Scene Recognition using CNN, Fine Tuning a Pre-Trained Deep Network: Alexnet and VGG16
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Updated
Dec 5, 2019 - Jupyter Notebook
Scene Recognition using CNN, Fine Tuning a Pre-Trained Deep Network: Alexnet and VGG16
10000-Image-caption-data-of-diverse-scenes
In this project, SVMs and Bag of Words were used to identify image scene.
This GitHub repository focuses on an integrated approach to scene classification and image caption generation, aiming to improve the accuracy of scene evaluation in computer vision applications.
CS838 Group Assignments
This project uses Convolutional Neural Networks to identify scene of image. It uses the dataset from the "Scene-Recognition-SVM" repository.
10142-Videos-Fall-Behavior-Data
Multi-class Logistic Regression Classifier with Spatial Pyramid Matching for Feature Extraction
11230-Videos-Fight-Behavior-Data
🌎 Web Frontend for the open source project http://photos.network
[IAC 2023] This repository contains the code used in our paper, "AstrobeeCD: Change Detection in Microgravity with Free-Flying Robots." This method is useful for detecting 3D scene changes given a 3D model, a sequence of images, and a sequence of camera poses.
10-Categories-8085-Groups-of-Urban-Refined-Management-Data
ECI2019 - Despegar - Classification of scenes in images of accommodations (Spanish). Each lodging ad on Despegar's site has multiple images. Each one illustrates different parts of the product offered. Knowing which scene each image corresponds to is essential to obtain the best user experience. This Machine Learning competition was part of the …
Reconstruction or Approximation of environment from edge data. Using pre-trained models, we are analyzing the incoming edge data (CCTV camera footage) and extracting the detected objects and person's pose. These metadata are sent to remote server, where using these metadata, the video scene is reconstructed or approximated on a Real-time manner.
21404-Images-Human-Posture-Detection-Data-in-Home-Scenes
Scene Classification
78024-Images-Parking-Space-Data
code complementing the article https://www.graphicon.ru/html/2017/papers/pp269-274.pdf
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