Fer Mek Checkpoints

--- license: mit tags: - facial-expression-recognition - image-classification - pytorch - resnet - imbalanced-classification library_name: pytorch --- # 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](https://arxiv.org/abs/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 | File | Description | Test acc | Mean-class | Backbone | |---|---|:-:|:-:|:-:| | `mek_resnet18_rafdb_msceleb_best.pth` | RAF-DB - MEK ResNet-18 - MS-Celeb-1M backbone (headline best, verified) | 0.8641 | 0.8137 | MS-Celeb-1M | | `mek_resnet18_rafdb_imagenet_best.pth` | RAF-DB - MEK ResNet-18 - ImageNet backbone | 0.8585 | 0.7910 | ImageNet | | `mek_resnet34_rafdb_imagenet_best.pth` | RAF-DB - MEK ResNet-34 - ImageNet backbone (best overall acc) | 0.8657 | 0.7892 | ImageNet | | `mek_resnet34_fer2013_imagenet_best.pth` | FER-2013 - MEK ResNet-34 - ImageNet backbone (best FER, verified) | 0.7205 | 0.6960 | ImageNet | | `mek_resnet18_fer2013_imagenet_best.pth` | FER-2013 - MEK ResNet-18 - ImageNet backbone | 0.7081 | 0.6928 | ImageNet | | `mek_webcam_resnet18_rafdb_clahe_facecrop_best.pth` | RAF-DB - webcam-robust MEK ResNet-18 - MS-Celeb-1M + EMA + face-crop/CLAHE (demo.py) | 0.8625 | 0.8083 | MS-Celeb-1M | All files are `MEKResNet` `state_dict`s (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 ```python 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], Im...

Source context: 0 downloads · 0 likes · Pipeline image-classification · Library pytorch · Repo ToiTenBao/fer-mek-checkpoints