Dataset | COIL20 | COIL100 | CMU-PIE | USPS | MNIST | REUTERS-10K |
|---|
# Observations | 1440 | 7200 | 2856 | 9298 | 70,000 | 10,000 |
# Classes | 20 | 100 | 68 | 10 | 10 | 4 |
Dimensions | 32 × 32 × 1 | 32 × 32 × 3 | 32 × 32 × 1 | 16 × 16 × 1 | 28 × 28 | 2000 |
Type | Image, pixel | Image, pixel | Image, pixel | Image, pixel | Image, pixel | Text, tf-idf |
Task | Object rec. | Object rec. | Face rec. | Digit rec. | Digit rec. | Topic rec. |
- COIL20: grayscale images for object recognition dataset containing 20 objects positioned at 72 different angles [29]. COIL100: RGB images of 100 objects at 72 different poses [29]. The images were downsampled to 32 × 32 pixels from the original 128 × 128 pixels to facilitate analysis for unsupervised feature learning [24]. CMU-PIE: grayscale images of 68 human faces with 4 different poses [30]. USPS: grayscale images of handwritten digits (0–9) from the USPS postal service [31]. MNIST: grayscales images of handwritten digits (0–9) obtained from NIST [32]. REUTERS-10K: A Reuters news service dataset containing text documents in English that is used for topic recognition, which was processed according to Xie et al. [8]. The term frequency–inverse document frequency (tf-idf) [33] feature matrix was computed, using the 2000 most frequent words
