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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.

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PINTO0309/PINTO_model_zoo

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PINTO_model_zoo

CodeQL DOI Ask DeepWiki

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. 😄

[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

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 ■しかく■しかく■しかく ⚫ ⚫ ⚫
010 Mobilenetv3 ■しかく■しかく■しかく ⚫ ⚫ ⚫
011 Mobilenetv2 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫
016 Efficientnet-lite ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
070 age-gender-recognition ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
083 Person_Reidentification ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 248,277,286,287,288,300
087 DeepSort ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
124 person-attributes-recognition-crossroad-0230 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
125 person-attributes-recognition-crossroad-0234 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
126 person-attributes-recognition-crossroad-0238 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
175 face-recognition-resnet100-arcface-onnx ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ RGB/BGR,112x112,[1,512]
187 vehicle-attributes-recognition-barrier-0039 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 72x72
188 vehicle-attributes-recognition-barrier-0042 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 72x72
191 anti-spoof-mn3 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 128x128
192 open-closed-eye-0001 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 32x32
194 face_recognizer_fast ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 112x112
195 person_reid_youtu ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 256x128, ReID
199 NSFW ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 224x224
244 FINNger ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 96x96
256 SFace ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 112x112
257 PiCANet ■しかく■しかく■しかく ⚫ ⚫ BDDA,SAGE/224x224
259 Emotion_FERPlus ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 64x64
290 AdaFace ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 112x112
317 MobileOne ■しかく■しかく■しかく ⚫ 224x224
346 facial_expression_recognition_mobilefacenet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 112x112
379 PP-LCNetV2 ■しかく■しかく■しかく ⚫ ⚫ ⚫ 224x224
429 OSNet ■しかく■しかく■しかく ⚫ 256x128, ReID
430 FastReID ■しかく■しかく■しかく ⚫ 384x128, ReID
431 NITEC ■しかく■しかく■しかく ⚫ 224x224, Gaze Estimation
432 face-reidentification-retail-0095 ■しかく■しかく■しかく ⚫ ⚫ ⚫ 128x128, FaceReID
451 DAN ■しかく■しかく■しかく ⚫ 224x224, Facial Expression
452 FairFace ■しかく■しかく■しかく ⚫ 224x224, Face Attribute
453 FairDAN ■しかく■しかく■しかく ⚫ 224x224, Face Attribute + Facial Expression
462 Gaze-LLE ■しかく■しかく■しかく ⚫ 448x448, Attention

2. 2D Object Detection

No. Model Name Link FP32 FP16 INT8 TPU DQ WQ OV CM TFJS TF-TRT ONNX Remarks
002 Mobilenetv3-SSD ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫
006 Mobilenetv2-SSDlite ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫
008 Mask_RCNN_Inceptionv2 ■しかく■しかく■しかく ⚫ ⚫ ⚫
018 EfficientDet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
023 Yolov3-nano ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫
024 Yolov3-lite ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
031 Yolov4 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
034 SSD_Mobilenetv2_mnasfpn ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
038 SSDlite_MobileDet_edgetpu ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
039 SSDlite_MobileDet_cpu ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫
042 Centernet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
045 SSD_Mobilenetv2_oid_v4 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
046 Yolov4-tiny ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
047 SpineNetMB_49 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ Mobile RetinaNet
051 East_Text_Detection ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
054 KNIFT ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ MediaPipe
056 TextBoxes++ with dense blocks, separable convolution and Focal Loss ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
058 keras-retinanet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ resnet50_coco_best_v2.1.0.h5,320x320
072 NanoDet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ issue #274
073 RetinaNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
074 Yolact ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
085 Yolact_Edge ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 21/10/05 new MobileNetV2(550x550)
089 DETR ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ 256x256
103 EfficientDet_lite ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ lite0,lite1,lite2,lite3,lite4
116 DroNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ DroNet,DroNetV3
123 YOLOR ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ssss_s2d/320x320,640x640,960x960,1280x1280
132 YOLOX ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ nano,tiny,s,m,l,x/256x320,320x320,416x416,480x640,544x960,736x1280,1088x1920
143 RAPiD ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Fisheye, cepdof/habbof/mw_r, 608x608/1024x1024
145 text_detection_db ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 480x640
151 object_detection_mobile_object_localizer ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 192x192
169 spaghettinet_edgetpu ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 320x320,S/M/L
174 PP-PicoDet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ S/M/L,320x320/416x416/640x640
178 vehicle-detection-0200 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 256x256,PriorBoxClustered->ndarray(0.npy)
179 person-detection-0202 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 512x512,PriorBoxClustered->ndarray(0.npy)
183 pedestrian-detection-adas-0002 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 384x672,PriorBox->ndarray(0.npy)
184 pedestrian-and-vehicle-detector-adas-0001 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 384x672,PriorBox->ndarray(0.npy)
185 person-vehicle-bike-detection-crossroad-0078 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 1024x1024,PriorBoxClustered->ndarray(0.npy)
186 person-vehicle-bike-detection-crossroad-1016 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 512x512,PriorBoxClustered->ndarray(0.npy)
189 vehicle-license-plate-detection-barrier-0106 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 300x300,PriorBoxClustered->ndarray(0.npy)
190 person-detection-asl-0001 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 320x320
197 yolact-resnet50-fpn ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ RGB,550x550
198 YOLOF ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ BGR/RGB,608x608
221 YOLACT-PyTorch ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 180x320,240x320,320x480,480x640,544x544,720x1280
226 CascadeTableNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ General,320x320 only
262 ByteTrack ■しかく■しかく■しかく ⚫ YOLOX/nano,tiny,s,m,l,x,mot17,ablation/128x320,192x320,192x448,192x640,256x320,256x448,256x640,384x640,512x1280,736x1280
264 object_localization_network ■しかく■しかく■しかく ⚫ 180x320,240x320,270x480,360x480,360x480,360x640,480x640,720x1280
307 YOLOv7 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ YOLOv7,YOLOv7-tiny
308 FastestDet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 180x320,256x320,320x480,352x352,352x640,480x640,736x1280
329 YOLOX-PAI ■しかく■しかく■しかく ⚫
332 CrowdDet ■しかく■しかく■しかく ⚫
334 DAMO-YOLO ■しかく■しかく■しかく ⚫
336 PP-YOLOE-Plus ■しかく■しかく■しかく ⚫
337 FreeYOLO ■しかく■しかく■しかく ⚫
341 YOLOv6 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
356 EdgeYOLO ■しかく■しかく■しかく ⚫
376 RT-DETR ■しかく■しかく■しかく ⚫ ResNet50,ResNet101,HgNetv2-L,HgNetv2-X
386 naruto_handsign_detection ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫
422 Gold-YOLO-Head-Hand ■しかく■しかく■しかく ⚫ Head,Hand
424 Gold-YOLO-Body ■しかく■しかく■しかく ⚫ Body
425 Gold-YOLO-Body-Head-Hand ■しかく■しかく■しかく ⚫ Body,Head,Hand
426 YOLOX-Body-Head-Hand ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ Body,Head,Hand, tflite float16 XNNPACK boost (ARMv8.2)
434 YOLOX-Body-Head-Hand-Face ■しかく■しかく■しかく ⚫ Body,Head,Hand,Face
441 YOLOX-Body-Head-Hand-Face-Dist ■しかく■しかく■しかく ⚫ Body,Head,Hand,Face,Complex Distorted
442 YOLOX-Body-Head-Face-HandLR-Dist ■しかく■しかく■しかく ⚫ Body,Head,Hands,Left-Hand,Right-Hand,Face,Complex Distorted
444 YOLOX-Foot-Dist ■しかく■しかく■しかく ⚫ Foot,Complex Distorted
445 YOLOX-Body-Head-Face-HandLR-Foot-Dist ■しかく■しかく■しかく ⚫ Body,Head,Face,Hands,Left-Hand,Right-Hand,Foot,Complex Distorted
446 YOLOX-Body-With-Wheelchair ■しかく■しかく■しかく ⚫ Body with WheelChair
447 YOLOX-Wholebody-with-Wheelchair ■しかく■しかく■しかく ⚫ Wholebody with WheelChair
448 YOLOX-Eye-Nose-Mouth-Ear ■しかく■しかく■しかく ⚫
449 YOLOX-WholeBody12 ■しかく■しかく■しかく ⚫ Body,BodyWithWheelchair,Head,Face,Eye,Nose,Mouth,Ear,Hand,Hand-Left,Hand-Right,Foot
450 YOLOv9-Wholebody-with-Wheelchair ■しかく■しかく■しかく ⚫ Wholebody with WheelChair
454 YOLOv9-Wholebody13 ■しかく■しかく■しかく ⚫ Body,BodyWithWheelchair,BodyWithCrutches,Head,Face,Eye,Nose,Mouth,Ear,Hand,Hand-Left,Hand-Right,Foot
455 YOLOv9-Gender ■しかく■しかく■しかく ⚫ Body,Male,Female
456 YOLOv9-Wholebody15 ■しかく■しかく■しかく ⚫ Body,Male,Female,BodyWithWheelchair,BodyWithCrutches,Head,Face,Eye,Nose,Mouth,Ear,Hand,Hand-Left,Hand-Right,Foot
457 YOLOv9-Wholebody17 ■しかく■しかく■しかく ⚫ Body,Male,Adult,Child,Female,BodyWithWheelchair,BodyWithCrutches,Head,Face,Eye,Nose,Mouth,Ear,Hand,Hand-Left,Hand-Right,Foot
458 YOLOv9-Discrete-HeadPose-Yaw ■しかく■しかく■しかく ⚫ Head,Front,Right-Front,Right-Side,Right-Back,Back,Left-Back,Left-Side,Left-Front
459 YOLOv9-Wholebody25 ■しかく■しかく■しかく ⚫ Body,Adult,Child,Male,Female,Body_with_Wheelchair,Body_with_Crutches,Head,Front,Right_Front,Right_Side,Right_Back,Back,Left_Back,Left_Side,Left_Front,Face,Eye,Nose,Mouth,Ear,Hand,Hand_Left,Hand_Right,Foot
460 RT-DETRv2-Wholebody25 ■しかく■しかく■しかく ⚫ Body,Adult,Child,Male,Female,Body_with_Wheelchair,Body_with_Crutches,Head,Front,Right_Front,Right_Side,Right_Back,Back,Left_Back,Left_Side,Left_Front,Face,Eye,Nose,Mouth,Ear,Hand,Hand_Left,Hand_Right,Foot
461 YOLOv9-Phone ■しかく■しかく■しかく ⚫ Phone
463 YOLOv9-Shoulder-Elbow-Knee ■しかく■しかく■しかく ⚫ Shoulder,Elbow,Knee
464 YOLOv9-Wholebody28 ■しかく■しかく■しかく ⚫ Body,Adult,Child,Male,Female,Body_with_Wheelchair,Body_with_Crutches,Head,Front,Right_Front,Right_Side,Right_Back,Back,Left_Back,Left_Side,Left_Front,Face,Eye,Nose,Mouth,Ear,Shoulder,Elbow,Hand,Hand_Left,Hand_Right,Knee,Foot
465 DEIM-Wholebody28 ■しかく■しかく■しかく ⚫ Body,Adult,Child,Male,Female,Body_with_Wheelchair,Body_with_Crutches,Head,Front,Right_Front,Right_Side,Right_Back,Back,Left_Back,Left_Side,Left_Front,Face,Eye,Nose,Mouth,Ear,Shoulder,Elbow,Hand,Hand_Left,Hand_Right,Knee,Foot
468 YOLOv9-Wholebody28-Refine ■しかく■しかく■しかく ⚫ Body,Adult,Child,Male,Female,Body_with_Wheelchair,Body_with_Crutches,Head,Front,Right_Front,Right_Side,Right_Back,Back,Left_Back,Left_Side,Left_Front,Face,Eye,Nose,Mouth,Ear,Shoulder,Elbow,Hand,Hand_Left,Hand_Right,Knee,Foot
471 YOLO-Wholebody34 ■しかく■しかく■しかく ⚫ body,adult,child,male,female,body_with_wheelchair,body_with_crutches,head,front,right-front,right-side,right-back,back,left-back,left-side,left-front,face,eye,nose,mouth,ear,collarbone,shoulder,solar_plexus,elbow,wrist,hand,hand_left,hand_right,abdomen,hip_joint,knee,ankle,foot
472 DEIMv2-Wholebody34 ■しかく■しかく■しかく ⚫ body,adult,child,male,female,body_with_wheelchair,body_with_crutches,head,front,right-front,right-side,right-back,back,left-back,left-side,left-front,face,eye,nose,mouth,ear,collarbone,shoulder,solar_plexus,elbow,wrist,hand,hand_left,hand_right,abdomen,hip_joint,knee,ankle,foot
473 HISDF ■しかく■しかく■しかく ⚫ Object Detection x Depth Estimation x Pose Estimation x Instance Segmentation x Binary Segmentation

