PINTOmodelzoo

Please read the contents of the LICENSE file located directly under each folder before using the model. My model conversion scripts are released under the MIT license, but the license of the source model itself is subject to the license of the provider repository.

Contributors

Made with contrib.rocks.

A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.

TensorFlow Lite, OpenVINO, CoreML, TensorFlow.js, TF-TRT, MediaPipe, ONNX [.tflite, .h5, .pb, saved_model, tfjs, tftrt, mlmodel, .xml/.bin, .onnx]

I have been working on quantization of various models as a hobby, but I have skipped the work of making sample code to check the operation because it takes a lot of time. I welcome a pull request from volunteers to provide sample code. :smile:

[Note Jan 05, 2020] Currently, the MobileNetV3 backbone model and the Full Integer Quantization model do not return correctly.

[Note Jan 08, 2020] If you want the best performance with RaspberryPi4/3, install Ubuntu 19.10 aarch64 (64bit) instead of Raspbian armv7l (32bit). The official Tensorflow Lite is performance tuned for aarch64. On aarch64 OS, performance is about 4 times higher than on armv7l OS.

My article

• [[Japanese ver.] [Tensorflow Lite] Various Neural Network Model quantization methods for Tensorflow Lite (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization, EdgeTPU). As of May 05, 2020.](https://qiita.com/PINTO/items/008c54536fca690e0572)

• [[English ver.] [Tensorflow Lite] Various Neural Network Model quantization methods for Tensorflow Lite (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization, EdgeTPU). As of May 05, 2020.](https://qiita.com/PINTO/items/865250ee23a15339d556)

• Conversion of PyTorch->ONNX->OpenVINO IR model to Tensorflow savedmodel / h5 / tflite / pb

• [[English] Converting PyTorch, ONNX, Caffe, and OpenVINO (NCHW) models to Tensorflow / TensorflowLite (NHWC) in a snap - Qiita](https://qiita.com/PINTO/items/ed06e03eb5c007c2e102)

• [[TF2 Object Detection] Converting SSD models into .tflite uint8 format #9371](https://github.com/tensorflow/models/issues/9371#issuecomment-735252080)

• tf.image.resizeを含むFull Integer Quantization (.tflite)モデルのEdgeTPUモデルへの変換後の推論時に発生する "main.ERROR - Only float32 and uint8 are supported currently, got -xxx.Node number n (op name) failed to invoke" エラーの回避方法

• A standalone 2MB installer for TensorflowLite v2.4.0-rc4 and a libedgetpu.so.1 build intended to run on a Yocto-generated environment

• [[Japanese] Custom Operation入りのtfliteを逆コンバートしてJSON化し標準OPへ置き換えたうえでtfliteを再生成する方法](https://zenn.dev/pinto0309/articles/9d316860f8d418)

• Generate savedmodel, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite, ONNX, OpenVINO, Myriad Inference Engine blob and .pb from .tflite.

• Add a custom OP to the TFLite runtime to build the whl installer (for Python), MaxPoolingWithArgmax2D, MaxUnpooling2D, Convolution2DTransposeBias

• ONNX to JSON

• JSON to ONNX

• Steps to merge two ONNX files into one

• Inverse Quantization of tflite's Sparse Tensor Densify to Refine a Clean Float32 Model

• Replace PyTorch's argsort with sort and export to ONNX

• A very simple tool that compresses the overall size of the ONNX model by aggregating duplicate constant values as much as possible. Simple Constant value Shrink for ONNX.

• Simple node deletion tool for onnx. I only test very miscellaneous and limited patterns as a hobby.

• A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or simply to separate onnx files to any size you want. Simple Network Extraction for ONNX.

• Simple tool to combine onnx models. Simple Network Combine Tool for ONNX.

• A set of simple tools for splitting, merging, OP deletion, size compression, rewriting attributes and constants, OP generation, and change opset for ONNX models.

• Self-Created Tools to convert ONNX files (NCHW) to TensorFlow format (NHWC). The purpose of this tool is to solve the massive Transpose extrapolation problem in onnx-tensorflow (onnx-tf).

List of pre-quantized models

\* WQ = Weight Quantization \** OV = OpenVINO IR \* CM = CoreML \** DQ = Dynamic Range Quantization

