+
+The performance measured as mAP is 75.69.
+
+
+
+
+#  Table of Contents
+
+- [Model Card for Oriented R-CNN pretrained on DOTA 1.0](#model-card-for--model_id-)
+- [Table of Contents](#table-of-contents)
+- [Model Details](#model-details)
+  - [Model Description](#model-description)
+- [Uses](#uses)
+  - [Direct Use](#direct-use)
+  - [Out-of-Scope Use](#out-of-scope-use)
+- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
+  - [Recommendations](#recommendations)
+- [Training Details](#training-details)
+  - [Training Data](#training-data)
+  - [Metrics](#metrics)
+  - [Results](#results)
+- [Model Card Contact](#model-card-contact)
+- [How to Get Started with the Model](#how-to-get-started-with-the-model)
+
+
+# Model Details
+
+## Model Description
+
+<!-- Provide a longer summary of what this model is/does. -->
+The original paper is [Oriented R-CNN for Object Detection](https://openaccess.thecvf.com/content/ICCV2021/papers/Xie_Oriented_R-CNN_for_Object_Detection_ICCV_2021_paper.pdf). 
+
+This implementation of this model has been developed by [OpenMMLab](https://openmmlab.com/) in the [MMRotate](https://github.com/open-mmlab/mmrotate) framework.
+
+The model has been trained on [DOTA 1.0](https://captain-whu.github.io/DOTA/)
+
+The performance measured as mAP is 75.69.
+
+- **Developed by:** OpenMMLab
+- **Model type:** Object Detection model
+- **License:** cc-by-nc-sa-4.0
+- **Resources for more information:** More information needed
+    - [GitHub Repo](https://github.com/open-mmlab/mmrotate/)
+    - [Associated Paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Xie_Oriented_R-CNN_for_Object_Detection_ICCV_2021_paper.pdf)
+
+# Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+## Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
+
+
+## Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
+
+
+# Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
+
+
+# Training Details
+
+## Training Data
+
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+The model has been trained on [DOTA 1.0](https://captain-whu.github.io/DOTA/)
+
+
+## Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+The performance is measured as mAP.
+
+## Results 
+
+The final mAP is 75.69.
+
+
+# Model Card Contact
+
+Jeff Faudi
+
+# How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```
+from mmdet.apis import init_detector, inference_detector
+import mmrotate
+
+config_file = 'oriented_rcnn_r50_fpn_1x_dota_le90.py'
+checkpoint_file = 'oriented_rcnn_r50_fpn_1x_dota_le90-6d2b2ce0.pth'
+model = init_detector(config_file, checkpoint_file, device='cuda:0')
+inference_detector(model, 'demo/demo.jpg')
+```
+

oriented_rcnn_r50_fpn_1x_dota_le90-6d2b2ce0.pth

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oriented_rcnn_r50_fpn_1x_dota_le90.py

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+dataset_type = 'DOTADataset'
+data_root = 'data/split_1024_dota1_0/'
+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='RResize', img_scale=(1024, 1024)),
+    dict(
+        type='RRandomFlip',
+        flip_ratio=[0.25, 0.25, 0.25],
+        direction=['horizontal', 'vertical', 'diagonal'],
+        version='le90'),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    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=(1024, 1024),
+        flip=False,
+        transforms=[
+            dict(type='RResize'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='DOTADataset',
+        ann_file='data/split_1024_dota1_0/trainval/annfiles/',
+        img_prefix='data/split_1024_dota1_0/trainval/images/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(type='RResize', img_scale=(1024, 1024)),
+            dict(
+                type='RRandomFlip',
+                flip_ratio=[0.25, 0.25, 0.25],
+                direction=['horizontal', 'vertical', 'diagonal'],
+                version='le90'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+        ],
+        version='le90'),
+    val=dict(
+        type='DOTADataset',
+        ann_file='data/split_1024_dota1_0/trainval/annfiles/',
+        img_prefix='data/split_1024_dota1_0/trainval/images/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1024, 1024),
+                flip=False,
+                transforms=[
+                    dict(type='RResize'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='DefaultFormatBundle'),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ],
+        version='le90'),
+    test=dict(
+        type='DOTADataset',
+        ann_file='data/split_1024_dota1_0/test/images/',
+        img_prefix='data/split_1024_dota1_0/test/images/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1024, 1024),
+                flip=False,
+                transforms=[
+                    dict(type='RResize'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='DefaultFormatBundle'),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ],
+        version='le90'))
+evaluation = dict(interval=1, metric='mAP')
+optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.3333333333333333,
+    step=[8, 11])
+runner = dict(type='EpochBasedRunner', max_epochs=12)
+checkpoint_config = dict(interval=1)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+angle_version = 'le90'
+model = dict(
+    type='OrientedRCNN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='OrientedRPNHead',
+        in_channels=256,
+        feat_channels=256,
+        version='le90',
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='MidpointOffsetCoder',
+            angle_range='le90',
+            target_means=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            target_stds=[1.0, 1.0, 1.0, 1.0, 0.5, 0.5]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(
+            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),
+    roi_head=dict(
+        type='OrientedStandardRoIHead',
+        bbox_roi_extractor=dict(
+            type='RotatedSingleRoIExtractor',
+            roi_layer=dict(
+                type='RoIAlignRotated',
+                out_size=7,
+                sample_num=2,
+                clockwise=True),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            type='RotatedShared2FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=15,
+            bbox_coder=dict(
+                type='DeltaXYWHAOBBoxCoder',
+                angle_range='le90',
+                norm_factor=None,
+                edge_swap=True,
+                proj_xy=True,
+                target_means=(0.0, 0.0, 0.0, 0.0, 0.0),
+                target_stds=(0.1, 0.1, 0.2, 0.2, 0.1)),
+            reg_class_agnostic=True,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=0,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.8),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=False,
+                iou_calculator=dict(type='RBboxOverlaps2D'),
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RRandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.8),
+            min_bbox_size=0),
+        rcnn=dict(
+            nms_pre=2000,
+            min_bbox_size=0,
+            score_thr=0.05,
+            nms=dict(iou_thr=0.1),
+            max_per_img=2000)))