Questions tagged [image-recognition]

For questions related to image recognition in the context of AI.

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CNN image classification

While training CNN with a fully connected layer for image classification, isn't training everything at once the problem? For example, we want to classify dogs. Somehow in the first epoch feature ...
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How to optimize input image tensor, to best fit image classifier, for specific target class?

Assume that we got pretrained image classifier, and we want to then optimize some input image tensor for it, that for given class, the output of classifier will match. How to do this for example in ...
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How can I reduce the loss? Why do I have the high loss and why do I have the gradient?

I want to classify some images (there are about 200.000 images) with a CNN. But I get a very high loss, see figures: Loss over the hole training run Loss for each epoch It's confused me, that there ...
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For an image (of any object), how to find its location in the other image(s) which contains it, given there are no labels or annotations for any image

Problem Statement: I am given 2 sets of images. All the images in both sets are without annotations and labels. First set : a set of images of the grocery store shelves (captured in the grocery stores)...
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How would you fine tune the model on images of larger size?

Let's say you have trained a model for image classification, on images of a defined size (H x W x Ch). How would you fine tune the model on images of larger size?
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How do I deal with a dataset of Images with variable sizes (width and height) when doing Image Classification?

I have a dataset in which the images which don't have the same width and height. How do I perform Image Classification with such images? I am trying as much as possible to steer away from image ...
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Image Recognition Method, calculate deviation from rectangular grid

I have a set-up which creates pictures of a grid that is a bit bend towards the ends, and I need some kind of program that can calculate the deviation, resp. it just needs to be some kind of indicator,...
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How to convert prediction probabilities of 2D images (initially 3D image) to 3D image predictions?

Classification: binary Model: CNN (ResNet50V2) During our research we've had 91x109x91 images (3-dimensional). We've used 2D CNN to train and evaluate our images and make predictions on labelled cases,...
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Does Using the Same Background for Binary Classification Improve Model Accuracy?

I am training a CNN that detects if a there is a pot of boiling water vs if there is a pot of boiling water with pasta inside. My hypothesis is that having the same background for both a positive and ...
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Is AI able to detect major changes in pairs of images while ignoring minor changes (due to tree crown growth, color and perspectiv disstortions)?

I'm starting to get involved into machine learning but still have some troubles to select the approriate tool or algorithm. My basic task is to compare remotly sensed images of individual trees at two ...
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Musical notes interpretation [closed]

Musical notes Musical notes videos Piano Can AI, Machine learning, Data science, Computer vision, image processing technologies assist in interpreting musical notes ? Input dataset : Musical notes ...
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Which is the best model for counting how many people are in an inside car image?

I'm new on AI... I've been given a mission of finding a model which detects how many people there are on a inside car/truck image. So far I worked only with YOLOv4 with OpenCV using yolov4 weights. ...
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Detecting cheats visually using AI

I really like to play my favorite 3D shooter game online. Unfortunately, it is really old and cheat protection isn't really common there, but cheaters are! It is very frustrating, because it really ...
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What's the best model to use for CNN(deep learning) regression task for small image dataset?

What are the best Deep learning models(with how many layers) to use in a regression task for a custom dataset containing around 100 images of only one object per image which is more or less ...
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Validity of ImageNet for measurement of the model performance

ImageNet dataset is an established benchmark for the measurement of the performance of CV models. ImageNet involves 1000 categories and the goal of the classification model is to output the correct ...
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How to improve accuracy of CNN used for facial micro-expression analysis

From the paper (1) Facial expression analysis using CNN, the results using CNN show a 65% and 62% accuracy respectively, for emotion classification and state of mind identification. Proposed Method: ...
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What exactly do gradient-based saliency map tell us?

As far as I understand, gradients are supposed to tell us 1) the magnitude and 2) direction, to update a parameter such as to minimize the loss function. Regarding saliency maps, which use gradients ...
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Comparing large numbers of images to find outliers

There are many methods you can use to compare two images in ML (Siamese NN, CNNs, Ect.) What I cannot figure out is comparing a large number of images (Without Retraining) to find images of a ...
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Encoding Image Priors into CNN

There's a core problem with all of ML which I haven't really seen made explicit: the issue is every model needs to have an assumption on the structure of the data you learn and this assumption needs ...
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Why am I getting a very small number as CNN prediction?

I created a CNN using Tensorflow to identify pneumonia and sometimes it returns a very small number as a prediction. why is this happening? I have attached the link for the dataset Here I how I ...
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For a task that searches for an image artifact within a picture, can existing tools can be used or do I need to design the process myself?

I am familiar only with basic AI/NN concepts but never worked with any libraries/tools as tensor flow. Currently, I have a task for which AI might be ideal: detection of a certain image artifact in a ...
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Multiple labels for the same rectbox?

My goal is to identify the horse in a photo. I'm dealing with about 500 unique horses. My feeling is that the best way to distinguish one horse from another is by its face. So I trained Yolov5 ...
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Can I flip a video to generate more data for action recognition?

There are 8 distinct action classes and around 50+ videos per class. I was wondering if flipping videos from the training set can be a good option to generate additional data. Is it?
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1 answer
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How do automatic high-beam headlights work on cars?

Modern cars can operate high-beam headlights automatically: They automatically switch from high-beam headlights to low-beam ones (less intense) when you enter a town or there is a car in front of you ...
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What sort of out-of-the-box technology could be used to create work similar to artist Refik Anadol?

Refik Anadol has machines view actual pictures and then has the machine create its own images. This video shows some of the stuff he does. What kind of out-of-the-box tools (e.g. a Python package) or ...
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What is the purpose of hard distillation?