1. Image Classification

|No.|Model Name|Link|FP32|FP16|INT8|DQ|TPU|WQ|OV|CM|TFJS|TF-TRT|ONNX|Remarks| |:-|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-| |004|Efficientnet|■■■|⚫||⚫|||⚫||||||| |010|Mobilenetv3|■■■|⚫||⚫|||⚫||||||| |011|Mobilenetv2|■■■|⚫||⚫|⚫||⚫||||||| |016|Efficientnet-lite|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||| |070|age-gender-recognition|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|| |083|PersonReidentification|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|248,277,286,287,288,300| |087|DeepSort|■■■|⚫|⚫|⚫|||⚫|⚫|⚫||⚫|⚫|| |124|person-attributes-recognition-crossroad-0230|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|| |125|person-attributes-recognition-crossroad-0234|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|| |126|person-attributes-recognition-crossroad-0238|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|| |175|face-recognition-resnet100-arcface-onnx|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|⚫|RGB/BGR,112x112,[1,512]| |187|vehicle-attributes-recognition-barrier-0039|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|72x72| |188|vehicle-attributes-recognition-barrier-0042|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|72x72| |191|anti-spoof-mn3|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫||⚫|128x128| |192|open-closed-eye-0001|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫||⚫|32x32| |194|facerecognizerfast|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|112x112| |195|personreidyoutu|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|256x128, ReID| |199|NSFW|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|224x224| |244|FINNger|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||⚫|96x96| |256|SFace|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫||⚫|112x112| |257|PiCANet|■■■|||||||⚫||||⚫|BDDA,SAGE/224x224| |259|EmotionFERPlus|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫||⚫|64x64| |290|AdaFace|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||⚫|112x112| |317|MobileOne|■■■|||||||||||⚫|224x224| |346|facialexpressionrecognitionmobilefacenet|■■■|⚫|⚫|⚫|⚫||⚫|||||⚫|112x112| |379|PP-LCNetV2|■■■|⚫|⚫|||||||||⚫|224x224| |429|OSNet|■■■|||||||||||⚫|256x128, ReID| |430|FastReID|■■■|||||||||||⚫|384x128, ReID| |431|NITEC|■■■|||||||||||⚫|224x224, Gaze Estimation| |432|face-reidentification-retail-0095|■■■|⚫|⚫|||||||||⚫|128x128, FaceReID| |451|DAN|■■■|||||||||||⚫|224x224, Facial Expression| |452|FairFace|■■■|||||||||||⚫|224x224, Face Attribute| |453|FairDAN|■■■|||||||||||⚫|224x224, Face Attribute + Facial Expression| |462|Gaze-LLE|■■■|||||||||||⚫|448x448, Attention| |474|Gaze-LLE-DINOv3|■■■|||||||||||⚫|640x640,416x416,320x320, Attention| |475|VSDLM|■■■|||||||||||⚫|30x48, Lip motion| |476|OCEC|■■■|||||||||||⚫|24x40, Wink/Blink| |477|PGC|■■■|||||||||||⚫|32x32, Pointing| |478|SC|■■■|||||||||||⚫|32x24, Sitting| |479|PUC|■■■|||||||||||⚫|32x24, Phone Usage Classifier| |480|HSC|■■■|||||||||||⚫|48x48, Happy smile classifier| |481|WHC|■■■|||||||||||⚫|4x32x32,6x32x32,8x32x32, Waving Hand Classification| |483|LVFace|■■■|||||||||||⚫|Nx3x112x112, Cosine similarity| |484|TransFace|■■■|||||||||||⚫|Nx3x112x112, Cosine similarity| |486|MWC|■■■|||||||||||⚫|Nx3x48x48, Mask wearing classifier| |489|Glasses-Detector|■■■|||||||||||⚫|Glasses| |491|SGC|■■■|||||||||||⚫|Sunglasses| |492|HHC|■■■|||||||||||⚫|Hat| |494|BPC|■■■|||||||||||⚫|Background Plain classification| |495|Comprehensive-Head-Classification|■■■|⚫||||||||||⚫|Comprehensive head classification| |497|PPC|■■■|||||||||||⚫|Binary classification to determine whether the subject is holding a smartphone| |502|PersonViT|■■■|||||||||||⚫|Person Re-Identification| |503|CDNet|■■■|||||||||||⚫|Person Re-Identification|

2. 2D Object Detection

|No.|Model Name|Link|FP32|FP16|INT8|TPU|DQ|WQ|OV|CM|TFJS|TF-TRT|ONNX|Remarks| |:-|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-| |002|Mobilenetv3-SSD|■■■|⚫||⚫|||⚫||⚫||||| |006|Mobilenetv2-SSDlite|■■■|⚫||⚫|⚫||⚫||||||| |008|MaskRCNNInceptionv2|■■■|⚫|⚫||||⚫||||||| |018|EfficientDet|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|| |023|Yolov3-nano|■■■|⚫|⚫||||⚫|||||⚫|| |024|Yolov3-lite|■■■|⚫|⚫|⚫|||⚫|⚫||||⚫|| |031|Yolov4|■■■|⚫|⚫|⚫|⚫||⚫|||||⚫|| |034|SSDMobilenetv2mnasfpn|■■■|⚫|⚫|⚫|⚫||⚫|||||⚫|| |038|SSDliteMobileDetedgetpu|■■■|⚫|⚫|⚫|⚫||⚫|||⚫|⚫|⚫|| |039|SSDliteMobileDetcpu|■■■|⚫|⚫|⚫|||⚫||||||| |042|Centernet|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|| |045|SSDMobilenetv2oidv4|■■■|⚫|⚫|⚫|⚫||⚫|||⚫|||| |046|Yolov4-tiny|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|| |047|SpineNetMB49|■■■|⚫|⚫|⚫|||⚫|||||⚫|Mobile RetinaNet| |051|EastTextDetection|■■■|⚫|⚫|⚫|⚫||⚫|⚫||⚫|⚫|⚫|| |054|KNIFT|■■■|⚫|⚫|⚫|||⚫|⚫||||⚫|MediaPipe| |056|TextBoxes++ with dense blocks, separable convolution and Focal Loss|■■■|⚫|⚫|⚫|||⚫|⚫|⚫||⚫|⚫|| |058|keras-retinanet|■■■|⚫|⚫||||⚫|⚫||⚫|⚫|⚫|resnet50cocobest_v2.1.0.h5,320x320| |072|NanoDet|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|issue #274| |073|RetinaNet|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|| |074|Yolact|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|| |085|YolactEdge|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|21/10/05 new MobileNetV2(550x550)| |089|DETR|■■■|⚫|⚫|⚫|||⚫|||||⚫|256x256| |103|EfficientDetlite|■■■|⚫|⚫|⚫|⚫||⚫|⚫||⚫|⚫|⚫|lite0,lite1,lite2,lite3,lite4| |116|DroNet|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|DroNet,DroNetV3| |123|YOLOR|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫||⚫|ssss_s2d/320x320,640x640,960x960,1280x1280| |132|YOLOX|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫||⚫|nano,tiny,s,m,l,x/256x320,320x320,416x416,480x640,544x960,736x1280,1088x1920| |143|RAPiD|■■■|⚫|⚫|⚫|||⚫|⚫||||⚫|Fisheye, cepdof/habbof/mw_r, 608x608/1024x1024| |145|textdetectiondb|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|480x640| |151|objectdetectionmobileobjectlocalizer|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|192x192| |169|spaghettinetedgetpu|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫||⚫|⚫|⚫|320x320,S/M/L| |174|PP-PicoDet|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|⚫|S/M/L,320x320/416x416/640x640| |178|vehicle-detection-0200|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|256x256,PriorBoxClustered->ndarray(0.npy)| |179|person-detection-0202|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|512x512,PriorBoxClustered->ndarray(0.npy)| |183|pedestrian-detection-adas-0002|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|384x672,PriorBox->ndarray(0.npy)| |184|pedestrian-and-vehicle-detector-adas-0001|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|384x672,PriorBox->ndarray(0.npy)| |185|person-vehicle-bike-detection-crossroad-0078|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|⚫|1024x1024,PriorBoxClustered->ndarray(0.npy)| |186|person-vehicle-bike-detection-crossroad-1016|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|512x512,PriorBoxClustered->ndarray(0.npy)| |189|vehicle-license-plate-detection-barrier-0106|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|300x300,PriorBoxClustered->ndarray(0.npy)| |190|person-detection-asl-0001|■■■|⚫|⚫|⚫||⚫|⚫|⚫||||⚫|320x320| |197|yolact-resnet50-fpn|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|⚫|RGB,550x550| |198|YOLOF|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|⚫|BGR/RGB,608x608| |221|YOLACT-PyTorch|■■■|⚫|⚫|⚫||⚫|⚫|⚫||⚫|⚫|⚫|180x320,240x320,320x480,480x640,544x544,720x1280| |226|CascadeTableNet|■■■|⚫|⚫|||⚫|⚫|⚫||||⚫|General,320x320 only| |262|ByteTrack|■■■|||||||||||⚫|YOLOX/nano,tiny,s,m,l,x,mot17,ablation/128x320,192x320,192x448,192x640,256x320,256x448,256x640,384x640,512x1280,736x1280| |264|objectlocalizationnetwork|■■■|||||||||||⚫|180x320,240x320,270x480,360x480,360x480,360x640,480x640,720x1280| |307|YOLOv7|■■■|⚫|⚫|⚫||⚫|⚫|⚫||⚫||⚫|YOLOv7,YOLOv7-tiny| |308|FastestDet|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||⚫|180x320,256x320,320x480,352x352,352x640,480x640,736x1280| |329|YOLOX-PAI|■■■|||||||||||⚫|| |332|CrowdDet|■■■|||||||||||⚫|| |334|DAMO-YOLO|■■■|||||||||||⚫|| |336|PP-YOLOE-Plus|■■■|||||||||||⚫|| |337|FreeYOLO|■■■|||||||||||⚫|| |341|YOLOv6|■■■|⚫|⚫|⚫||⚫|⚫|||||⚫|| |356|EdgeYOLO|■■■|||||||||||⚫|| |376|RT-DETR|■■■|||||||||||⚫|ResNet50,ResNet101,HgNetv2-L,HgNetv2-X| |386|narutohandsigndetection|■■■|⚫|⚫|⚫||⚫||||||⚫|| |422|Gold-YOLO-Head-Hand|■■■|||||||||||⚫|Head,Hand| |424|Gold-YOLO-Body|■■■|||||||||||⚫|Body| |425|Gold-YOLO-Body-Head-Hand|■■■|||||||||||⚫|Body,Head,Hand| |426|YOLOX-Body-Head-Hand|■■■|⚫|⚫|⚫||||||||⚫|Body,Head,Hand, tflite float16 XNNPACK boost (ARMv8.2)| |434|YOLOX-Body-Head-Hand-Face|■■■|||||||||||⚫|Body,Head,Hand,Face| |441|YOLOX-Body-Head-Hand-Face-Dist|■■■|||||||||||⚫|Body,Head,Hand,Face,Complex Distorted| |442|YOLOX-Body-Head-Face-HandLR-Dist|■■■|||||||||||⚫|Body,Head,Hands,Left-Hand,Right-Hand,Face,Complex Distorted| |444|YOLOX-Foot-Dist|■■■|||||||||||⚫|Foot,Complex Distorted| |445|YOLOX-Body-Head-Face-HandLR-Foot-Dist|■■■|||||||||||⚫|Body,Head,Face,Hands,Left-Hand,Right-Hand,Foot,Complex Distorted| |446|YOLOX-Body-With-Wheelchair|■■■|||||||||||⚫|Body with WheelChair| |447|YOLOX-Wholebody-with-Wheelchair|■■■|||||||||||⚫|Wholebody with WheelChair| |448|YOLOX-Eye-Nose-Mouth-Ear|■■■|||||||||||⚫|| |449|YOLOX-WholeBody12|■■■|||||||||||⚫|Body,BodyWithWheelchair,Head,Face,Eye,Nose,Mouth,Ear,Hand,Hand-Left,Hand-Right,Foot| |450|YOLOv9-Wholebody-with-Wheelchair|■■■|||||||||||⚫|Wholebody with WheelChair| |454|YOLOv9-Wholebody13|■■■|||||||||||⚫|Body,BodyWithWheelchair,BodyWithCrutches,Head,Face,Eye,Nose,Mouth,Ear,Hand,Hand-Left,Hand-Right,Foot| |455|YOLOv9-Gender|■■■|||||||||||⚫|Body,Male,Female| |456|YOLOv9-Wholebody15|■■■|||||||||||⚫|Body,Male,Female,BodyWithWheelchair,BodyWithCrutches,Head,Face,Eye,Nose,Mouth,Ear,Hand,Hand-Left,Hand-Right,Foot| |457|YOLOv9-Wholebody17|■■■|||||||||||⚫|Body,Male,Adult,Child,Female,BodyWithWheelchair,BodyWithCrutches,Head,Face,Eye,Nose,Mouth,Ear,Hand,Hand-Left,Hand-Right,Foot| |458|YOLOv9-Discrete-HeadPose-Yaw|■■■|||||||||||⚫|Head,Front,Right-Front,Right-Side,Right-Back,Back,Left-Back,Left-Side,Left-Front| |459|YOLOv9-Wholebody25|■■■|||||||||||⚫|Body,Adult,Child,Male,Female,BodywithWheelchair,BodywithCrutches,Head,Front,RightFront,RightSide,RightBack,Back,LeftBack,LeftSide,LeftFront,Face,Eye,Nose,Mouth,Ear,Hand,HandLeft,HandRight,Foot| |460|RT-DETRv2-Wholebody25|■■■|||||||||||⚫|Body,Adult,Child,Male,Female,BodywithWheelchair,BodywithCrutches,Head,Front,RightFront,RightSide,RightBack,Back,LeftBack,LeftSide,LeftFront,Face,Eye,Nose,Mouth,Ear,Hand,HandLeft,HandRight,Foot| |461|YOLOv9-Phone|■■■|||||||||||⚫|Phone| |463|YOLOv9-Shoulder-Elbow-Knee|■■■|||||||||||⚫|Shoulder,Elbow,Knee| |464|YOLOv9-Wholebody28|■■■|||||||||||⚫|Body,Adult,Child,Male,Female,BodywithWheelchair,BodywithCrutches,Head,Front,RightFront,RightSide,RightBack,Back,LeftBack,LeftSide,LeftFront,Face,Eye,Nose,Mouth,Ear,Shoulder,Elbow,Hand,HandLeft,HandRight,Knee,Foot| |465|DEIM-Wholebody28|■■■|||||||||||⚫|Body,Adult,Child,Male,Female,BodywithWheelchair,BodywithCrutches,Head,Front,RightFront,RightSide,RightBack,Back,LeftBack,LeftSide,LeftFront,Face,Eye,Nose,Mouth,Ear,Shoulder,Elbow,Hand,HandLeft,HandRight,Knee,Foot| |468|YOLOv9-Wholebody28-Refine|■■■|||||||||||⚫|Body,Adult,Child,Male,Female,BodywithWheelchair,BodywithCrutches,Head,Front,RightFront,RightSide,RightBack,Back,LeftBack,LeftSide,LeftFront,Face,Eye,Nose,Mouth,Ear,Shoulder,Elbow,Hand,HandLeft,HandRight,Knee,Foot| |471|YOLO-Wholebody34|■■■|||||||||||⚫|body,adult,child,male,female,bodywithwheelchair,bodywithcrutches,head,front,right-front,right-side,right-back,back,left-back,left-side,left-front,face,eye,nose,mouth,ear,collarbone,shoulder,solarplexus,elbow,wrist,hand,handleft,handright,abdomen,hipjoint,knee,ankle,foot| |472|DEIMv2-Wholebody34|■■■|||||||||||⚫|body,adult,child,male,female,bodywithwheelchair,bodywithcrutches,head,front,right-front,right-side,right-back,back,left-back,left-side,left-front,face,eye,nose,mouth,ear,collarbone,shoulder,solarplexus,elbow,wrist,hand,handleft,handright,abdomen,hipjoint,knee,ankle,foot| |473|HISDF|■■■|||||||||||⚫|Object Detection x Depth Estimation x Pose Estimation x Instance Segmentation x Binary Segmentation| |482|UHD|■■■|||||||||||⚫|64x64, Object Detection, Human Detection| |485|DEIMv2-Wholebody40|■■■|||||||||||⚫|40 classes| |489|489Glasses-Detector|■■■|||||||||||⚫|glasses|

3. 3D Object Detection

|No.|Model Name|Link|FP32|FP16|INT8|TPU|DQ|WQ|OV|CM|TFJS|TF-TRT|ONNX|Remarks| |:-|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-| |036|Objectron|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|MediaPipe/camera,chair,chair1stage,cup,sneakers,sneakers1stage,ssdmobilenetv2oidv4_fp16| |063|3D BoundingBox estimation for autonomous driving|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|YouTube| |107|SFA3D|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|| |263|EgoNet|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||⚫|| |321|DID-M3D|■■■|||||||||||⚫|| |363|YOLO-6D-Pose|■■■|⚫||||||||⚫||⚫|Texas Instruments ver, PINTO Special ver|

4. 2D/3D Face Detection

|No.|Model Name|Link|FP32|FP16|INT8|TPU|DQ|WQ|OV|CM|TFJS|TF-TRT|ONNX|Remarks| |:-|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-| |025|HeadPoseEstimation|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|| |030|BlazeFace|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|MediaPipe| |032|FaceMesh|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|MediaPipe| |040|DSFDvgg|■■■|⚫|⚫||||⚫||||||| |041|DBFace|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|MobileNetV2/V3, 320x320,480x640,640x960,800x1280| |043|FaceLandmark|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|| |049|IrisLandmark|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|MediaPipe| |095|CenterFace|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|| |096|RetinaFace|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|| |106|WHENet|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫||⚫|Real-time Fine-Grained Estimation for Wide Range Head Pose| |129|SCRFD|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|⚫|All types| |134|head-pose-estimation-adas-0001|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|60x60| |144|YuNet|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|120x160| |227|face-detection-adas-0001|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|384x672,PriorBox->ndarray(0.npy)| |250|Face-Mask-Detection|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫||⚫|PriorBox->ndarray(0.npy)| |282|facelandmarkwithattention|■■■|⚫|⚫|⚫||⚫|⚫|⚫||⚫||⚫|MediaPipe,192x192| |289|face-detection-0100|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫||⚫|256x256,PriorBoxClustered->ndarray(0.npy)| |293|Lightweight-Head-Pose-Estimation|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||⚫|HeadPose, 224x224| |300|6DRepNet|■■■|⚫|⚫|⚫||⚫|⚫|⚫||⚫||⚫|6D HeadPose, 224x224| |301|YOLOv4Face|■■■|||||||||||⚫|480x640| |302|SLPT|■■■|||||||||||⚫|decoder=6/12,256x256| |303|FAN|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||⚫|Face Alignment,128x128/256x256| |304|SynergyNet|■■■|||||||||||⚫|6D HeadPose,224x224| |305|DMHead|■■■|||||||||||⚫|6D HeadPose,Multi-Model-Fused,224x224,PINTO's custom models| |311|HHP-Net|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||⚫|6D HeadPose,No-LICENSE| |319|ACR-Loss|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||⚫|Face Alignment| |322|YOLOv7Head|■■■|||||||||||⚫|PINTO's custom models| |383|DirectMHP|■■■|⚫|⚫|||||||||⚫|| |387|YuNetV2|■■■|⚫|⚫|⚫||⚫||||||⚫|640x640| |390|BlendshapeV2|■■■|⚫|⚫|||||||||⚫|1x146x2,Nx146x2,MediaPipe| |399|RetinaFaceMobileNetv2|■■■|||||||||||⚫|| |410|FaceMeshV2|■■■|⚫|⚫|⚫||⚫||⚫||||⚫|MediaPipe| |414|STAR|■■■|⚫|⚫|⚫||⚫||⚫||||⚫|| |421|Gold-YOLO-Head|■■■|||||||||||⚫|Head (not Face)| |423|6DRepNet360|■■■|||||||||||⚫|6D HeadPose, FullRange, 224x224| |433|FaceBoxes.PyTorch|■■■|||||||||||⚫|2D Face| |435|MobileFaceNet|■■■|||||||||||⚫|Face Alignment,112x112| |436|PeppaPigFaceLandmark|■■■|||||||||||⚫|Face Alignment,128x128,256x256| |437|PIPNet|■■■|||||||||||⚫|Face Alignment,256x256| |443|Opal23HeadPose|■■■|||||||||||⚫|6D HeadPose, FullRange, 128x128|

5. 2D/3D Hand Detection

|No.|Model Name|Link|FP32|FP16|INT8|TPU|DQ|WQ|OV|CM|TFJS|TF-TRT|ONNX|Remarks| |:-|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-| |027|Minimal-Hand|■■■|⚫|⚫|⚫|||⚫||⚫|⚫|⚫|⚫|| |033|HandDetectionandTracking|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|MediaPipe| |094|handrecrop|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|MediaPipe| |403|trtposehand|■■■|||||||||||⚫|2D| |420|Gold-YOLO-Hand|■■■|||||||||||⚫|2D| |438|PeCLR|■■■|||||||||||⚫|2D+3D|

