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0 | 123 125 124 142 209 226 234 236 231 232 235 223 211 196 184 181 182 186 185 193 208 211 208 201 196 192 191 192 194 201 207 216 225 225 223 220 219 220 172 124 124 122 121 121 122 122 122 122 123 125 123 158 221 230 234 236 229 232 237 225 212 200 187 182 182 186 189 192 205 211 204 196 195 197 196 194 195 202 210 217 ... |
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Facial Expression Recognition Challenge (ICML 2013)
Overview
This dataset was created for the Challenges in Representation Learning: Facial Expression Recognition Challenge, part of the ICML 2013 Workshop. The challenge focused on evaluating how well learning algorithms can generalize to newly introduced data, particularly for facial expression classification.
- Start Date: April 13, 2013
- End Date: May 25, 2013
The dataset contains facial images labeled with one of seven emotional classes, and participants were challenged to develop models that could accurately classify these expressions.
One motivation for representation learning is that learning algorithms can design features better and faster than humans can. To this end, we introduce an entirely new dataset and invite competitors to build models that work effectively on unseen data.
Files
train.csv: Training datatest.csv: Public test data used for leaderboard scoringtrain.csv.zip: Zipped version of the training datatest.csv.zip: Zipped version of the test data
Dataset Details
The dataset consists of grayscale images, each represented as a flattened array of pixel intensities along with a corresponding emotion label (for training). It has been sourced from various internet sources and curated specifically for this challenge.
Evaluation
Participants are evaluated based on the accuracy of their models in predicting the correct facial expression out of the seven available classes.
- Public Leaderboard: Uses
test.csv - Final Evaluation: Uses a hidden test set released 72 hours before the contest closes
To ensure fair play:
- Manual labeling of the test set is strictly prohibited.
- Preliminary winners must release their code and methodology under an OSI-approved open-source license.
Timeline
- April 12, 2013: Competition launched
- May 17, 2013: Final test set released
- May 24, 2013 (11:59 PM UTC): Final submission deadline
- May 31, 2013: Code release deadline for preliminary winners
- June 20–21, 2013: ICML Workshop and winner presentations
Prizes
- 🥇 First Prize: $350 + Invitation to speak at the ICML 2013 Workshop
- 🥈 Second Prize: $150
Prize funding provided by Google Inc.
Baseline Code
Baseline models are provided as part of the pylearn2 Python package.
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