Overview:
The Dataset have created a 37 category pet dataset with roughly 200 images for each class. The images have a large variations in scale, pose and lighting. All images have an associated ground truth annotation of breed, head ROI, and pixel level trimap segmentation.
Class Names:
['Abyssinian', 'american_bulldog', 'american_pit_bull_terrier', 'basset_hound', 'beagle', 'Bengal', 'Birman', 'Bombay', 'boxer', 'British_Shorthair', 'chihuahua', 'Egyptian_Mau', 'english_cocker_spaniel', 'english_setter', 'german_shorthaired', 'great_pyrenees', 'havanese', 'japanese_chin', 'keeshond', 'leonberger', 'Maine_Coon', 'miniature_pinscher', 'newfoundland', 'Persian', 'pomeranian', 'pug', 'Ragdoll', 'Russian_Blue', 'saint_bernard', 'samoyed', 'scottish_terrier', 'shiba_inu', 'Siamese', 'Sphynx', 'staffordshire_bull_terrier', 'wheaten_terrier', 'yorkshire_terrier']
This Project uses:
- Transfer Learning (InceptionV3)
- Tensorflow
- Numpy
- Matplotlib
- Tensorflow Dataset (TFDS)
- Callbacks (Early Stopping)
- Keras
- Scikit Learning (Metrics)
