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 ...
C.J. Windisch's user avatar
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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 ...
Ramiro Hum-Sah's user avatar
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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)...
Igor's user avatar
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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 ( and continued in later RCNN papers such as faster RCNN ( the learned offsets of the anchor boxes ...
phil's user avatar
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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 ...
Valentin's user avatar
2 votes
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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 ...
Valery Noname's user avatar
2 votes
1 answer

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 ...
Timco Vanco's user avatar
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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 ...
Abd El-Rahman Akram's user avatar
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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. ...
Tibo Geysen's user avatar
2 votes
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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 ...
Nawra C's user avatar
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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 ...
Naveen Reddy Marthala's user avatar
6 votes
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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 ...
ozoubia's user avatar
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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 ...
Alexander Soare's user avatar
1 vote
1 answer

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 ...
user31844's user avatar