Description
Project 1 Specification (v1.1) Change log:
minor changes to the description of the test set.
Challenge
The goal of this mini challenge is to identify the attribute label depicted in a facial photograph. The data for this task comes from the CelebA dataset [1], which contains 200 thousand images belonging to 40 attribute labels. Specifically, the challenge data for this course consists of 160,000 images for training, 20,000 images for validation and 20,000 images for testing. The images will be pre-cropped and aligned to make the data more manageable.
For each image, algorithms will produce a list of all possible attribute labels. The quality of a labeling will be evaluated based on the label that best matches the ground truth label for the image. The idea is to allow an algorithm to identify multiple attribute labels in an image given that humans often describe a face using different words (e.g. black hair, big eyes, smiling).
Example:
000001.jpg -1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 1 1 -1 1 -1 -1
1 -1 -1 1 -1 -1 -1 1 1 -1 1 -1 1 -1 -1 1
where “1” indicates positive prediction while “-1” indicates negative prediction for each attribute.
- A txt containing the following info:
- Description of the files you have submitted. o References to the third party libraries you are using in your solution (leave blank if you are not using any of them).
- Any details you want the person who tests your solution to know when he/she tests your solution, e.g which script to run.
Tips
Refer to reference [2] to get started.
Use the following techniques to boost the recognition accuracy:
- data augmentation, e.g. random flip [3]
- deeper model, e.g. ResNet-50 [4]
- advanced loss functions, e.g. focal loss [5]
Computational Resource
You can use the computational resources assigned by the MSAI course. Alternatively, you can use Amazon’s EC2 or Google CoLab for computation. As a student, you can sign up to receive free $100 credit through the AWS Educate program. We encourage students to use g2.2xlarge instances running Ubuntu for maximal ease of installing. Note that $100 of Amazon credit allows you to run a g2.2xlarge GPU instance for approximately 6 days without interruption (you should keep it on only while using it).
References
- Liu et al. Deep Learning Face Attributes in the Wild, ICCV 2015
- Face attribute prediction: https://github.com/d-li14/face-attribute-prediction
- He et al. Bag of Tricks for Image Classification with Convolutional Neural Networks, ArXiv 2018
- He et al. Deep Residual Learning for Image Recognition, CVPR 2016
[3] T-Y Lin et al., Focal Loss for Dense Object Detection, ICCV 2017
