course materials

data/100KUsedCar/readme.txt

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+Original dataset available at https://www.kaggle.com/datasets/adityadesai13/used-car-dataset-ford-and-mercedes
+
+# About Dataset
+If you download/use the data set I'd appreciate an up vote, cheers.
+
+# Updated
+Scraped data of used cars listings. 100,000 listings, which have been separated into files corresponding to each car manufacturer. I collected the data to make a tool to predict how much my friend should sell his old car for compared to other stuff on the market, and then just extended the data set. Then made a more general car value regression model.
+
+# previous version
+Picked two fairly common cars on the British market for analysis (Ford Focus and Mercedes C Class). The hope is to find info such as: when is the ideal time to sell certain cars (i.e. at what age and mileage are there significant drops in resale value). Also can make comparisons between the two, and make a classifier for a ford or Mercedes car. Can easily add more makes and models, so comment for any request e.g. if you want a big data set of all Mercedes makes and models.
+
+# Content
+The cleaned data set contains information of price, transmission, mileage, fuel type, road tax, miles per gallon (mpg), and engine size. I've removed duplicate listings and cleaned the columns, but have included a notebook showing the process and the original data for anyone who wants to check/improve my work.
+
+# Inspiration
+It'd be cool to have some insights and visualisations of the data. Also, am open to ideas on how to expand the data set.

data/AGNews/readme.txt

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+Original dataset available at https://github.com/mhjabreel/CharCnn_Keras/tree/master/data/ag_news_csv
+
+AG's News Topic Classification Dataset
+
+Version 3, Updated 09/09/2015
+
+
+ORIGIN
+
+AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000  news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .
+
+The AG's news topic classification dataset is constructed by Xiang Zhang ([email protected]) from the dataset above. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
+
+
+DESCRIPTION
+
+The AG's news topic classification dataset is constructed by choosing 4 largest classes from the original corpus. Each class contains 30,000 training samples and 1,900 testing samples. The total number of training samples is 120,000 and testing 7,600.
+
+The file classes.txt contains a list of classes corresponding to each label.
+
+The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 3 columns in them, corresponding to class index (1 to 4), title and description. The title and description are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".

data/AutoMPG/readme.txt

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+Original dataset available at https://archive.ics.uci.edu/dataset/9/auto+mpg
+
+# Information
+
+## Additional Information
+This dataset is a slightly modified version of the dataset provided in the StatLib library.  In line with the use by Ross Quinlan (1993) in predicting the attribute "mpg", 8 of the original instances were removed because they had unknown values for the "mpg" attribute.  The original dataset is available in the file "auto-mpg.data-original".
+
+"The data concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes." (Quinlan, 1993)

data/ConcreteCrack/readme.txt

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+Original dataset available at https://data.mendeley.com/datasets/5y9wdsg2zt/2
+
+# Concrete Crack Images for Classification
+
+Published: 23 July 2019| Version 2 | DOI: 10.17632/5y9wdsg2zt.2
+Contributor: Çağlar Fırat Özgenel
+
+# Description
+The dataset contains concrete images having cracks. The data is collected from various METU Campus Buildings.
+The dataset is divided into two as negative and positive crack images for image classification. 
+Each class has 20000images with a total of 40000 images with 227 x 227 pixels with RGB channels. 
+The dataset is generated from 458 high-resolution images (4032x3024 pixel) with the method proposed by Zhang et al (2016). 
+High-resolution images have variance in terms of surface finish and illumination conditions. 
+No data augmentation in terms of random rotation or flipping is applied. 
+
+If you use this dataset please cite: 
+2018 – Özgenel, Ç.F., Gönenç Sorguç, A. “Performance Comparison of Pretrained Convolutional Neural Networks on Crack Detection in Buildings”, ISARC 2018, Berlin.
+
+Lei Zhang , Fan Yang , Yimin Daniel Zhang, and Y. J. Z., Zhang, L., Yang, F., Zhang, Y. D., & Zhu, Y. J. (2016). Road Crack Detection Using Deep Convolutional Neural Network. In 2016 IEEE International Conference on Image Processing (ICIP). http://doi.org/10.1109/ICIP.2016.7533052

