[SOLVED] Ai6126 project 1  celebamask face parsing

Project 1 Specification

Face parsing assigns pixel-wise labels for each semantic components, e.g., eyes, nose, mouth. The goal of this mini challenge is to design and train a face parsing network. We will use the data from the CelebAMask–HQ Dataset [1] (See Figure 1). For this challenge, we prepared a mini-dataset, which consists of 1000 training and 100 validation pairs of images, where both images and annotations have a resolution of 512 x 512.

The performance of the network will be evaluated based on the F-measure between the predicted masks and the ground truth of the test set (the ground truth of the test set will not be released).

Figure 1. Sample images in CelebAMask-HQ

We will evaluate and rank the performance of your network model on our given 100 unseen test images based on the F-measure.

The higher the rank of your solution, the higher the score you will receive. In general, scores will be awarded based on the Table below.

Notes:

sum(p.numel() for p in model.parameters())

to compute the number of parameters in your network.

We host the validation and test sets on CodaBench. Please follow the guidelines to ensure your results to be recorded.

The website of the competition is https://www.codabench.org/competitions/5726

IMPORANT NOTE Please refer “Get Started → Submission” on the CodaBench page to for the file structure of your submission. Please adhere to the required file structure. Submissions that do not follow the structure cannot be properly evaluated, which may affect your final marks.

If your submission status is “failed”, check the error logs to identify the issue. The evaluation process may take a few minutes.

You can use the computational resources assigned by the MSAI course. Alternatively, you can use Google CoLab for computation

Interactive Facial Image Manipulation, CVPR 2020