Questions tagged [image-classification]

For questions about image classification: a decision problem where an algorithm must decide to which class ('cat', 'chair', 'tree') an input image belongs.

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How to classify (supervised) a multi dimensional vector?

What kinds of machine learning tool is used to classify a vector of data which are not spatially correlated? I have a 158*158 image*15000 samples which I tried to ...
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How can I reduce overfitting in CNN model for image classification, even after data augmentation?

its my first time posting here. I'm trying to build a CNN model that identifies fruits from a dataset of apples, bananas, mixed fruits, and oranges. So far, one of the things I have done to prevent ...
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Resnet34 vs ResNet34d

Can someone please explain to me the difference between resnet34 and resnet34d? Is resnet34d simply double a normal resnet34? Also for both, what are the input and output sizes of each layer?
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In a CNN architecture, is it possible to incorporate both class weights and data augmentation?

I'd like to conduct image classification using some CNN architectures, but the problem is that my classes are imbalanced, and each class has insufficient data. To solve this situation, I have a ...
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CutMix VS Mixup Data Augumentation method for end-to-end deep learning Traning

I am looking for arguments on which Data augmentation (Mixup VS CutMix) method would be preferable for Image data and Time-series classification data. As for as I know, both have the following ...
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1answer
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spot/stain growth in image classification problems

I am working on a problem with images where we are monitoring development of spot in certain region of image. We are able to classify spot present(NOK) or not present(OK) successfully if initially ...
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1answer
66 views

Algorithms to do a CTRL+F (find object) on an image

We all know the CTRL+F "Find text..." feature in text editors / browsers. I'd like to study the available algorithms to do something similar on an image. Example of UI/UX: let's say you have ...
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Image Classification: Different Background on Training and Test Set

I am currently working on a challenge for a course at my university. We need to assign the image of a leaf to one of 14 different classes and we are given an unbalanced training set of ~17k pictures (...
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ResNet50 + Transformer

In many papers people extract features from image using ResNet and than pass them through transformer. I want to implement the same. I want to get features and than classify them using transformer. ...
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Using Object Detection or Image Segmentation without labelled input data to build a dataset to then be manually labelled?

I'm looking to build an object detection model or image segmentation model, ideally the latter, which will identify and label objects from satellite imagery but I don't currently have any labelled ...
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Preprocessing for Transfer Learning Model with an Inception Network

I am trying to build an image classification model using an Inception Network as the base. This is a simple binary classification model. My images are available in many smaller directories within one ...
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How can I calculate the level of agreement between my K-Means cluster labels and my ground truth labels in R?

I have made a K-Means clustering from 3 rasters with various values of k (k=2, k=4, k=7) and would like to know which values of k explains the most variance in my ground-truth data or the value of k ...
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1answer
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Robustness vs Generalization

I don't quite understand the difference between robustness and generalisability in relation to image processing (CNN). If my model generalises well, it is also robust to changes in the image material. ...
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1answer
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Is there any difference between classifying images by their type and by the objects they represent?

Let us suppose that I would like to train a machine learning model for classifying images according to their types (for example, photographs and drawings). The techniques that I can use for this would ...
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1answer
17 views

Pre-processing images for fine-tuning

When you are fine-tuning a CNN like ResNet, VGG, EfficientNet, etc and you want to train the model with your own images, or even when you want to do a inference with any image of your dataset, do you ...
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3answers
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100% Accuracy and 0 loss in image classification

I am working on image classification using CNNs and the pretrained model VGG16, my dataset has 3 classes with almost 900 images per class. after traning for 5 epochs my model reached 1 accuracy with 0....
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1answer
32 views

Trained model performs worse on the whole dataset

I used pytorch as the training framework and the official pytorch imagenet example to train the image classification model with my custom dataset. My custom dataset has 2 different label (good and bad)...
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Why concatenating these layers, why applying masks over and over to partial convoluted image?

I have to ask some questions about one topic. In this sentence of Nvidia's article they are saying: "The last partial convolution layer’s input will contain the concatenation of the original ...
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Classification of RGB images

What is the preferred way to specify the features for image classification when the input consists of RGB images? Is it a good approach to flatten the image into a single vector (where for instance '...
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Can i segment/crop out image using image processing?

I have a large dataset of bottles. I want to train a model with this dataset. But before feeding the input images to the model I want to crop out the bottle from the background. Is there a way to do ...
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1answer
167 views

How do you add negative class sample for binary classification?

How do you prepare the negative dataset for binary classification? Let us say that I am building a classifier that has to classify whether the input image is of a car or not. I already have a dataset ...
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30 views

Keras data augmentation for fashion minst data set performs worse than without it

I am trying to build an image classification model for fashion mnist data set. I designed a network and achieved accuracy of ~93%. I wanted to improve it further, so I decided to augment data using <...
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Transfer learning on images with higher dynamic range

Is it possible to fine-tune a CNN-based model previously trained on images with 8 bits depth [0 ~ 2^8] to fit a 16 bits depth [0 ~ 2^16] images? if there is any research paper that confirm that, it ...
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Tensorflow convolutional neural network error during training

I built a simple CNN for binary image classification (cat/dog). ...
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1answer
71 views

Classification of scanned documents in pdf files using deep learning or NLP

I know classifying images using cnn but I have a problem where I have multiple types of scanned documents in a pdf file on different pages. Some types of scanned documents present in multiple pages ...
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5 views

Best smoothing image techniques for digit recognition

There are several ways to smooth an image like - Gaussian Blur Median Blurr etc. as mentioned on the page - https://docs.opencv.org/4.5.2/d4/d13/tutorial_py_filtering.html Suppose I have an image ...
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Can smbd tell why ViT takes ~twice as much time to train?

