Image classification models: Machine learning model using Support Vector Machine vs Deep learning model using CNN

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Training dataset: Caltech-UCSD-Birds-200-2011 dataset which contains 11,788 images of 200 bird species. The dataset is divided into 3 subset: training, validation, and testing subsets with the ratio 60:20:20 respectively.

For more information about the dataset, visit the project website:

http://www.vision.caltech.edu/visipedia

If you use the dataset in a publication, please cite the dataset in the style described on the dataset website (see url above).

Directory Information

• images/ The images organized in subdirectories based on species. See IMAGES AND CLASS LABELS section below for more info.

========================= IMAGES AND CLASS LABELS:

Images are contained in the directory images/, with 200 subdirectories (one for each bird species)

------- List of image files (images.txt) ------ The list of image file names is contained in the file images.txt, with each line corresponding to one image:

<image_id> <image_name>

------- List of class names (classes.txt) ------ The list of class names (bird species) is contained in the file classes.txt, with each line corresponding to one class:

<class_id> <class_name>

------- Image class labels (image_class_labels.txt) ------ The ground truth class labels (bird species labels) for each image are contained in the file image_class_labels.txt, with each line corresponding to one image:

<image_id> <class_id>

where <image_id> and <class_id> correspond to the IDs in images.txt and classes.txt, respectively.

========================= BOUNDING BOXES:

Each image contains a single bounding box label. Bounding box labels are contained in the file bounding_boxes.txt, with each line corresponding to one image:

<image_id>

where <image_id> corresponds to the ID in images.txt, and , , , and are all measured in pixels