Questions tagged [faster-rcnn]
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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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What are the architectures that CAN use Vision Transformers as a backbone for Object Detection?
I just want to know that how can I fine tune Vision Transformer like DiT for object detection on my custom data and classes?
I want to train a model for Document ...
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How is ResNet different from FPN?
I'm learning more about different variations of deep CNNs. Based from my understanding, ResNet makes use of skip connections that's also somehow shaped like a pyramid or triangle? How is this ...
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masked image and language modelling
I was coding this piece of code which heavily relies on the demo of visual question answering, and I'm masking inputs while feeding it to the bert using [MASK] token, and providing a label which ...
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Can I use pre-trained coco weights for medical lesion segmentation? Or should I train the network from scratch?
I am trying to understand in what cases I can benefit from pre-trained weights. Sometimes, pre-trained weights works (can be fine-tuned) for other domains but I cannot understand how to make a ...
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When using a model like VGG16 as a classifier within Faster RCNN, does Faster RCNN then use 2 CNNs in total?
Im currently doing a project about CNN's but im quite confused because they can be used to classify and to extract features. According to the Faster RCNN paper, it uses a ResNet backbone. I have also ...
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Difference between RRPN and R2CNN
I have been working on rotated object detection with Faster R-CNN on aerial imagery for some time and encountered with two different approaches for producing rotated bounding boxes. The first approach ...
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How many model parameters do R-CNN, Fast R-CNN and Faster R-CNN have respectively?
I am making research on object detection and I would like to know the number of parameters these three models obtain.
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Which model is used for document extraction (CamScanner, Microsoft Lens etc)
I want to start a small project where I'd create a model(s) that would extract document from a picture and rescale it, something like CamScanner or Microsoft Lens apps do.
I've gathered a small ...
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How to convert horizontal bounding box coordinates to oriented bounding box coordinates
I am trying to detect oriented bounding boxes with faster rcnn for a long time, but I could not make it to do so. I aim to detect objects in the DOTA dataset. I was using built-in faster rcnn model in ...
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Understanding how anchors are created in a regional proposal network
I understand that in Faster R-CNN, the image is fed into a pre-trained CNN (such as VG16). So say I have a 37x50x512 feature map. Firstly, I assume that each feature map (37x50x1) is fed into the RPN? ...
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Best way to train yolov5 on a custom dataset
I have a dataset with about 100 images that look like this.
My goal is get yolov5 to detect buildings in similar images.
In order to do this I would like yolov5 to get to close to 1 in precision on ...
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Count repeating "objects" in a picture
This is my first data-science project and I would love to get some guidance to know how to get started.
My problem is the following:
I want to count objects that are in a picture. This picture has a ...
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What is the difference between a bounding box and ROI (Region of Interest)
I was reading about the Fast RCNN for object detection. From what I understand, it uses pre-computed ROI's (using selective search) and uses these to predict the bounding box offsets and uses smooth ...
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How to interpret fast-rcnn metrics?
I'm following this tutorial to fine tune Faster RCNN model, during training process a lot of statistics are produced however I don't know how to interpret them.
what are major characteristics to look ...
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Faster RCNN is not able to recognize composed elements
I tried to fine tune Faster RCNN to realize object detection task with bounding boxes where I shall recognize face, eyes and mouth on image. However Faster RCNN seems to fail to recognize objects when ...
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Region Proposal Network - How to subsampling 256 fg/bg anchors
I am trying to understand training process of RPN. I have problem with creating mini batches of 256 anchors. If features map has shape 18x25=450 and every position has 9 anchors it is 4050 potential ...
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Preprocess problem Faster RCNN in tensorflow object detection API
I've wrapped meta-architecture with the code below:
...
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157
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Training Object Detection model on just 10 images
I am trying to train an object detection model using Mask-RCNN with Resnet50 as backbone. I am using the pre-trained models from PyTorch's Torchvision library. I have only 10 images that I can use to ...
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Why does Faster R-CNN use SGD optimizer instead of Adam?
I just start learning Faster R-CNN and I have some doubts about the optimizer of this network. In my understanding, Adam optimizer performs much better than SGD in a lot of networks. However, the ...
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Faster RCNN-RPN NETWORK
I already asked this question in stack overflow, but got response from experts, to post this question here, please help me to understand this concept...
I am trying to understand RPN network in Faster ...
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How can I calculate the F1 score using Mask RCNN?
I customized the "https://github.com/matterport/Mask_RCNN.git" repository to train with my own data set, for object detection, ignoring the mask segmentation part. Now I am evaluating my results, I ...
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Detect the presence/absense of simple components from a circuit board using object detection
In this case I have a challenge of inspecting a recently mounted product, using computer vision, to detect the absence of any component that I have to check.
For this task, I tried the concept of ...
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How to create a feature vector given final set of feature maps?
I've got a faster-rcnn (resnet-101 backbone) for object detection, and am extracting feature tensors for each detected object, which is a 7x7x2048 tensor (basically 2048 feature maps, each 7x7). For ...
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Efficient implementation of seperable convolution in tensorflow
It seems like the native implementation of separable convolution in tensorflow is not efficient.
https://github.com/tensorflow/tensorflow/issues/12940
Is anyone aware how can we get an efficient ...
