Create README.md

README.md

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+Hugging Face's logo
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+language: 
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+- multilingual
+
+---
+
+# Mask R-CNN
+
+## Model desription
+
+Mask R-CNN is a model that extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. The model locates pixels of images instead of just bounding boxes as Faster R-CNN was not designed for pixel-to-pixel alignment between network inputs and outputs. 
+
+### More information on the model and dataset:
+
+### The model
+Mask R-CNN works towards the approach of instance segmentation, which involves object detection, and semantic segmentation. For object detection, Mask R-CNN uses an architecture that is similar to Faster R-CNN, while it uses a Fully Convolutional Network(FCN) for semantic segmentation. 
+The FCN is added to the top of features of a Faster R-CNN to generate a mask segmentation output. This segmentation output is in parallel with the classification and bounding box regressor network of the Faster R-CNN model. From the advancement of Fast R-CNN Region of Interest Pooling(ROI), Mask R-CNN adds refinement called ROI aligning by addressing the loss and misalignment of ROI Pooling; the new ROI aligned leads to improved results. 
+
+### Technical Specifications
+
+Please [read the paper](https://arxiv.org/pdf/1703.06870.pdf) for more information on training. 
+
+The model architecture is divided into two parts:
+  - Region proposal network (RPN) to propose candidate object bounding boxes.
+  - Binary mask classifier to generate a mask for every class 
+
+#### Technical Summary.
+-  Mask R-CNN is quite similar to the structure of faster R-CNN. 
+-  Outputs a binary mask for each Region of Interest.
+-  Applies bounding-box classification and regression in parallel, simplifying the original R-CNN's multi-stage pipeline.
+-  The network architectures utilized are called ResNet and ResNeXt. The depth can be either 50 or 101
+
+#### Results Summary
+- Instance Segmentation: Based on the COCO dataset, Mask R-CNN outperforms all categories compared to MNC and FCIS, which are state-of-the-art model.
+- Bounding Box Detection: Mask R-CNN outperforms the base variants of all previous state-of-the-art models, including the COCO 2016 Detection Challenge winner.
+
+
+## Intended uses & limitations 
+
+- With great generality, Mask RCNN can be extended to human pose estimation.
+
+
+
+## Training Procedure
+Please [read the paper](https://arxiv.org/pdf/1703.06870.pdf) for more information on training, or check OpenMMLab [repository](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn)