HyperAI
HyperAI
Home
Console
Docs
News
Papers
Tutorials
Datasets
Wiki
SOTA
LLM Models
GPU Leaderboard
Events
Search
About
Terms of Service
Privacy Policy
English
HyperAI
HyperAI
Toggle Sidebar
Search the site…
⌘
K
Command Palette
Search for a command to run...
Console
Home
SOTA
Facial Landmark Detection
Facial Landmark Detection On 300W
Facial Landmark Detection On 300W
Metrics
NME
Results
Performance results of various models on this benchmark
Columns
Model Name
NME
Paper Title
3DDFA
7.01
Face Alignment Across Large Poses: A 3D Solution
Pose-Invariant
6.30
Pose-Invariant Face Alignment with a Single CNN
CFSS
5.76
Face Alignment Across Large Poses: A 3D Solution
SAN GT
3.98
Style Aggregated Network for Facial Landmark Detection
TS3
3.49
Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection
Adaloss
3.31
Adaloss: Adaptive Loss Function for Landmark Localization
CNN-CRF (Inter-ocular Norm)
3.30
Deep Structured Prediction for Facial Landmark Detection
CHR2C (Inter-ocular Norm)
3.3
Cascade of Encoder-Decoder CNNs with Learned Coordinates Regressor for Robust Facial Landmarks Detection
DCFE (Inter-ocular Norm)
3.24
A Deeply-initialized Coarse-to-fine Ensemble of Regression Trees for Face Alignment
3DDE (Inter-ocular Norm)
3.13
Face Alignment using a 3D Deeply-initialized Ensemble of Regression Trees
AnchorFace
3.12
AnchorFace: An Anchor-based Facial Landmark Detector Across Large Poses
SPIGA (Inter-ocular Norm)
2.99
Shape Preserving Facial Landmarks with Graph Attention Networks
FiFA
2.89
Fiducial Focus Augmentation for Facial Landmark Detection
D-ViT
2.85
Cascaded Dual Vision Transformer for Accurate Facial Landmark Detection
FPN
-
FacePoseNet: Making a Case for Landmark-Free Face Alignment
0 of 15 row(s) selected.
Previous
Next
HyperAI
HyperAI
Home
Console
Docs
News
Papers
Tutorials
Datasets
Wiki
SOTA
LLM Models
GPU Leaderboard
Events
Search
About
Terms of Service
Privacy Policy
English
HyperAI
HyperAI
Toggle Sidebar
Search the site…
⌘
K
Command Palette
Search for a command to run...
Console
Home
SOTA
Facial Landmark Detection
Facial Landmark Detection On 300W
Facial Landmark Detection On 300W
Metrics
NME
Results
Performance results of various models on this benchmark
Columns
Model Name
NME
Paper Title
3DDFA
7.01
Face Alignment Across Large Poses: A 3D Solution
Pose-Invariant
6.30
Pose-Invariant Face Alignment with a Single CNN
CFSS
5.76
Face Alignment Across Large Poses: A 3D Solution
SAN GT
3.98
Style Aggregated Network for Facial Landmark Detection
TS3
3.49
Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection
Adaloss
3.31
Adaloss: Adaptive Loss Function for Landmark Localization
CNN-CRF (Inter-ocular Norm)
3.30
Deep Structured Prediction for Facial Landmark Detection
CHR2C (Inter-ocular Norm)
3.3
Cascade of Encoder-Decoder CNNs with Learned Coordinates Regressor for Robust Facial Landmarks Detection
DCFE (Inter-ocular Norm)
3.24
A Deeply-initialized Coarse-to-fine Ensemble of Regression Trees for Face Alignment
3DDE (Inter-ocular Norm)
3.13
Face Alignment using a 3D Deeply-initialized Ensemble of Regression Trees
AnchorFace
3.12
AnchorFace: An Anchor-based Facial Landmark Detector Across Large Poses
SPIGA (Inter-ocular Norm)
2.99
Shape Preserving Facial Landmarks with Graph Attention Networks
FiFA
2.89
Fiducial Focus Augmentation for Facial Landmark Detection
D-ViT
2.85
Cascaded Dual Vision Transformer for Accurate Facial Landmark Detection
FPN
-
FacePoseNet: Making a Case for Landmark-Free Face Alignment
0 of 15 row(s) selected.
Previous
Next