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afrisign-encoder-best-models
luciayen/afrisign-encoder-best-models
afrisign-encoder-best-models is a machine learning model from luciayen. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as other.
This repository contains the best focused AfriSign Encoder checkpoints for the KSL + CASL + NSL contribution. The models are trained for African sign-language recognition using shared multilingual representations acro…
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Updated Aug 12, 2026
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At a glance
- Library
- pytorch
- License
- other
- Access
- Public
- Created
- Aug 12, 2026
- Updated
- Aug 12, 2026
- SHA
- 3d027624
Model card
From the Hugging Face model README
AfriSign Encoder Best Models
This repository contains the best focused AfriSign Encoder checkpoints for the KSL + CASL + NSL contribution. The models are trained for African sign-language recognition using shared multilingual representations across pose landmarks, RGB video frames, and static sign images.
What Is Included
| Folder | Model | Checkpoint | Result file |
|---|---|---|---|
models/e9_5_research_v2_pose | AfriSign Encoder E9.5 Research-v2 Pose-only | pytorch_model.pt | results.json |
models/e9_6_research_v2_rgb_image | AfriSign Encoder E9.6 Research-v2 RGB/image-only | pytorch_model.pt | results.json |
models/e9_7_research_v2_multimodal | AfriSign Encoder E9.7 Research-v2 RGB+pose/image | pytorch_model.pt | results.json |
Main Results
| Experiment | Model | Modality | Test macro-F1 | Note |
|---|---|---|---|---|
| E9.1 | naive pooled pose-only | pose | 32.00% | baseline |
| E9.2 | proposed pose-only | pose | 79.75% | language-aware model |
| E9.3 | proposed RGB/image-only | RGB/image | 57.18% | visual-only model |
| E9.4 | proposed RGB+pose/image | multimodal | 69.87% | first multimodal model |
| E9.5 | research-v2 pose-only | pose | 80.26% | best overall focused model |
| E9.6 | research-v2 RGB/image-only | RGB/image | 57.58% | best visual-only model |
| E9.7 | research-v2 RGB+pose/image | multimodal | 71.82% | best multimodal model |
Methodology
The focused benchmark uses KSL, CASL, and NSL streams:
- CASL signer-independent word-level pose landmarks.
- KSL word-level pose landmarks.
- NSL image-level pose landmarks.
- CASL signer-independent RGB video frames.
- KSLC/KSL static RGB images.
The proposed encoder uses shared modality encoders with language/task conditioning, task-specific heads, balanced sampling, supervised contrastive learning, and metric-learning heads. The research-v2 models add motion-aware pose features, multi-scale temporal modeling, ArcFace/CosFace-style heads, and center-loss regularization.
Loading A Checkpoint
import torch
ckpt = torch.load("models/e9_5_research_v2_pose/pytorch_model.pt", map_location="cpu")
print(ckpt.keys())
The checkpoints store the model state dict, normalization statistics, task
metadata, language mappings, and training arguments. Use the code in
code/experiments/ or the GitHub repository to rebuild the architecture before
loading the state dict.
Project
GitHub: https://github.com/Yen-hub/AfriSignEncoder
Model repo: https://huggingface.co/luciayen/afrisign-encoder-best-models
- Library
- pytorch
- License
- other
- Created
- Aug 12, 2026
- Updated
- Aug 12, 2026