I'm using a dataset with 300k images (ChestXray). On ViT paper authors claim that ViT is 2-4 times faster in training due to parallelization capability, but in reality it's the opposite. I played with ...
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19 views

Digit recognition images for testing

I made a python program to solve the digit recognition problem that is mentioned here - https://www.kaggle.com/c/digit-recognizer The sample dataset that is given on this website is basically pixel ...
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Forward height/width information into classification model?

Please forgive me if it's not the right StackExchange, but I didn't find any related to computer vision questions. Problem: I have a pipeline for object detection and classification where I first ...
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How to create COCO format data out of list of boxes

I have $N$ images. I have a script that extracts boundary boxes of an object that I am interested in. For each image, I may get $m$ boxes. There is only one item that I am interested in which is cat. ...
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24 views

Encoding technique used in Keras ImageDataGenrator class

I would like to know what encoding is used by ImageDataGenerator for encoding the class labels. I have done a lot of research and found that there is a variable called ...
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How to modify this training function in order to print the aggregation of models

I have 3 VGG: VGGA, VGGB and VGG*, trained with the following training function: ...
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1answer
119 views

Train-test split and augmentation strategy for small dataset for video classification problem

I have a small data set of videos of approximately 100 videos for each class for a binary classification problem. This results in a total of 200 videos. I am applying two types of augmentations on the ...
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how to make image classification model to detect object and track object in a video

i have built a disaster classification model . now i want to use this classification model to detect ex- cyclone in a video to draw bounding box around it and track the cyclone if possible.is it ...
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can i do object detection on pretrained classification model...?

so i built a natural disaster classification model using transfer learning Renet50(tensor flow) got 98% accuracy and now instead of just classifying natural disaster lets say a cyclone appeared in ...
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8 views

Mobile or web app to standardize image collection

Is there an app that I can use to unify / standardize how I take pictures to form a data set? Assuming that the image size, object location are, maybe picture quality are of importance for a CNN ...
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2answers
52 views

Combining CNNs for image classification

I would like to take the output of an intermediate layer of a CNN (layer G) and feed it to an intermediate layer of a wider CNN (layer H) to complete the inference. Challenge: The two layers G, H have ...
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How to extract undetected object class labels after extracting `y_pred` from `predictions. pth` inference file for Mask rcnn

I training maskrcnn on a custom dataset with two classes (1 and 2). After testing, I get some files segm.json, predictions.pth, <...
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1answer
33 views

Underfitting issue [closed]

I have a small datset (530 images) trained on a simple CNN called AquaSight. This is the architecture. I had an underfitting problem, 75% accuracy and 0.6 loss. How can I solve the underfitting ...
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1answer
24 views

SageMaker - mini_batch_size what is it and why can't I get it higher than 5?

I'm following this tutorial but I keep getting the error: "The number of input images must be bigger or equal to the mini_batch_size." I've tried a series ...
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65 views

Imagenet2012-Subset dataset

I want to use imagenet2012-subset. As I understood, I should first download imagenet dataset manually (it is about 150 GB). I thought it is not reasonable. Is there any way to use imagenet2012 subset, ...
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23 views

how to debug model that why that model prediction goes to a particular label for an incorrect prediction?

Let's say I have implemented a model to predict whether the image is dog, cat, bird, elephant. my model predicts the input dog image as a cat how to interpret the model how/why it goes high prediction ...
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19 views

Low accuracy in the image classification model

I am trying to build an image classification model to classify whether Thistle Caterpillar is present in an image or not. The classification is a single label classification. The dataset and the code ...
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18 views

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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362 views

Problem with batching tensors - InvalidArgumentError: Cannot batch tensors with different shapes in component

So, I am trying to build this model for an image classifier using the oxford flower dataset 102, and I am having issues when trying to fit the model. The error says: ...
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25 views

Don't understand Channels in Covolutional Layers [duplicate]

I'm struggling to understand the concept of 'Channels'. What does a channel mean in the context of an image. I understand that a grey scale image only has 1 channel, and a RGB has 3, but then I see ...
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Comparing results of different image splicing methods on a part of the CASIA 2.0 dataset

So I am working on an image splicing detection algorithm using ResNet-50 model. I am using the CASIA 2.0 dataset which consists of 7491 Authentic images and 5123 Fake images. However out of the fake ...
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25 views

Training data for image digit recognition

I am creating a digit image recognition algorithm. For this I would like to collect sample data to train the model. Does python have any inbuilt libraries for this? Where could I get good training ...
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Image Classification using ML and Image processing

I'm doing a project in ML and Image processing where I try to classify cats and dogs! dataset: https://www.kaggle.com/chetankv/dogs-cats-images The models I'm using: KNN, Random Forest, SNM, and ...
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Image classification with CNNs

I created an image classification model using CNNs for 235 classes and I got 71% accuracy on the test set. My dataset contains some classes with more than 1000 images and others with 30 images. For ...

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