Update README.md

README.md

@@ -1,127 +1,33 @@
----
-license: cc-by-nc-sa-4.0
----
-
----
-
----
-
-
-
-
-
-
-# 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')
-```
-
+# 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.
+
+- **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)
+
+
+# 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')
+```
+