Have you heard of #Tube #CNN ?

Object or human detection in video is crucial for many applications.

It can also have useful applications such as in repetitive #manufacturing tasks to monitor and proactively prevent catastrophes.

Compared to images, video provides additional cues which can help to disambiguate the detection problem.

Here the authors attempt to learn discriminative models for the temporal evolution of object appearance and to use such models for object detection.

They introduced space-time tubes corresponding to temporal sequences of bounding boxes. They propose a TPN network where two CNN architectures for generating and classifying tubes, respectively, this helps maximize object recall.

The Tube-CNN then implements a tube-level object detector in the video. Our method improves state of the art on two large-scale datasets for object detection in video: HollywoodHeads and ImageNet VID. Tube models show particular advantages in difficult dynamic scenes.

Link to paper: https://lnkd.in/d3DW5Qe
Pytorch implementation of Tube-CNN : https://lnkd.in/dzxGdtt

#deeplearning #CNN #machinelearning #videoanalytics

✴️ @AI_Python_EN
🗣 @AI_Python_Arxiv
❇️ @AI_Python

#AI approach outperformed human experts (AGAIN) in identifying #cervical precancer!

A research team led by investigators from the National Institutes of Health and Global Good has developed a #deeplearning #algorithm that can analyze digital images of a woman's cervix and accurately identify precancerous changes that require medical attention. This artificial intelligence (AI) approach, called automated visual evaluation, has the potential to revolutionize cervical cancer screening, particularly in low-resource settings.

To create the algorithm, the research team used more than 60,000 cervical images from an NCI archive of photos collected during a cervical cancer screening study that was carried out in Costa Rica in the 1990s.

Overall, the algorithm performed better than all standard screening tests at predicting all cases diagnosed during the Costa Rica study. Automated visual evaluation identified precancer with greater accuracy (AUC=0.91) than a human expert review (AUC=0.69) or conventional cytology (AUC=0.71).

Paper here: https://lnkd.in/dxETi8K
#algorithms #prediction #cancer #machinelearning #cnn #transferlearning

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

Highly recommend MIT 6.S191: Introduction to Deep Learning Course by Alexander Amini and Ava Soleimany through Massachusetts Institute of Technology (MIT)

#artificialintelligence
#deeplearning
#machinelearning
#CNN
#vision

MIT Deep Learning Course 6.S191 https://www.linkedin.com/pulse/mit-deep-learning-course-6s191-bhagirath-kumar-lader

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

Speech-to-Text using Convolutional Neural Networks
#CNN
Deep Learning beginners quickly learn that Recurrent Neural Network ( #RNN s) are for building models for sequential data tasks (such as language translation) whereas Convolutional Neural Networks (CNNs) are for image and video related tasks. This is a pretty good thumb rule - but recent work at Facebook has shown some great results for sequential data just by using CNNs.

✴️ @AI_Python_EN

Building a #Conversational #AI #Agent for medical and healthcare services is one of the products in our pipeline in the coming months.

Here is how a typical chatbot recirculation recurrent #pipeline looks like

#CNN #RNN #GAN #DeepLearning #NLP

✴️ @AI_Python_EN

How to Develop a Convolutional Neural Network From Scratch for MNIST Handwritten Digit Classification
#cnn
https://machinelearningmastery.com/blog/

✴️ @AI_Python_EN

Walkthrough - When and How to Use MLP, CNN, and RNN Neural Networks - Jason Brownlee

To follow posts: https://lnkd.in/ev9S2hh

#machinelearning #artificialintelligence #datascience #ml #ai #deeplearning #MLP #CNN #RNN #neuralnetworks

✴️ @AI_Python_EN

Best Practices for Preparing and Augmenting Image Data for #ConvolutionalNeuralNetwork s
#CNN
🌎 Best Practices

✴️ @AI_Python_EN

Classifying Legendary Pokemon Birds 🐦🐦🐦

👉👉👉 Try it yourself:
https://lnkd.in/eYhKNAh 👈👈👈

After only the second fastai "Practical Deep Learning for Coders" class I was able to complete an end-to-end deep learning project! 🤖🤖🤖

The main goal is to classify an image as either one of the Legendary Pokemon Birds - Articuno, Moltres or Zapdos - or an alternative class which includes everything else. Needless to say that sometimes my model gets confused about the alternative class since not so many diverse images were feed into it...

Source code:
https://lnkd.in/eRfkBx8
Forked from:
https://lnkd.in/e_k4nqN

#ai #ml #dl #deeplearning #cnn #python

✴️ @AI_Python_EN

#Detection ...? or #Classification ...? its all there in #PyTorch.
A pytorch lib with state-of-the-art architectures, pre-trained models and real-time updated results.
#deeplearning #ai #cnn

https://lnkd.in/eTdvfEp

✴️ @AI_Python_EN