In order to get a smaller model, one often uses larger model, that performs reasonably well on the data as a teacher, and uses the information from large model to train the smaller one. There are ...
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Without using data augmentation gives results better than using data augmentation

I am a beginner to deep learning, I'm doing the image classification problem on a small self plant disease imaging dataset (400 images). I am doing transfer learning (pre-trained ...
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How to increase accuracy of image orientation classification (Left, Right, Center)?

I am working on classifying images in "Left", "Right", "Center", "Back". Training and Validation images look like this: The images are "Left", "...
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Is having near-duplicates in a training dataset a bad thing?

I am making a labeled dataset of images from web streams for a CNN classification. Pictures from the same stream are quite similar as far as background, but slightly different as far as the main ...
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Image recognition neural network: scaling and rotation

Are there some effective and robust solutions for scaling and rotation for image recognition with the neural networks (NN)? I see tons of sources on the Web with explanation how neural network is used ...
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Machine Learning Algorithm for OCR on full pages of text

I would like to build an OCR application. In. particular, I want my algorithm to scan entire pages of text in a specific niche language. I was therefore wondering if there are some algorithms that ...
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Is soft labeling the same thing as label smoothing?

I have some data with soft labels and I am trying to figure out the best approach to solve the problem with Machine Learning (since regular classification is of the table, i.e. hard labels). However, ...
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How to localize and classify objects in video

What methods are used to localize an object in an video and classify that object? Example: I have a camera which detects an pickup truck driving into a garage of three (1,2,3). In need to know if the ...
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2 votes
2 answers
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How to classify two very similar images using Deep Learning?

I am a newbie in Computer Vision. I have a scenario in which I have a stationary camera in a factory. I want to detect whether the technician is working on the machine or not. Images are like the ...
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1 answer
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Should I label static objects on video dataset?

I'm using nvidia Transfer Learning Toolkit to detect cars in some video frames. I found some dataset (for example https://www.jpjodoin.com/urbantracker/dataset.html and https://www.kaggle.com/...
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What does the lambda parameter in the paper "Interpretable Explanations of Black Boxes by Meaningful Perturbation" do?

I do not understand the purpose of the $\lambda$ parameter in equation 3 of the paper Interpretable Explanations of Black Boxes by Meaningful Perturbation. $$m^{*}=\underset{m \in[0,1]^{\Lambda}}{\...
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when updating the bias matrix, do we get the total sum of dZ or the sum of the axis of dZ?

I'm currently studying how to implement a neural network from scratch to know how it works, I came across this article: https://www.samsonzhang.com/2020/11/24/understanding-the-math-behind-neural-...
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Is it possible to create a simple face-tracking app that can monitor how much time one spends at their desk?

Context: I'm an experienced programmer with a graduate education in AI and previous CUDA programming experience. I'm versed in Machine Learning but am out of the loop -- I've not used any of the ...
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How to deal with images that do not contain any object of interest?

I'm currently working on an iOS App where I want to detect if there is a table, chair or bench in the current camera input. My idea was to take the MobileNetV2 model and get it to classify these three ...
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DeepLabV3: Why use global average pooling in the ASPP module?

I'm trying to understand the rationale of the various modifications the authors of the DeepLab models have made to their third version, DeepLabV3. In the paper, the following is written: ASPP with ...
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Is it possible to train a perceptron to tell if a picture is a dog or cat?

I know perceptron is a linear classifier that tells linearly separable binary class data, such as iris setosa vs. iris versicolor via their sepal's length and width. I'd just like to know if I have 2 ...
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Document clustering from ordered pages list

I have a series of ordered pdf pages which own to different documents. Let me give you an example: Pages: 1 2 3 4 5 6 True Pages: 1 2 | 1 2 3 4 So I have like six ordered pages, two of which from ...
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Number of classes vs number of parameters/layers?

How to estimate the number of parameters in CNN for object detection? I know that there are some well-known architectures that was trained on a lot of data (AlexNet, ResNet, VGG, GoogleLeNet). But ...
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How can my CNN produce an "unknown" label?

I have a dataset of 20k images of infected mango. I have built a web-based app using Flask, where a user can upload a picture, and my CNN model detects the disease. I have 6 classes in the model, ...
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1 answer
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Cnn for Combination of both digits and letters(small and capital) [closed]

Hi I am new to machine learning can anyone suggest open dataset consists of both digits and letters(small,capital) I want images consisisting of both digits and letters to train my cnn model and make ...
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2 votes
1 answer
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How to properly use Flatten layer?

Context I'm trying to create net that will be able to recognize printed-like digits. Something like MNIST, but only for standard printing font. Images are of the size 40x40 and I'd like to put them ...
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What algorithm to use to classify data by spatial relations?

Let's assume I have dataset of image-like 2D samples where values can be divided into few discrete levels (for example 1, 2, 3 and 4) like in the image below, where each color maps different value, ...
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How can I train a model to recognize object with zoomed-in image?

Humans are good at guessing animals with zoomed-in images from patterns of fur/skin. (For example, if we saw a black-white pattern fur, it must be a zebra) I have some experience guessing a car model ...
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2 answers
89 views

Accuracy Not Going Above 30%

I am trying to make a big classification model using the coco2017 dataset. Here is my code: ...
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1 answer
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Should one use an "other" category in image classification?

In image classification, there are sometimes images that do not fit in any category. For example, if I build a CNN in Keras to classify Dogs and Cats, does it help (in terms of training time and ...
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