All Questions
35 questions
2
votes
1
answer
414
views
Is vision transformer (ViT) always better than CNN?
The paper - AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE proposed vision transformer and outperformed CNN-based models in many cases.
When it comes to sequential data, we ...
- 23
1
vote
0
answers
25
views
Suggestions for labeling regression data to improve model accuracy
I'm working on a convolutional neural network that should predict up to 3 (x,y) coordinate pairs representing the waypoints of a concrete path, given an input image. This network will be used to help ...
- 11
1
vote
0
answers
22
views
Attention mechanism: Why apply multiple different transformations to obtain query, key, value
I have two questions about the structure of attention modules:
Since I work with imagery I will be talking about using convolutions on feature maps in order to obtain attention maps.
If we have a set ...
- 181
1
vote
1
answer
361
views
Feature extraction from sequence of images with Siamese Neural Network
I am trying to train a neural network to recognize certain actions in short movies.
Each such movie consists of a fixed number of frames, each frame - the image is of course the same size, after ...
- 113
1
vote
0
answers
23
views
Machine learning model (neural network or SVM) for unequal feature matrices size
I have feature matrices obtained from visual bags of words model for various dictionary sizes. Example, Nx5, Nx10, …., Nx15000. Where N is the number of samples and 5, 10, …15000 are the visual ...
- 11
1
vote
0
answers
65
views
Denoising Prior to Image Classification
From what I have read, Denoising during preprocessing for image classification tasks seems to be a bit controversial.
While on one hand it might improve classification accuracy, the computational ...
- 123
1
vote
0
answers
136
views
What should be the best CNN model for Feature extraction of images for Image-Image search engine using LSH?
I have images something like the below:
And like this:
I have a huge data around 20M or so am I want to apply de-duplication and improve the search for this ...
- 323
0
votes
1
answer
55
views
How to manage memory constraint and increase speed for 1 vs rest image similarity comparison for over 100k images for computer vision?
I'm looking for ideas on how to do things in a better way, efficiently when using Machine/Deep Learning.
I am working on a search improvement problem using Computer vision where I am thinking about ...
- 323
1
vote
1
answer
325
views
Building a prediction model for dynamic coordinates and later categorize as binary classification
Project Summary
I have a website (academic project) that records mouse movements such as click, mouse up, mouse down, etc. It records the coordinates for each event on a given web page from a visitor....
0
votes
1
answer
736
views
UNet Model accuracy is stuck at exact 0.5 (neither more or less) (No class imbalance, tried tuning learning rate)
This is using PyTorch
I have been trying to implement UNet model on my images, however, my model accuracy is always exact 0.5. Loss does decrease.
I have also checked for class imbalance. I have ...
- 103
3
votes
0
answers
65
views
Information Extraction from image / text - approach?
I need assistance with a ML project I am currently trying to create.
I receive a lot of invoices from a lot of different suppliers - all in their own unique layout. I need to extract 3 key elements ...
- 185
-1
votes
3
answers
3k
views
why the sigmoid function will be 1 and 0 if we use a fully connected layer that produce a big enough positive(res negative )output
HI I am using a fully connected network that uses sigmoid if we feed a a big enough weights the sigmoid function will finally become 1 or 0 , is there any solution to avoid this ?
and will this lead ...
- 387
8
votes
2
answers
183
views
Neural Network Architecture for Identifying Image Copies
I have a large image collection and wish to identify the images within that collection that appear to copy other images from the collection.
To give you a sense of the kinds of image pairs that I ...
- 204
5
votes
1
answer
3k
views
How design a autoencoder architecture
I would like to build an autoencoder (CNN) to learn a representation of my data.
I never built such a network and I have some experience in supervised learning (classification).
I would like to know ...
- 428
2
votes
0
answers
283
views
Maximum number of classes YOLO net can recognize on mobile
I'm trying to make a mobile app on image recognition(Computer Vision Application) . Does anyone know whether modern day smartphones have enough processing power/memory to recognize, say about 1 ...
3
votes
2
answers
219
views
Simple Object Detection
I want to create a simple object detection tool. So basically an image will be provided to the tool and from that, it has to detect the number of objects.
For example
An image of a dining table ...
- 131
3
votes
1
answer
277
views
How to label "other" while labeling image for object detection/classification?
I want to train a model to recognize the different categories of food e.g. rice, burger, apple, pizza, orange and other things.
After the first training, I realized that the model is detecting other ...
- 141
13
votes
1
answer
9k
views
What is difference between Fully Connected layer and Bilinear layer in CNN?
