CASL TransSLR
This repository contains the state-of-the-art Transformer architecture for the CASL (Chinese-American Sign Language) Research Project. This specific version (v4) is optimized for Signer Independence, meaning it is...
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 33,1–99,3 MB
- Versão
- vPytorch model
- Atualizado
- 10 de out. de 2026
- Fonte
- Hugging Face
Descrição da fonte
🤟 CASL-TransSLR: Robust Sign Language Transformer
SignVLM-v4 Champion Model
This repository contains the state-of-the-art Transformer architecture for the CASL (Chinese-American Sign Language) Research Project. This specific version (v4) is optimized for Signer Independence, meaning it is designed to recognize signs from people the model has never seen before.
📊 Performance Metrics (Unseen Signers)
Evaluated on 862 files from independent signers:
| Metric | Value |
|---|---|
| Overall Accuracy | 80.39% |
| Weighted F1-Score | 78.33% |
| Classes | 60 Signs |
🏗️ Architecture Insight
The model uses a hybrid Feature Extractor + Transformer Encoder approach:
- Feature Extractor: A Linear layer (225 → 512) followed by Temporal BatchNorm (64 frames) to normalize motion across time.
- Transformer: 4 Layers of Multi-Head Attention ($d_{model}=512$, $n_{head}=8$, $ff_{dim}=1024$).
- Classifier: A 2-layer MLP with Dropout (0.5) for robust generalization.
⚙️ Pre-processing Requirements
IMPORTANT: This model expects landmarks to be normalized. If you pass raw MediaPipe coordinates, the accuracy will drop significantly.
- Centering: Translate all points relative to the Mid-Hip (Point 0).
- Scaling: Normalize by the Shoulder-to-Shoulder distance to account for different body types.
- Shape: Input must be a tensor of shape
(Batch, 64, 225).
🚀 How to Load and Use
import torch
from huggingface_hub import hf_hub_download
import importlib.util
# 1. Download files
repo_id = "luciayen/CASL-TransSLR"
model_bin = hf_hub_download(repo_id=repo_id, filename="pytorch_model.bin")
model_script = hf_hub_download(repo_id=repo_id, filename="model.py")
# 2. Import architecture
spec = importlib.util.spec_from_file_location("model_arch", model_script)
model_arch = importlib.util.module_from_spec(spec)
spec.loader.exec_module(model_arch)
# 3. Initialize & Load
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model_arch.SignVLM().to(device)
model.load_state_dict(torch.load(model_bin, map_location=device))
model.eval()
Fontes
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Fontes
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Artefatos de modelo
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Artefatos de modelo
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Trechos de fonte
2 trechos--- license: mit library_name: pytorch tags: - sign-language-recognition - transformer - mediapipe - skeleton-based-action-recognition datasets: - luciayen/CASL-W60-Landmarks metrics: - accuracy pipeline_tag: video-classification --- # 🤟 CASL-TransSLR: Robust Sign Language Transformer **SignVLM-v4 Champion Model** This repository contains the state-of-the-art Transformer architecture for the **CASL (Chinese-American Sign Language) Research Project**. This specific version (v4) is optimized for **Signer Independence**, meaning it is designed to recognize signs from people the model has never seen before. ## 📊 Performance Metrics (Unseen Signers) Evaluated on **862 files** from independent signers: | Metric | Value | | :--- | :--- | | **Overall Accuracy** | **80.39%** | | **Weighted F1-Score** | **78.33%** | | **Classes** | 60 Signs | ## 🏗️ Architecture Insight The model uses a hybrid **Feature Extractor + Transformer Encoder** approach: * **Feature Extractor:** A Linear layer (225 → 512) followed by **Temporal BatchNorm** (64 frames) to normalize motion across time. * **Transformer:** 4 Layers of Multi-Head Attention ($d_{model}=512$, $n_{head}=8$, $ff_{dim}=1024$). * **Classifier:** A 2-layer MLP with Dropout (0.5) for robust generalization. ## ⚙️ Pre-processing Requirements **IMPORTANT:** This model expects landmarks to be normalized. If you pass raw MediaPipe coordinates, the accuracy will drop significantly. 1. **Centering:** Translate all points relative to the **Mid-Hip** (Point 0). 2. **Scaling:** Normalize by the **Shoulder-to-Shoulder** distance to account for different body types. 3. **Shape:** Input must be a tensor of shape `(Batch, 64, 225)`. ## 🚀 How to Load and Use ```python import torch from huggingface_hub import hf_hub_download import importlib.util # 1. Download files repo_id = "luciayen/CASL-TransSLR" model_bin = hf_hub_download(repo_id=repo_id, filename="pytorch_model.bin") model_script = hf_hub_download(repo_id=repo_id, filename="model.py") # 2. Import architecture spec = importlib.util.spec_from_file_location("model_arch", model_script) model_arch = importlib.util.module_from_spec(spec) spec.loader.exec_module(model_arch) # 3. Initialize & Load device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = model_arch.SignVLM().to(device) model.load_state_dict(torch.load(model_bin, map_location=device))...
Source context: 0 downloads · 0 likes · Pipeline video-classification · Library pytorch · Repo luciayen/CASL-TransSLR
