This dataset contains 35 classes of wild edible plants with a total of 16,535 images. Each class varies in the number of images between 400 to 500. It has been tested with three state-of-the-art CNN architectures: MobileNet v2, GoogLeNet, and ResNet-34 using Transfer Learning.

These images have been obtained through Flickr's API and are free to use by all members of the public.

Context

This dataset is used in one of my team assignment to create a flower recognition system using different Machine Learning algorithms which are MLP, CNN, AlexNet, VGGNet, GoogleNet and ResNet.

Content

The pictures are divided into five classes: daisy, tulip, rose, sunflower, dandelion.

Acknowledgements

This dataset is an extension of the flower recognition dataset by Alexander Mamaev in https://www.kaggle.com/alxmamaev/flowers-recognition. Additional pictures are added into this dataset to provide more variety of flowers to train the model better.

Inspiration

Create a flower recognition system with the ML techniques that you know!

AI Data center to the Edge INTEL AI course

In this project, using Inception v3 model USA's most stolen cars was analysed and modeled t to predict the most stolen car.

Inception v3

The main hallmark of this architecture is the improved utilization of the computing resources inside the network. This was achieved by a carefully crafted design that allows for increasing the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC 2014 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

The necessary python packages to install is given in environment.yml file

environment.yml

DATASET

This is an overview of the VMMR dataset introduced in "A Large and Diverse Dataset for Improved Vehicle Make and Model Recognition".

Overview Despite the ongoing research and practical interests, car make and model analysis only attracts few attentions in the computer vision community. We believe the lack of high quality datasets greatly limits the exploration of the community in this domain. To this end, we collected and organized a large-scale and comprehensive image database called VMMRdb, where each image is labeled with the corresponding make, model and production year of the vehicle.

Description The Vehicle Make and Model Recognition dataset (VMMRdb) is large in scale and diversity, containing 9,170 classes consisting of 291,752 images, covering models manufactured between 1950 and 2016. VMMRdb dataset contains images that were taken by different users, different imaging devices, and multiple view angles, ensuring a wide range of variations to account for various scenarios that could be encountered in a real-life scenario. The cars are not well aligned, and some images contain irrelevant background. The data covers vehicles from 712 areas covering all 412 sub-domains corresponding to US metro areas. Our dataset can be used as a baseline for training a robust model in several real-life scenarios for traffic surveillance.

VMMRdb data distribution

The distribution of images in different classes of the dataset. Each circle is associated with a class, and its size represents the number of images in the class. The classes with labels are the ones including more than 100 images.

Citation If you use this dataset, please cite the following paper:

A Large and Diverse Dataset for Improved Vehicle Make and Model Recognition F. Tafazzoli, K. Nishiyama and H. Frigui In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2017.