data/Fruits360/readme.txt

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+The FOMO dataset is a subset of the Fruits-360 dataset. The original dataset is available at:
+- https://github.com/Horea94/Fruit-Images-Dataset
+- https://www.kaggle.com/datasets/moltean/fruits
+
+# Fruits 360 dataset: A dataset of images containing fruits and vegetables
+
+Version: 2020.05.18.0
+
+# Content
+The following fruits and are included:
+Apples (different varieties: Crimson Snow, Golden, Golden-Red, Granny Smith, Pink Lady, Red, Red Delicious), Apricot, Avocado, Avocado ripe, Banana (Yellow, Red, Lady Finger), Beetroot Red, Blueberry, Cactus fruit, Cantaloupe (2 varieties), Carambula, Cauliflower, Cherry (different varieties, Rainier), Cherry Wax (Yellow, Red, Black), Chestnut, Clementine, Cocos, Corn (with husk), Cucumber (ripened), Dates, Eggplant, Fig, Ginger Root, Granadilla, Grape (Blue, Pink, White (different varieties)), Grapefruit (Pink, White), Guava, Hazelnut, Huckleberry, Kiwi, Kaki, Kohlrabi, Kumsquats, Lemon (normal, Meyer), Lime, Lychee, Mandarine, Mango (Green, Red), Mangostan, Maracuja, Melon Piel de Sapo, Mulberry, Nectarine (Regular, Flat), Nut (Forest, Pecan), Onion (Red, White), Orange, Papaya, Passion fruit, Peach (different varieties), Pepino, Pear (different varieties, Abate, Forelle, Kaiser, Monster, Red, Stone, Williams), Pepper (Red, Green, Orange, Yellow), Physalis (normal, with Husk), Pineapple (normal, Mini), Pitahaya Red, Plum (different varieties), Pomegranate, Pomelo Sweetie, Potato (Red, Sweet, White), Quince, Rambutan, Raspberry, Redcurrant, Salak, Strawberry (normal, Wedge), Tamarillo, Tangelo, Tomato (different varieties, Maroon, Cherry Red, Yellow, not ripened, Heart), Walnut, Watermelon.
+
+# Dataset properties
+
+The total number of images: 90483.
+
+Training set size: 67692 images (one fruit or vegetable per image).
+
+Test set size: 22688 images (one fruit or vegetable per image).
+
+The number of classes: 131 (fruits and vegetables).
+
+Image size: 100x100 pixels.
+
+Filename format: image_index_100.jpg (e.g. 32_100.jpg) or r_image_index_100.jpg (e.g. r_32_100.jpg) or r2_image_index_100.jpg or r3_image_index_100.jpg. "r" stands for rotated fruit. "r2" means that the fruit was rotated around the 3rd axis. "100" comes from image size (100x100 pixels).
+
+# WARNING
+
+There is a new -major version- of the dataset under release. A test archive (named fruits-360-original-size.zip) was already loaded to Kaggle. The new version contains images at their original (captured) size.
+The name of the image files in the new version does not contain the "_100" suffix anymore. This will help you to make distinction between this version and the old 100x100 version.
+So, if you use the 100x100 version, please make sure that the file names have the "_100" suffix. All others MUST be ignored.
+
+# END OF WARNING
+
+Different varieties of the same fruit (apple for instance) are stored as belonging to different classes.
+
+# How fruits were filmed
+
+Fruits and vegetables were planted in the shaft of a low-speed motor (3 rpm) and a short movie of 20 seconds was recorded.
+
+A Logitech C920 camera was used for filming the fruits. This is one of the best webcams available.
+
+Behind the fruits, we placed a white sheet of paper as a background.
+
+Here is a movie showing how the fruits and vegetables are filmed: https://youtu.be/_HFKJ144JuU
+
+# How fruits were extracted from background
+