6. 2D/3D Human/Animal Pose Estimation

|No.|Model Name|Link|FP32|FP16|INT8|TPU|DQ|WQ|OV|CM|TFJS|TF-TRT|ONNX|Remarks| |:-|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-| |003|Posenet|■■■|⚫|⚫|⚫|⚫||⚫|⚫|||||| |007|Mobilenetv2PoseEstimation|■■■|⚫|⚫|⚫|⚫||⚫|⚫||⚫|⚫||| |029|HumanPoseEstimation3D|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|RGB,180x320,240x320,360x640,480x640,720x1280| |053|BlazePose|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|MediaPipe| |065|ThreeDPoseUnityBarracuda|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|YouTube| |080|tfposeestimation|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|| |084|EfficientPose|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|SinglePose| |088|Mobilenetv3PoseEstimation|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|| |115|MoveNet|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|⚫|lightning,thunder| |137|MoveNetMultiPose|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|lightning,192x192,192x256,256x256,256x320,320x320,480x640,720x1280,1280x1920| |156|MobileHumanPose|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|3D| |157|3DMPPEPOSENET|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|⚫|3D,192x192/256x256/320x320/416x416/480x640/512x512| |265|PoseAug|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||⚫|2D->3D/GCN,MLP,STGCN,VideoPose/Nx16x2| |268|Lite-HRNet|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||⚫|COCO,MPII/Top-Down| |269|Higher-HRNet|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||⚫|192x320,256x320,320x480,384x640,480x640,512x512,576x960,736x1280/Bottom-Up| |271|HRNet|■■■|||||||||||⚫|COCO,MPII/Top-Down| |333|E2Pose|■■■|⚫|⚫|⚫||⚫||||||⚫|COCO/CrowdPose,End-to-End| |350|P-STMO|■■■|||||||||||⚫|2D->3D,inthewild| |355|MHFormer|■■■|⚫|⚫|||||||||⚫|2D->3D| |365|HTNet|■■■|⚫|⚫|||||||||⚫|2D->3D| |392|STCFormer|■■■|⚫|⚫|||||||||⚫|2D->3D| |393|RTMPoseWholeBody|■■■|⚫|⚫|⚫||⚫||||||⚫|2D| |394|RTMPoseAnimal|■■■|⚫|⚫|⚫||⚫||||||⚫|2D| |402|trtpose|■■■|||||||||||⚫|2D| |412|pytorchcpn|■■■|⚫|⚫|||||||||⚫|2D| |427|RTMPoseHand|■■■|||||||||||⚫|2D| |440|ViTPose|■■■|||||||||||⚫|2D|

7. Depth Estimation from Monocular/Stereo Images

|No.|Model Name|Link|FP32|FP16|INT8|TPU|DQ|WQ|OV|CM|TFJS|TF-TRT|ONNX|Remarks| |:-|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-| |009|Multi-Scale Local Planar Guidance for Monocular Depth Estimation|■■■|⚫|||||||||||| |014|tf-monodepth2|■■■|⚫|⚫|⚫|||⚫|⚫|⚫||⚫|⚫|| |028|struct2depth|■■■|⚫|⚫||||⚫|||||⚫|| |064|Dense Depth|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|| |066|Footprints|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|| |067|MiDaS|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|| |081|MiDaS v2|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|| |135|CoEx|■■■|||||||⚫||||⚫|WIP, onnx/OpenVINO only| |142|HITNET|■■■|⚫|⚫||||⚫|||||⚫|WIP issue1,issue2,flyingthingsfinalpassxl/eth3d/middlebury_d400,120x160/240x320/256x256/480x640/720x1280| |146|FastDepth|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|128x160,224x224,256x256,256x320,320x320,480x640,512x512,768x1280| |147|PackNet-SfM|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|ddad/kitti,Convert all ResNet18 backbones only| |148|LapDepth|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|kitti/nyu,192x320/256x320/368x640/480x640/720x1280| |149|depthestimation|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|nyu,180x320/240x320/360x640/480x640/720x1280| |150|MobileStereoNet|■■■||||||||||||WIP. Conversion script only.| |153|MegaDepth|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|192x256,384x512| |158|HR-Depth|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|⚫|| |159|EPCDepth|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|⚫|| |160|msgchnwacv20|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||⚫|192x320,240x320,256x256,352x480,368x480,368x640,480x640,720x1280,1280x1920| |162|PyDNet|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|⚫|| |164|MADNet|■■■|⚫|⚫||||⚫|⚫|⚫|⚫|⚫|⚫|Real-time-self-adaptive-deep-stereo (perform only inference mode, no-backprop, kitti)| |165|RealtimeStereo|■■■|⚫|⚫|||⚫|⚫|⚫||⚫|⚫|⚫|180x320,216x384,240x320,270x480,360x480,360x640,480x640,720x1280| |166|Insta-DM|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|⚫|192x320,256x320,256x832,384x640,480x640,736x1280| |167|DPT|■■■|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|⚫|dpt-hybrid,480x640,ViT,ONNX 96x128/256x320/384x480/480x640| |173|MVDepthNet|■■■|||||||⚫||||⚫|256x320| |202|stereoDNN|■■■|⚫|⚫|||⚫|⚫|⚫|⚫||⚫|⚫|NVSmall321x1025,NVTiny161x513,ResNet18321x1025,ResNet182d_257x513| |203|SRHNet|■■■|||||||||||⚫|finetune2_kitti/sceneflow,maxdisp192,320x480/480x640| |210|SCDepthpl|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|⚫|kitti/nyu,320x320,320x480,480x640,640x800| |211|Lac-GwcNet|■■■|||||||||||⚫|kitti,240x320,320x480,480x640,720x1280| |219|StereoNet|■■■|⚫|⚫|⚫||⚫|⚫|⚫|||⚫|⚫|Left/180x320,240x320,320x480,360x640,480x640| |235|W-Stereo-Disp|■■■|||||||||||⚫|Kitti,Sceneflow/320x480,384x576,480x640| |236|A-TVSNet|■■■|⚫|⚫|||⚫|⚫|||||⚫|Stereo only/192x320,256x320,320x480,480x640| |239|CasStereoNet|■■■|||||||||||⚫|Stereo KITTI only/256x320,384x480,480x640,736x1280| |245|GLPDepth|■■■|||||||⚫||||⚫|Kitti,NYU/192x320,320x480,384x640,480x640,736x1280,non-commercial use only| |258|TinyHITNet|■■■|||||||⚫||||⚫|180x320,240x320,300x400,360x640,384x512,480x640,720x960,720x1280| |266|ACVNet|■■■|||||||⚫||||⚫|sceneflow,kitti/240x320,320x480,384x640,480x640,544x960,720x1280| |280|GASDA|■■■|||||||||||⚫|No-LICENSE| |284|CREStereo|■■■|||||||||||⚫|ITER2,ITER5,ITER10,ITER20/240x320,320x480,360x640,480x640,480x640,720x1280| |292|Graft-PSMNet|■■■|||||||||||⚫|192x320,240x320,320x480,368x640,480x640,720x1280| |294|FSRE-Depth|■■■|||||||⚫||||⚫|192x320,256x320,320x480,368x640,480x640,736x1280| |296|MGNet|■■■|⚫|⚫|⚫||⚫|⚫|⚫||⚫||⚫|240x320,360x480,360x640,360x1280,480x640,720x1280| |312|NeWCRFs|■■■|||||||||||⚫|384x384,384x576,384x768,384x960,576x768,768x1344| |313|PyDNet2|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫||⚫|Mono-Depth| |327|EMDC|■■■|||||||||||⚫|RGB+SarseDepth| |338|Fast-ACVNet|■■■|||||||||||⚫|Stereo/gridsample opset=16,nogrid_sample opset=11| |358|CGI-Stereo|■■■|⚫|⚫|||||||||⚫|Stereo| |362|ZoeDepth|■■■|||||||||||⚫|Mono-Depth| |364|IGEV|■■■|||||||||||⚫|Stereo| |371|Lite-Mono|■■■|||||||||||⚫|Mono| |384|TCMonoDepth|■■■|||||||||||⚫|Mono| |397|MiDaSv3.1|■■■|||||||||||⚫|Mono| |415|High-frequency-Stereo-Matching-Network|■■■|||||||||||⚫|Stereo| |439|Depth-Anything|■■■|||||||||||⚫|Mono|

8. Semantic Segmentation

|No.|Model Name|Link|FP32|FP16|INT8|TPU|DQ|WQ|OV|CM|TFJS|TF-TRT|ONNX|Remarks| |:-|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-| |001|deeplabv3|■■■|⚫|||||⚫||||||| |015|Faster-Grad-CAM|■■■|⚫||⚫|||⚫||||||| |020|EdgeTPU-Deeplab|■■■|⚫|⚫|⚫|||⚫||||||| |021|EdgeTPU-Deeplab-slim|■■■|⚫|⚫|⚫|||⚫||||||| |026|Mobile-Deeplabv3-plus|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|| |035|BodyPix|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|MediaPipe,MobileNet0.50/0.75/1.00,ResNet50| |057|BiSeNetV2|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|| |060|Hair Segmentation|■■■|⚫|||||⚫|||⚫|⚫|⚫|WIP,MediaPipe| |061|U^2-Net|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|| |069|ENet|■■■|⚫|⚫||||⚫|||⚫|⚫|⚫|Cityscapes,512x1024| |075|ERFNet|■■■|⚫|⚫|⚫|⚫||⚫|⚫||⚫|⚫|⚫|Cityscapes,256x512,384x786,512x1024| |078|MODNet|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|128x128,192x192,256x256,512x512| |082|MediaPipeMeetSegmentation|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|MediaPipe,128x128,144x256,96x160| |104|DeeplabV3-plus|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫||⚫|cityscapes,200x400,400x800,800x1600| |109|SelfieSegmentation|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|256x256| |136|road-segmentation-adas-0001|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|| |138|BackgroundMattingV2|■■■|⚫|⚫||||⚫|⚫||||⚫|720x1280,2160x4096| |181|modelsedgetpucheckpointandtflitevisionsegmentation-edgetputflitedefaultargmax|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|| |182|modelsedgetpucheckpointandtflitevisionsegmentation-edgetputflitefusedargmax|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|| |196|humansegmentationpphumanseg|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|| |201|CityscapesSOTA|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||⚫|180x320,240x320,360x640,480x640,720x1280| |206|Matting|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||⚫|PaddleSeg/modnetmobilenetv2,modnethrnetw18,modnetresnet50_vd/256x256,384x384,512x512,640x640| |228|Fast-SCNN|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|192x384,384x384,384x576,576x576,576x768,768x1344| |238|SUIM-Net|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|||⚫|RSB,VGG/240x320,256x320,320x480,360x640,384x480,384x640,480x640,720x1280| |242|RobustVideoMatting|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫||⚫|Mbnv3,ResNet50/192x320,240x320,320x480,384x640,480x640,720x1280,1088x1920,2160x3840| |246|SqueezeSegV3|■■■|⚫|⚫|||⚫|⚫|⚫|⚫|⚫||⚫|21,53/180x320,240x320,320x480,360x640,480x640,720x1280| |267|LIOT|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||⚫|180x320,240x320,320x480,360x640,480x640,540x960,720x1280,1080x1920| |287|Topformer|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||⚫|Tiny,Small,Base/448x448,512x512| |295|SparseInst|■■■|||||||⚫||||⚫|r50giamaug/192x384,384x384,384x576,384x768,576x576,576x768,768x1344| |299|DGNet|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫||⚫|| |313|IS-Net|■■■|||||||||||⚫|180x320,240x320,320x480,360x640,480x640,720x1280,1080x1920,1080x2048,2160x4096,N-batch,Dynamic-HeightxWidth| |335|PIDNet|■■■|||||||||||⚫|Cityscapes,CamVid/Dynamic-HeightxWidth| |343|PP-MattingV2|■■■|⚫|⚫|||||||||⚫|HumanSeg| |347|RGBXSemanticSegmentation|■■■|||||||||||⚫|| |369|SegmentAnything|■■■|||||||||||⚫|| |380|Skin-Clothes-Hair-Segmentation-using-SMP|■■■|⚫|⚫|||||||||⚫|| |391|MagicTouch|■■■|⚫|⚫|||⚫||||||⚫|MediaPipe| |405|EarSegmentation|■■■|||||||||||⚫|Ear| |417|PopNet|■■■|||||||||||⚫|Saliency| |466|PeopleSegmentation|■■■|||||||||||⚫|UNet| |467|HumanParsing|■■■|||||||||||⚫|| |470|RHIS|■■■|||||||||||⚫|UNet+UNet| |489|489Glasses-Detector|■■■|||||||||||⚫|glasses|

