fer-mek-checkpoints

FER / MEK checkpoints (ResNet-18/34)

7-class facial-expression recognition checkpoints for MEK (Mine Extra Knowledge, Zhang et al., NeurIPS 2023, arXiv:2310.19636) re-implemented on torchvision ResNet-18/34. Trained and evaluated on RAF-DB and FER-2013 under a leakage-free validation protocol (config selected on a 10% val split; test reported only). Headline metric: mean-class accuracy (unweighted mean of per-class recalls), the right objective under severe class imbalance.

Code: https://github.com/frichickens/CV-Project-HUST.

Checkpoints

All files are MEKResNet state_dicts (7 classes, 224x224 input). The rafdb_msceleb and fer_rn34 checkpoints are end-to-end verified: re-running them reproduces the reported overall / mean-class / per-class accuracy to four decimals.

Usage

import torch
from huggingface_hub import hf_hub_download
from mek.model import MEKResNet   # from the project repo

path = hf_hub_download("ToiTenBao/fer-mek-checkpoints", "mek_resnet18_rafdb_msceleb_best.pth")
model = MEKResNet("resnet18", num_classes=7, pretrained=False)
model.load_state_dict(torch.load(path, map_location="cpu", weights_only=True))
model.eval()
logits, attn = model(images)            # images: [B,3,224,224], ImageNet-normalised

RAF-DB class index -> emotion: 0=surprise 1=fear 2=disgust 3=happy 4=sad 5=angry 6=neutral (folders 1..7 sorted as strings). FER-2013 classes are the folder names in alphabetical order: angry, disgust, fear, happy, neutral, sad, surprise.