What is the difference between Fully Connected layers and Bilinear layers in deep learning?
- 2,015
3
votes
3
answers
1k
views
The goal of fine tuning
I would like to ask what is the goal of fine-tuning a VGGNet on my dataset.
What does fine-tuning mean? Does it mean to change the weights or keep the weight values?
- 2,015
3
votes
2
answers
2k
views
Watermark detection using Deep Learning [closed]
I have images with and without watermark. There is only one type of watermark. I have tried VGG16 transfer learning, but results were bad.
What are the methods to ...
- 59
1
vote
0
answers
88
views
How to estimate Distance to Obstacles using Lidar's BEV(Bir's Eye view ) Representation?
I am using a implementation that use both camera and Lidar for 3D obstacle detection for self driving cars but I have no idea how to calculate the distance to the obstacles since the Lidar uses the ...
- 387
9
votes
4
answers
15k
views
Exploratory Data Analysis with Image Datset
In Machine Learning Kernels on Kaggle I often see EDAs with structured data. So, I was wondering, if there are any recommended/standard procedures for EDA with image datasets. What kind of statistical ...
2
votes
1
answer
188
views
Doing a fine tuning after a transfer learning
I read about fine tuning and transfer learning for CNNs and was wondering if we can do fine tuning after using transfer learning on the same CNN? If so, will this increase the performance of the model ...
- 387
0
votes
2
answers
73
views
What it Would be easier Building a Deep Net From Scratch or Using an existing Architecture? [closed]
In Practice with CNN what would be easier: Building a CNN from scratch or using a an existing architecture with some updates?
- 387
41
votes
2
answers
53k
views
How to calculate mAP for detection task for the PASCAL VOC Challenge?
How to calculate the mAP (mean Average Precision) for the detection task for the Pascal VOC leaderboards?
There said - at page 11:
Average Precision (AP). For the VOC2007 challenge, the interpolated
...
- 649
0
votes
1
answer
3k
views
Multiple Object recognition in image using deep Learning [closed]
I am working on recognizing object classes in images using neuronal nets so I could make classifiers for cats, dogs... using Imagenet and some Conv nets famous architectures but my problems is if I ...
- 387
5
votes
1
answer
398
views
What is the fully-convolutional model?
What is the fully-convolutional model?
Is fully-convolutional model a model that has only convolutional layers (with Batch-norm and Activation) and has not any: max-pool, fully-connected, and other ...
- 649
8
votes
2
answers
9k
views
What is the different between Fine-tuning and Transfer-learning?
Usually the neural network training has at least 2 steps:
first trained on a large set of some standard data (ImageNet, ...)
and then the resulting weights are trained on a small set of my data (in ...
- 649
2
votes
2
answers
2k
views
Why do we need for Shortcut Connections to build Residual Networks?
Why do we need for Shortcut Connections to build Residual Networks, and how it help to train neural networks for classification and detection?
- 649
6
votes
1
answer
2k
views
Convnet training error does not decrease
I'm training a convoluted neural net to drive a toy car, and no matter what I do the training accuracy does not increase beyond 30-35%, which is where it starts when the convnet is randomly ...
- 4,189
0
votes
2
answers
449
views
My first machine learning experiment , model not converging , tips? [closed]
I wanted to recreate the model mentioned in this paper:https://arxiv.org/pdf/1610.09204v1.pdf . I am using keras with tensorflow backend, and a gtx 1050ti.
I am an ML beginner, and thought this would ...
- 109
6
votes
2
answers
14k
views
Overfitting after first epoch
I am using convolutional neural networks (via Keras) as my model for facial expression recognition (55 subjects). My data set is quite hard and around 450k with 7 classes. I have balanced my training ...
- 91
2
votes
2
answers
2k
views
Training Validation Testing set split for facial expression dataset
I am using Convolutional Neural Networks (CNN) and I just want to ask if the way I split my training/validation/testing set is correct.
I have a total of 55 subjects. I plan to split them into 80–10–...
- 91
3
votes
0
answers
230
views
How to improve the neural art algorithm?
I have been playing around the algorithm with tensorflow in this paper.
I tried to convert a photo to a Chinese ink and wash painring, but I got some strange patterns in the output picture(those in ...
- 31
1
vote
0
answers
639
views
Trajectory data mining and pattern recognition using ORB-SLAM and KNN-DTW
I've started working on a project about the trajectory data mining from videos - for example: snowboarding video from GoPro action camera.
This is the continuation of my previous experiment (MotionML)...
- 111