dihoogl.blogg.se - Oid the talos principle image

#Oid the talos principle image android#
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#Oid the talos principle image download#

Privacy Dataset for Context-Dependent Photo Sharing.Omnidirectional HDR consumer camera dataset.Multimodal Dataset for Assessment of Quality of experience in Immersive Multimedia.MIMESIS: Modeling Immersive Media Experiences by Sensing Impact on Subjects.HDR image dataset with results of JPEG XT subjective evaluation.HDR-Eye: dataset of high dynamic range images with eye tracking data.Ultra-Eye: UHD and HD images eye tracking dataset.PEViD-HD: Privacy Evaluation Video Dataset.Rate-Distortion Evaluation For Two-Layer Coding Systems.OPPD: Odor Pleasantness Perception Database.PEViD-UHD: Privacy Evaluation Ultra High Definition Video Dataset.SCENIC: Subjective Comparison of ENcoders based on fItted Curves.JPEG core experiment for the evaluation of JPEG XR image coding.EPFL-PoliMI Subjective Video Quality Assessment Database.

#Oid the talos principle image software#

BCI: P300 based brain-computer interface software and subjects EEG dataset.

JJ2000: An Implementation of the JPEG2000 Standard in Java.

Summer School: Powder Diffraction School – PSI, Villigen.

2018 CCMX Advanced Course: Instrumented Nanoindentation.

CCMX – NCCR MARVEL Materials Science Day 2018.

CCMX – ScopeM Advanced Course: Combining Structural & Analytical Investigations of Matter at the Micro-, Nano and Atomic Scale.

CCMX Winter School – Surface Science: Fundamentals, Properties and Selected Applications.

CCMX Advanced Course: Inorganic Particle Synthesis by Precipitation.

Carbon Composites Schweiz Conference – “Thermoplastic Composites”.

2019 Advanced Course: From Additive Manufacturing to Field-Assisted Sintering.

2019 Advanced Course: Powder Characterisation and Dispersion – from nanometers to millimeters and from theory to practice.

2019 Summer School – Characterization of Materials.

2019 Advanced Course: Instrumented Nanoindentation.

2019 CCMX – NCCR MARVEL Materials Science Day.

Tech Apero – Smart Fibers for Wearable Sensors and Drug Delivery.

2021 Advanced Course: Advanced X-Ray Diffraction Methods for Coatings: Strain, Defects and Deformation Analysis of Thin Films.

2019 Advanced Course: Introduction to scanning electron microscopy microanalysis techniques.

2020 Winter School “Nanoparticles: from fundamentals to applications in life sciences”.

The total size of the Food-5K dataset is ~446.9 MB, while the total size of the Food-11 dataset is ~1.16 GB.

#Oid the talos principle image download#

You can download the two datasets as well as the demonstration app from the following FTP (please use dedicated FTP clients, such as FileZilla or FireFTP):Īfter you connect, choose the FoodImage folder from the remote site, and download the relevant material.

If the recognized food is wrong, you can correct the answer and our system will remember your correct label. You can simply take a picture with your camera or from photo gallery, and our app will recognize the food in it.

#Oid the talos principle image android#

We have also developed an Android app, named NutriTake, to demonstrate the food classification and recognition. The same naming convention is used, where ID 0-10 refers to the 11 food categories respectively. Similar as Food-5K dataset, the whole dataset is divided in three parts: training, validation and evaluation. The 11 categories are Bread, Dairy product, Dessert, Egg, Fried food, Meat, Noodles/Pasta, Rice, Seafood, Soup, and Vegetable/Fruit. This is a dataset containing 16643 food images grouped in 11 major food categories. ImageID: ID of the image within the class. The naming convention is as follows:ĬlassID: 0 or 1 0 means non-food and 1 means food. The whole dataset is divided in three parts: training, validation and evaluation. This is a dataset containing 2500 food and 2500 non-food images, for the task of food/non-food classification in our paper “Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model”. The data provided hereunder is on an “as is” basis and the Ecole Polytechnique Fédérale de Lausanne (EPFL) has no obligation to provide maintenance, support, updates, enhancements, or modifications. The Ecole Polytechnique Fédérale de Lausanne (EPFL) specifically disclaims any warranties. In no event shall the Ecole Polytechnique Fédérale de Lausanne (EPFL) be liable to any party for direct, indirect, special, incidental, or consequential damages arising out of the use of the data and its documentation. The data provided may not be commercially distributed.

#Oid the talos principle image license#

Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, modify, and distribute the data provided and its documentation for research purpose only.