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Moreover, ,Mask R-CNN, is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, ,Mask R-CNN, outperforms all existing ...
Understanding ,Mask R-CNN Mask R-CNN, is an extension of Faster ,R-CNN,. Faster ,R-CNN, is widely used for object detection tasks. For a given image, it returns the class label and bounding box coordinates for each object in the image. So, let’s say you pass the following image: The Fast ,R-CNN, model will return something like this:
My ,Mask R-CNN, implementation uses the COCO Dataset and computes mAP. Is it ,fair, to compare my mAP values with their published IoU values directly ? mAP used in ,Mask R-CNN, is only computed for predictions with IoU threshold of >= 0.7. Thus, would it even be ,fair, to compare with the other authors' IoU values at all ?
R-CNN, is the predecessor to the present existing and most happening architectures such as Faster ,RCNN, and ,Mask RCNN,. Last year, ,FAIR, (Facebook AI Research) developed a fully functional framework called the Detectron2 which was built upon these state-of-the-art architectures, Faster ,R-CNN,, and ,Mask R-CNN,.
The ,Mask R-CNN, authors at Facebook AI Research (,FAIR,) extended Faster ,R-CNN, to perform instance segmentation, along with the class and bounding box. Instance segmentation is a combination of object detection and semantic segmentation, which means that it performs both detection of all objects in an image, and segmentation of each instance while differentiating it from the rest of the instances.
This method is built on ,Mask R-CNN,, because it is a simple instance segmentation model that also achieves state-of-the-art results. In ,Mask R-CNN,, the last layer in the bounding box branch and the last layer in the ,mask, branch both contain category-specific parameters that are used to perform bounding box classification and instance ,mask, prediction, respectively, for each category.
Faster ,R-CNN, takes close to 0.2 seconds for every test image during inference and is about 250 times faster than Fast ,R-CNN,. Your Social Distancing Tool – A Use Case of Object Detection & Tracking Social Distancing is the only way to prevent the spread of COVID-19 right now.
This is the final step in ,Mask R-CNN, where we predict the ,masks, for all the objects in the image. Keep in mind that the training time for ,Mask R-CNN, is quite high. It took me somewhere around 1 to 2 days to train the ,Mask R-CNN, on the famous COCO dataset. So, for the scope of this article, we will not be training our own ,Mask R-CNN, model.
We model a keypoint’s location as a one-hot,,mask,, and adopt ,Mask R-CNN, to predict,K,,masks,, one for,each of,K,keypoint types (,e.g,., left shoulder, right elbow).,This task helps demonstrate the flexibility of ,Mask R-CNN,.,We note that,minimal,domain knowledge for human pose,is exploited by our system, as the experiments are mainly to,demonstrate the generality of the ,Mask R-CNN, framework.,We ...
Improvements Over Faster ,R-CNN,. Predict segmentation ,masks, on each ROI together with doing bbox classification and regression. Propose a quantization-free layer, RoIAlign, to solve the disability of faster ,R-CNN, to do pixel-to-pixel alignment, which is able to improve ,mask, accuracy by relative 10% to 50%. ($\leftarrow$ I feel this is the key ...