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What does the notation mAP@[.5:.95] mean?

mAP@[.5:.95](someone denoted mAP@[.5,.95]) means average mAP over different IoU thresholds, from 0.5 to 0.95, step 0.05 (0.5, 0....
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What is the difference between semantic segmentation, object detection and instance segmentation?

Object Detection : is the technology that is related to computer vision and image processing. Its aim? detect objects in an image. Semantic Segmentation : is a technique that detects , for each pixel ,...
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How to calculate mAP for detection task for the PASCAL VOC Challenge?

To answer your questions: Yes your approach is right Of A, B and C the right answer is B. The explanation is the following: In order to calculate Mean Average Precision (mAP) in the context of ...
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What is the difference between Inception v2 and Inception v3?

In the paper Batch Normalization,Sergey et al,2015. proposed Inception-v1 architecture which is a variant of the GoogleNet in the paper Going deeper with convolutions, and in the meanwhile they ...
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Why convolutions always use odd-numbers as filter size

The convolution operation, simply put, is combination of element-wise product of two matrices. So long as these two matrices agree in dimensions, there shouldn't be a problem, and so I can understand ...
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number of parameters for convolution layers

Actually it's $49C*C$, the first $C$ is the number of input channels, and the second $C$ is the number of filters. Quote from CS231n: To summarize, the Conv Layer: Accepts a volume of size ...
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How to calculate mAP for detection task for the PASCAL VOC Challenge?

There is a nice and detailed explanation with an easy to use code on my Github. Certainly it will help you guys.
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What is fractionally-strided convolution layer?

Here is an animation of fractionally-strided convolution (from this github project): where the dashed white cells are zero rows/columns padded between the input cells (blue). These animations are ...
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Optimizer for Convolutional neural network

Yes, you can use the same optimizers you are familiar with for CNNs. I don't think that there is a best optimizer for CNNs. The most popular in my opinion is Adam. However some people like to use a ...
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Class token in ViT and BERT

My question is — why does this token exist as input in all the transformer blocks and is treated the same as the word / patches tokens? The transformers, by default are sequence to sequence networks. ...
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How to deal with large training data?

I do something similar with keras and GPU training, where i also have only a small memory amount available. The idea would be split the numpy files into smaller ones, let's say 64 samples per file and ...
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How to implement global contrast normalization in python?

there are multiple issues with the code: You force the values in the image to be uint8 (8-bit integer). Since the values are floats they will be casted/rounded to either 0 or 1. This will later be ...
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Using Neural Networks to extract multiple parameters from images

A CNN could be a good choice for this task if you expect variation in the original image scale, rotation lighting etc, and also have a lot of training data. The usual CNN architecture is to have ...
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What is the difference between Dilated Convolution and Deconvolution?

In sort of mechanistic/pictorial/image-based terms: Dilation: ### SEE COMMENTS, WORKING ON CORRECTING THIS SECTION Dilation is largely the same as run-of-the-mill convolution (frankly so is ...
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What is the meaning of hand crafted features in computer vision problems?

"Hand Crafted" features refer to properties derived using various algorithms using the information present in the image itself. For example, two simple features that can be extracted from images are ...
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What is difference between Fully Connected layer and Bilinear layer in CNN?

I quote the answers from What is a bilinear tensor layer (in contrast to a standard linear neural network layer) or how can I imagine it?. A bilinear function is a function of two inputs $x$ and $y$ ...
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Does image's background matter for detector training (CNN)?

Of course, it matters. Which one is best completely depends on your problem. The golden rule for machine learning problems is that you want the data you train on to be as representative as the data ...
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How to make two parallel convolutional neural networks in Keras?

You essentially need a multi-input model. This can only be done through keras' functional api and can work with the pretrained nets in keras.applications. To create ...
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How can you build a model that extracts data out from receipts?

The simplest pipeline would be to do the following: OCR Named Entity Extraction Entity Disambiguation OCR This is basically transforming your receipts into plain text. If you have scans (pictures) ...
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Convnet training error does not decrease

In your convnet code, you compute the cross entropy manually: ...
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What is the difference between Dilated Convolution and Deconvolution?

Though both seem to be doing the same thing, which is up-sampling a layer, there's a clear margin between them. First we talk about Dilated Convolution I found this nice blog on above topic. So as ...
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Why do we need for Shortcut Connections to build Residual Networks?

The short answer is that when a net is very deep it becomes very difficult for gradients to propagate backwards all the way. Skip connections offer "short cuts" for gradients to propagate further and ...
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The goal of fine tuning

Fine tuning means changing the weights such that the VGGNet can perform the task you want in your dataset. The reason why fine-tuning is not called training (which is what you are doing) is because it ...
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