+However, due to the variations in the lighting conditions, the background was not uniform and we wrote a dedicated algorithm that extracts the fruit from the background. This algorithm is of flood fill type: we start from each edge of the image and we mark all pixels there, then we mark all pixels found in the neighborhood of the already marked pixels for which the distance between colors is less than a prescribed value. We repeat the previous step until no more pixels can be marked.
+
+All marked pixels are considered as being background (which is then filled with white) and the rest of the pixels are considered as belonging to the object.
+
+The maximum value for the distance between 2 neighbor pixels is a parameter of the algorithm and is set (by trial and error) for each movie.
+
+Pictures from the test-multiple_fruits folder were taken with a Nexus 5X phone.
+
+# Research papers
+
+Horea Muresan, Mihai Oltean, Fruit recognition from images using deep learning, Acta Univ. Sapientiae, Informatica Vol. 10, Issue 1, pp. 26-42, 2018.
+
+The paper introduces the dataset and implementation of a Neural Network trained to recognize the fruits in the dataset.
+Alternate download
+
+This dataset is also available for download from GitHub: Fruits-360 dataset
+
+# History
+
+Fruits were filmed at the dates given below (YYYY.MM.DD):
+
+2017.02.25 - Apple (golden).
+
+2017.02.28 - Apple (red-yellow, red, golden2), Kiwi, Pear, Grapefruit, Lemon, Orange, Strawberry, Banana.
+
+2017.03.05 - Apple (golden3, Braeburn, Granny Smith, red2).
+
+2017.03.07 - Apple (red3).
+
+2017.05.10 - Plum, Peach, Peach flat, Apricot, Nectarine, Pomegranate.
+
+2017.05.27 - Avocado, Papaya, Grape, Cherrie.
+
+2017.12.25 - Carambula, Cactus fruit, Granadilla, Kaki, Kumsquats, Passion fruit, Avocado ripe, Quince.
+
+2017.12.28 - Clementine, Cocos, Mango, Lime, Lychee.
+
+2017.12.31 - Apple Red Delicious, Pear Monster, Grape White.
+
+2018.01.14 - Ananas, Grapefruit Pink, Mandarine, Pineapple, Tangelo.
+
+2018.01.19 - Huckleberry, Raspberry.
+
+2018.01.26 - Dates, Maracuja, Plum 2, Salak, Tamarillo.
+
+2018.02.05 - Guava, Grape White 2, Lemon Meyer
+
+2018.02.07 - Banana Red, Pepino, Pitahaya Red.
+
+2018.02.08 - Pear Abate, Pear Williams.
+
+2018.05.22 - Lemon rotated, Pomegranate rotated.
+
+2018.05.24 - Cherry Rainier, Cherry 2, Strawberry Wedge.
+
+2018.05.26 - Cantaloupe (2 varieties).
+
+2018.05.31 - Melon Piel de Sapo.
+
+2018.06.05 - Pineapple Mini, Physalis, Physalis with Husk, Rambutan.
+
+2018.06.08 - Mulberry, Redcurrant.
+
+2018.06.16 - Hazelnut, Walnut, Tomato, Cherry Red.
+
+2018.06.17 - Cherry Wax (Yellow, Red, Black).
+
+2018.08.19 - Apple Red Yellow 2, Grape Blue, Grape White 3-4, Peach 2, Plum 3, Tomato Maroon, Tomato 1-4 .
+
+2018.12.20 - Nut Pecan, Pear Kaiser, Tomato Yellow.
+
+2018.12.21 - Banana Lady Finger, Chesnut, Mangostan.
+
+2018.12.22 - Pomelo Sweetie.
+
+2019.04.21 - Apple Crimson Snow, Apple Pink Lady, Blueberry, Kohlrabi, Mango Red, Pear Red, Pepper (Red, Yellow, Green).
+
+2019.06.18 - Beetroot Red, Corn, Ginger Root, Nectarine Flat, Nut Forest, Onion Red, Onion Red Peeled, Onion White, Potato Red, Potato Red Washed, Potato Sweet, Potato White.
+
+2019.07.07 - Cauliflower, Eggplant, Pear Forelle, Pepper Orange, Tomato Heart.
+
+2019.09.22 - Corn Husk, Cucumber Ripe, Fig, Pear 2, Pear Stone, Tomato not Ripened, Watermelon.
+License
+
+# MIT License
+
+Copyright (c) 2017-2021 Mihai Oltean
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.