Questions tagged [r-cnn]
For questions related to the family of models known as R-CNN (such as the original R-CNN model, fast R-CNN, faster R-CNN and mask R-CNN).
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How to handle multiple object instances in object detection?
I’m constructing a neural net with Keras for object detection for identifying hamburgers. I have a data set with the objects and each image has an array of bounding boxes (there are between 1 and 5 ...
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How are OCR training datasets constructed?
For the sake of concreteness: let's suppose that the word "OCR" refers to any OCR system build on an R-CNN architecture. Similarly, in aims of simplicity, let's declare that we are ...
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Darknet as a part of Yolo v3
I am pretty new to ML and my question may look strange. Especially the last part of it.
1)As far as I understand Darknet53 is an integral part of Yolo just as Resnet50 is a part of R-CNN Am I right?
2)...
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Why are the learned offsets of anchor boxes in the RCNN object detection models scale invariant?
In the original RCNN paper (https://arxiv.org/pdf/1311.2524.pdf) and continued in later RCNN papers such as faster RCNN (https://arxiv.org/pdf/1506.01497.pdf) the learned offsets of the anchor boxes ...
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What to do when the ROIs are smaller than $227 \times 227$ in R-CNN?
As English is not my native language, I have some hard time understanding the following sentence:
Regardless of the size or aspect ratio of the candidate region, we warp all pixels in a tight ...
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Is intersection of labels acceptable in computer vision?
I have a dataset, where objects are very close to each other. So, the question is: what is the best approach to label them?
There are two possible options:
mark objects so that they will not ...
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Is it possible to pre-train a CNN in a self-supervised way so that it can later be used to solve an instance segmentation task?
I would like to use self-supervised learning (SSL) to learn features from images (the dataset consists of similar images with small differences), then use the resulting trained model to bootstrap an ...
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How is the data labelled in order to train a region proposal network?
I don't get how the training of the RPN works. From the forward propagation, I have $W \times H \times k$ outputs from the RPN.
How is the training data labeled such that I can use the loss function ...
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Does the selective search algorithm in object detection learn?
I am trying to get a better grasp of how object detection works. I (almost) completely understand the concept behind RPNs. However, I am a little bit confused with the selective search algorithm part. ...
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Inaccurate masks with Mask-RCNN: Stairs effect and sudden stops
I've been using matterport's Mask R-CNN to train on a custom dataset. However, there seem to be some parameters that i failed to correctly define because on practically all of the images, the bottom ...
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Why are RNNs used in some computer vision problems?
I am learning computer vision. When I was going through implementations of various computer vision projects, some OCR problems used GRU or LSTM, while some did not. I understand that RNNs are used ...
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How does the region proposal method work in Fast R-CNN?
I read so many articles and the Fast R-CNN paper, but I'm still confused about how the region proposal method works in Fast R-CNN.
As you can see in the image below, they say they used a proposal ...
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In Fast R-CNN, how are input RoIs mapped to the respective RoIs in the feature map before RoI pooling?
I've been reading the Fast R-CNN paper.
My understanding is that the input to one forward pass is the whole input image plus a list of RoIs (generated by selective search or another region proposal ...
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In Faster R-CNN, how can I get the predicted bounding box given the neural network's output?
The RPN loss in Faster RCNN paper is
$$
L({p_i}, {t_i}) = \frac{1}{N_{cls}} \sum_{i} L_{cls}(p_i,p_i^*) + \lambda \frac{1}{N_{reg}} \sum_i p_i^* L_{reg}(t_i, t_i^*)
$$
For regression problems, we have ...
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