Vision Model List
These are the “well-known” computer vision models that wasmVision can automatically download and run.
candy-8
Candy model for fast neural style transfer (opset 8)
For more information see:
https://github.com/onnx/models/tree/main/validated/vision/style_transfer/fast_neural_style
candy-9
Candy model for fast neural style transfer (opset 8)
For more information see:
https://github.com/onnx/models/tree/main/validated/vision/style_transfer/fast_neural_style
dexined_2024sep
Dense Extreme Inception Network for Edge Detection (DexiNed), a Convolutional Neural Network (CNN) architecture for edge detection.
For more information see:
https://github.com/opencv/opencv_zoo/blob/main/models/edge_detection_dexined/README.md
and also
https://github.com/xavysp/DexiNed
mobilefacenet_2022july
MobileFaceNet model for Facial Expression Recognition (FER) featuring semi-supervised learning model with 88.27% accuracy.
For more information see https://github.com/opencv/opencv_zoo/blob/main/models/facial_expression_recognition/README.md
mobilefacenet_2022july_int8
MobileFaceNet model for Facial Expression Recognition (quantized)
For more information see https://github.com/opencv/opencv_zoo/blob/main/models/facial_expression_recognition/README.md
mobilefacenet_2022july_int8bq
MobileFaceNet model for Facial Expression Recognition (quantized with bias)
For more information see https://github.com/opencv/opencv_zoo/blob/main/models/facial_expression_recognition/README.md
mosaic-8
Mosaic model for fast neural style transfer (opset 8)
For more information see:
https://github.com/onnx/models/tree/main/validated/vision/style_transfer/fast_neural_style
mosaic-9
Mosaic model for fast neural style transfer (opset 9)
For more information see:
https://github.com/onnx/models/tree/main/validated/vision/style_transfer/fast_neural_style
pointilism-8
Pointilism model for fast neural style transfer (opset 8)
For more information see:
https://github.com/onnx/models/tree/main/validated/vision/style_transfer/fast_neural_style
pointilism-9
Pointilism model for fast neural style transfer (opset 9)
For more information see:
https://github.com/onnx/models/tree/main/validated/vision/style_transfer/fast_neural_style
rain-princess-8
Rain Princess model for fast neural style transfer (opset 8)
For more information see:
https://github.com/onnx/models/tree/main/validated/vision/style_transfer/fast_neural_style
rain-princess-9
Rain Princess model for fast neural style transfer (opset 9)
For more information see:
https://github.com/onnx/models/tree/main/validated/vision/style_transfer/fast_neural_style
udnie-8
Udnie model for fast neural style transfer (opset 8)
For more information see:
https://github.com/onnx/models/tree/main/validated/vision/style_transfer/fast_neural_style
udnie-9
Udnie model for fast neural style transfer (opset 9)
For more information see:
https://github.com/onnx/models/tree/main/validated/vision/style_transfer/fast_neural_style
yolov8l
YOLOv8 real-time object detection model. YOLOv8 was released by Ultralytics on January 10th, 2023, offering cutting-edge performance in terms of accuracy and speed.
This is the YOLOv8l variant.
For more information see:
https://docs.ultralytics.com/models/yolov8/
yolov8m
YOLOv8 real-time object detection model. YOLOv8 was released by Ultralytics on January 10th, 2023, offering cutting-edge performance in terms of accuracy and speed.
This is the YOLOv8m variant.
For more information see:
https://docs.ultralytics.com/models/yolov8/
yolov8n
YOLOv8 real-time object detection model. YOLOv8 was released by Ultralytics on January 10th, 2023, offering cutting-edge performance in terms of accuracy and speed.
This is the YOLOv8n variant.
For more information see:
https://docs.ultralytics.com/models/yolov8/
yolov8s
YOLOv8 real-time object detection model. YOLOv8 was released by Ultralytics on January 10th, 2023, offering cutting-edge performance in terms of accuracy and speed.
This is the YOLOv8s variant.
For more information see:
https://docs.ultralytics.com/models/yolov8/
yolov8x
YOLOv8x model for object detection
YOLOv8 real-time object detection model. YOLOv8 was released by Ultralytics on January 10th, 2023, offering cutting-edge performance in terms of accuracy and speed.
This is the YOLOv8s variant.
For more information see:
https://docs.ultralytics.com/models/yolov8/
yolox
YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. YOLOX is a high-performing object detector, an improvement to the existing YOLO series. YOLO series are in constant exploration of techniques to improve the object detection techniques for optimal speed and accuracy trade-off for real-time applications.
For more information see:
https://github.com/opencv/opencv_zoo/tree/main/models/object_detection_yolox
yunet_2023mar
YuNet, a light-weight, fast and accurate face detection model, which achieves 0.834(AP_easy), 0.824(AP_medium), 0.708(AP_hard) on the WIDER Face validation set.
For more information see:
https://github.com/opencv/opencv_zoo/blob/main/models/face_detection_yunet/README.md