9. Anomaly Detection

|No.|Model Name|Link|FP32|FP16|INT8|TPU|WQ|OV|CM|TFJS|TF-TRT|ONNX|Remarks| |:-|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-| |005|OneClassAnomalyDetection|■■■|⚫||⚫|⚫|⚫||||||| |099|EfficientnetAnomalyDetectionSegmentation|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫||

10. Artistic

|No.|Model Name|Link|FP32|FP16|INT8|TPU|DQ|WQ|OV|CM|TFJS|TF-TRT|ONNX|Remarks| |:-|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-| |017|Artistic-Style-Transfer|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫||⚫|| |019|White-box-Cartoonization|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|| |037|FirstNeuralStyleTransfer|■■■|⚫|||||⚫|||||⚫|| |044|Selfie2Anime|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|||⚫|| |050|AnimeGANv2|■■■|⚫|⚫|⚫|||⚫|⚫|⚫||⚫|⚫|| |062|Facial Cartoonization|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|| |068|ColorfulImageColorization|■■■|⚫|⚫||||⚫|⚫|⚫|⚫||⚫|experimental| |101|arbitraryimagestylization|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|magenta| |113|Anime2Sketch|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|| |161|EigenGAN-Tensorflow|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|Anime,CelebA| |193|CoCosNet|■■■|⚫|⚫|||⚫|⚫|⚫|⚫|⚫||⚫|RGB,256x256|

11. Super Resolution

|No.|Model Name|Link|FP32|FP16|INT8|TPU|DQ|WQ|OV|CM|TFJS|TF-TRT|ONNX|Remarks| |:-|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-| |012|FastAccurateandLightweightSuper-Resolution|■■■|⚫||⚫|||⚫||||||| |022|LearningtoSeeMovingObjectsintheDark|■■■|⚫|⚫||||⚫|||||⚫|| |071|Noise2Noise|■■■|⚫|⚫|⚫|⚫||⚫|||||⚫|srresnet/clear only| |076|DeepWhiteBalance|■■■|⚫|⚫||||⚫|⚫|⚫|⚫|⚫|⚫|| |077|ESRGAN|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|50x50->x4, 100x100->x4| |079|MIRNet|■■■|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|⚫|Low-light Image Enhancement/40x40,80x80,120x120,120x160,120x320,120x480,120x640,120x1280,180x480,180x640,180x1280,180x320,240x320,240x480,360x480,360x640,480x640,720x1280| |086|Defocus Deblurring Using Dual-Pixel|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|| |090|Ghost-freeShadowRemoval|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|||⚫|256x256| |111|SRN-Deblur|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|240x320,480x640,720x1280,1024x1280| |112|DeblurGANv2|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|inception/mobilenetv2:256x256,320x320,480x640,736x1280,1024x1280| |114|Two-branch-dehazing|■■■|⚫|⚫|⚫|||⚫|⚫||⚫|⚫|⚫|240x320,480x640,720x1280| |133|Real-ESRGAN|■■■|⚫|⚫||||⚫|⚫|⚫|⚫|⚫|⚫|16x16,32x32,64x64,128x128,240x320,256x256,320x320,480x640| |152|DeepLPF|■■■|⚫|⚫||||⚫|⚫||||⚫|| |170|Learning-to-See-in-the-Dark|■■■|⚫|⚫|⚫|||⚫|⚫|⚫|⚫|⚫|⚫|sony/fuji, 240x320,360x480,360x640,480x640| |171|Fast-SRGAN|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|||⚫|120x160,128x128,240x320,256x256,480x640,512x512| |172|Real-Time-Super-Resolution|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|64x64,96x96,128x128,256x256,240x320,480x640| |176|StableLLVE|■■■|⚫|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|Low-light Image/Video Enhancement,180x240,240x320,360x640,480x640,720x1280| |200|AGLLNet|■■■|⚫|⚫||||⚫|⚫|⚫|⚫|⚫|⚫|Low-light Image/Video Enhancement,256x256,256x384,384x512,512x640,768x768,768x1280| |204|HINet|■■■|⚫|⚫|⚫||⚫|⚫|⚫|⚫|⚫|⚫|⚫|DeBlur,DeNoise,DeRain/256x320,320x480,480x640| |205|MBLLEN|■■■