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How many augmentated data points for each training image?
What are some useful rules of thumb for picking the number of augmenters per training image?
I realize this is a hyperparameter I can vary and test: I'm just trying to get a sense for reasonable ...
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Image masking tool to implement mask RCNN using python
I am trying to understand Mask RCNN. For that I have to input image with mask in png format while building the model.
I try to follow the article present in this blog.
The blogger used Pixel ...
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How does a Faster R-CNN gets trained and how does the Region Proposal Network fits into that?
so a Faster R-CNN consists of 3 parts:
-the Base CNN
-the Region Proposal Network with RoIpooling
-and the classification CNN at the end
So I asked myself if all these three Networks are trained ...
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Pre trained dataset for Car damage detection
I'm making a Car Damage Detection model which would have 2 classes to detect upon. My dataset has a total of 300 images (out of which I'd be using some for testing), which are totally insufficient to ...
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How does R-CNN and AlexNet compare?
I know AlexNet does object classification in images [categories] and R-CNN does object localization [category and bounding box].
How does R-CNN and AlexNet compare?
Are they used for the same purpose ...
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Saving images in a non-retraceable way, but still able to train R-CNN's on them
For a computer vision project I am working with images that the company only allows me to have on my computer for a maximum of 24 hours due to regulations. Every day a few hundred images come in via ...
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Object detection model using images which have single instance of class per image-can it detect multiple instances in a single image?
I tried to train Faster R-CNN to detect multiple instances of a single class (eg. pomegranate on a pomegranate tree) but the training data consisted of 200 images where each image had only one object ...
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Extract segment from document scan
I need to extract some "valuable" information from document scan. For example, document's number, incoming date, organizations, persons, etc.
Example document:
I'm trying to extract highlighted ...
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Is Faster RCNN the same thing as VGG-16, RESNET-50, etc... or not?
My understanding is that Faster RCNN is an architecture for performing object detection. It finds objects in an image and classifies them. My understanding is also that VGG-16, RESNET-50, etc... also ...
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Tensorflow F-RCNN first stage input and output strides
Trying to optimize performances on Tensorflow's faster_rcnn_resnet50 (from the model zoo), I'm currently working on understanding the full .config file they provide, and I'm having a hard time with ...
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matterport mask-r-cnn transfer learning on own dataset using VGG annotator ver.1
The code I used was taken from shapes.py, balloon.py, inspect_balloon_data.ipynb but mostly ...
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Feeding 3 consecutive video frames to a CNN to track a tennis ball
I want to use CNN transfer learning to track a tennis ball from TV broadcasts of tennis matches.
I used VGG annotating tool annotation tool link (use version 1 of the tool for compatibility with ...
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Understanding Faster R-CNN
I'm having some trouble understanding the way the Faster R-CNN algorithm works. Specifically, the way the authors describe the concept of anchors.
In their paper from here they describe anchors in ...
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Bounding box regression in R-CNN
In R-CNN paper, they give the definition of the target values for bounding box regression
Given that $(P, G)$ is a (prediction box, ground-truth box) pair of the form $(x, y, w, h)$ where $x, y$ is ...
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Building Image Dataset In a Studio
I'm currently working in a problem of Object Detection, more specifically we want to count and differentiate similar species of moths.
We are already testing some ...
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what is the best approach to detect small objects with similar shape?
I'm working a model which detect different products in supermarket shelf. In the training data, there are a lot of objects with similar shape placed very close to or stacked to each others.(eg: milks ...
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Mask RCNN detecting object but mask is inaccurate
I am trying to detect the inner region of a object. Currently I am using the mask rcnn implementation provided by tensorflow in the models zoo.
The object is similar to a hula hoop that is square in ...
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Mask RCNN: Random predictions during inference for the same image
I recently trained the Mask RCNN (matterport's implementation) on some satellite images, but during inference mode, I'm getting random predictions for the same set of weights for the same image.
That ...
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with tf.device(DEVICE): model = modellib.MaskRCNN(mode = "inference", model_dir = LOGS_DIR, config = config) [closed]
ValueError Traceback (most recent call last)
/miniconda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
509 as_ref=...
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Get bounding boxes for adjacent instances of a single class in image
I have a dataset with thousands of music score pages and manually annotated bounding boxes for the individual bars:
My objective is now to train a DNN that should ultimately be able to get these ...
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Query about Landmark pooling layer in Fashionnet paper
I have gone through Deepfashion paper.I have query regarding Landmark pooling layer.
1) Image has 4,6 or 8 landmark points depending on cloth type. So how do you decide size of last FC layer in ...
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Faster R-CNN wrapper for the number of RPNs in the layer dimensions?
When I intialize Faster R-CNN in the deployment phase, the number of samples per image (parameter from config file: TEST.RPN_POST_NMS_TOP_N) is set to 300, that's the number of predicted bounding ...
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How does the bounding box regressor work in Fast R-CNN?
In the fast R-CNN paper (https://arxiv.org/abs/1504.08083) by Ross Girshick, the bounding box parameters are continuous variables. These values are predicted using regression method. Unlike other ...
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Backpropagation in Faster R-CNN
I understand how the convolution layers are applied after selective search finds the regions of interest in vanilla R-CNN and so the back-propagation or any weight updating is done in the individual ...
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