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Deep Learning,Online Communities,Image Data,Computer Vision,Multiclass Classification Classification

I had to prepare a presentation for a meetup in Portland, OR area and was looking for a fresh data set. I didn't wan......

数据结构 ? 5.1G

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    I had to prepare a presentation for a meetup in Portland, OR area and was looking for a fresh data set. I didn't want to use the well known data sets such as digits-mnist, fashion-mnist, iris flowers, etc. I decided to just create my own data set about something that I love to do. Summer 2019 is coming and Oregon is beautiful to explore and while I'm driving I'm always looking for a deer on the side of the route, or eagles on top of the trees, and go to the local safaris to see bears (don't want to find them in a hike lol) and take some pictures. So well there is when the idea came out. "Let's download a data set of Oregon wildlife and get some fun training a model to classify them".  For this I use a google scraper I found on GitHub. Forked the repository, started a new branch, did some adaptations of that code and downloaded the data set. After the presentation I didn't know what to do with the database. It was just resting on my laptop. I decided to load it here on Kaggle and share some notebooks and hoping this data set can be fun to explore for the community!


    Here we find a small data set of 14013 images in the folder distributed in 20 classes as follows:

    folder: baldeagle images: 748   folder: blackbear images: 718  
    folder: cougar images: 680  
    folder: elk images: 660  
    folder: graywolf images: 730   folder: mountainbeaver images: 577  
    folder: bobcat images: 696  
    folder: nutria images: 701  
    folder: coyote images: 736  
    folder: columbianblack-taileddeer images: 735  
    folder: seals images: 698  
    folder: canadalynx images: 717   folder: ringtail images: 588   folder:  redfox images: 759  
    folder: grayfox images: 668   folder: virginiaopossum images: 728  
    folder: sea_lions images: 726  
    folder: raccoon images: 728  
    folder: raven images: 656  
    folder: deer images: 764

    The second file is a sample of the data with just 5 classes already prepossessed with GapCV library in a h5 file with 3531 images distributed as follows:

    key: baldeagle images: 748   key: blackbear images: 718  
    key: cougar images: 680  
    key: elk images: 660  
    key: gray_wolf images: 730




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