Model Catalog

This catalog contains prebuilt AI models optimized for SiMa AI accelerators and other embedded platforms. These models are designed for fast inference, efficient processing, and real-world deployment across various AI domains such as image classification, object detection, pose estimation, depth estimation, and anomaly detection. The catalog is organized into major AI categories, helping developers quickly find the right models for their needs.

Image Classification Models

These models are optimized for image classification, helping identify objects, scenes, and patterns in images.

Image Classification Models
Model Name	Description
alexnet	Classic CNN model for image classification
densenet121_12	DenseNet-121 variant for classification
densenet121_9	Optimized DenseNet-121 for edge AI
densenet_121	Standard DenseNet-121 for deep feature extraction
densenet_161	Deeper DenseNet for high-accuracy classification
densenet_169	Balanced DenseNet variant for classification
densenet_201	Large DenseNet model for advanced recognition
efficientnet-lite4-11	EfficientNet-Lite for mobile and embedded devices
efficientnet-v2-b0	EfficientNetV2 with optimized feature extraction
efficientnet-v2-m	Medium variant of EfficientNetV2 for accuracy
googlenet	Inception-based model for image classification
regnet_x_1.6gf	RegNet optimized for low-power image tasks
regnet_x_3.2gf	Scalable RegNet model with improved accuracy
regnet_y_8gf	High-performance RegNet-Y variant
resnet50	Standard ResNet-50 for robust classification
resnet101_32x8d	Wider ResNet-101 for detailed feature extraction
vgg16	Popular VGG model for classification
vgg19_bn	Batch-normalized VGG19 for improved convergence
wide-resnet-101	Wide ResNet for handling complex datasets

Object Detection Models

These models are designed for real-time object detection, tracking multiple objects in images and videos.

Object Detection Models
Model Name	Description
centernet	CenterNet for efficient object detection
yolov3	Classic YOLOv3 model for real-time detection
yolov3_tiny	Lightweight YOLOv3 for edge devices
yolov7	High-performance YOLO model with fast inference
yolov7x	Extended YOLOv7 with improved accuracy
yolov8l	Large variant of YOLOv8 for high-precision detection
yolov8n	Nano YOLOv8 optimized for low-power devices
yolov9c	Experimental YOLOv9 variant for advanced tasks
yolox_s	Small YOLOX variant for edge deployment
yolox_x	Extra-large YOLOX model for best performance

Pose Estimation Models

These models are used for human pose estimation, detecting keypoints in body movement and gestures.

Pose Estimation Models
Model Name	Description
open_pose	Human pose estimation for activity tracking
hrnet_w32	High-resolution pose estimation model
face_landmark	Facial keypoint detection for AR applications
fld_68landmarks	68-point facial landmark detection
regnet_x_32gf	High-performance backbone for pose tasks

Depth Estimation Models

These models estimate depth information from single or stereo images, useful for 3D reconstruction and robotics.

Depth Estimation Models
Model Name	Description
depth_anything_v2_vits	General-purpose depth estimation model
midas_v21_small_256	MiDaS depth estimation optimized for efficiency
optimized_midas	High-speed depth estimation with accuracy balance
lightstereo	Lightweight stereo depth estimation
yolov7_gelan_c_seg	YOLO-based depth and segmentation model

Anomaly Detection & Feature Extraction

These models are used for detecting anomalies, identifying defects, and learning representations.

Anomaly Detection & Feature Extraction
Model Name	Description
efficient_ad	Efficient Anomaly Detection for industrial use
fastflow_demo	Fast anomaly detection for real-time applications
fastflow_mvtec	MVTec-trained FastFlow model for defect detection
mnist_cnn	CNN model trained on the MNIST dataset
reid	Person re-identification model for surveillance
stfpm_mvtec	Self-teaching feature pyramid anomaly detection