3. 3D Object Detection

No. Model Name Link FP32 FP16 INT8 TPU DQ WQ OV CM TFJS TF-TRT ONNX Remarks
036 Objectron ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ MediaPipe/camera,chair,chair_1stage,cup,sneakers,sneakers_1stage,ssd_mobilenetv2_oidv4_fp16
063 3D BoundingBox estimation for autonomous driving ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ YouTube
107 SFA3D ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
263 EgoNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
321 DID-M3D ■しかく■しかく■しかく ⚫
363 YOLO-6D-Pose ■しかく■しかく■しかく ⚫ ⚫ ⚫ 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 Head_Pose_Estimation ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
030 BlazeFace ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ MediaPipe
032 FaceMesh ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ MediaPipe
040 DSFD_vgg ■しかく■しかく■しかく ⚫ ⚫ ⚫
041 DBFace ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ MobileNetV2/V3, 320x320,480x640,640x960,800x1280
043 Face_Landmark ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
049 Iris_Landmark ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ MediaPipe
095 CenterFace ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
096 RetinaFace ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
106 WHENet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Real-time Fine-Grained Estimation for Wide Range Head Pose
129 SCRFD ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ All types
134 head-pose-estimation-adas-0001 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 60x60
144 YuNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 120x160
227 face-detection-adas-0001 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 384x672,PriorBox->ndarray(0.npy)
250 Face-Mask-Detection ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ PriorBox->ndarray(0.npy)
282 face_landmark_with_attention ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ MediaPipe,192x192
289 face-detection-0100 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 256x256,PriorBoxClustered->ndarray(0.npy)
293 Lightweight-Head-Pose-Estimation ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ HeadPose, 224x224
300 6DRepNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 6D HeadPose, 224x224
301 YOLOv4_Face ■しかく■しかく■しかく ⚫ 480x640
302 SLPT ■しかく■しかく■しかく ⚫ decoder=6/12,256x256
303 FAN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Face Alignment,128x128/256x256
304 SynergyNet ■しかく■しかく■しかく ⚫ 6D HeadPose,224x224
305 DMHead ■しかく■しかく■しかく ⚫ 6D HeadPose,Multi-Model-Fused,224x224,PINTO's custom models
311 HHP-Net ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 6D HeadPose,No-LICENSE
319 ACR-Loss ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Face Alignment
322 YOLOv7_Head ■しかく■しかく■しかく ⚫ PINTO's custom models
383 DirectMHP ■しかく■しかく■しかく ⚫ ⚫ ⚫
387 YuNetV2 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ 640x640
390 BlendshapeV2 ■しかく■しかく■しかく ⚫ ⚫ ⚫ 1x146x2,Nx146x2,MediaPipe
399 RetinaFace_MobileNetv2 ■しかく■しかく■しかく ⚫
410 FaceMeshV2 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ MediaPipe
414 STAR ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
421 Gold-YOLO-Head ■しかく■しかく■しかく ⚫ Head (not Face)
423 6DRepNet360 ■しかく■しかく■しかく ⚫ 6D HeadPose, FullRange, 224x224
433 FaceBoxes.PyTorch ■しかく■しかく■しかく ⚫ 2D Face
435 MobileFaceNet ■しかく■しかく■しかく ⚫ Face Alignment,112x112
436 Peppa_Pig_Face_Landmark ■しかく■しかく■しかく ⚫ Face Alignment,128x128,256x256
437 PIPNet ■しかく■しかく■しかく ⚫ Face Alignment,256x256
443 Opal23_HeadPose ■しかく■しかく■しかく ⚫ 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 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
033 Hand_Detection_and_Tracking ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ MediaPipe
094 hand_recrop ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ MediaPipe
403 trt_pose_hand ■しかく■しかく■しかく ⚫ 2D
420 Gold-YOLO-Hand ■しかく■しかく■しかく ⚫ 2D
438 PeCLR ■しかく■しかく■しかく ⚫ 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 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
007 Mobilenetv2_Pose_Estimation ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
029 Human_Pose_Estimation_3D ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ RGB,180x320,240x320,360x640,480x640,720x1280
053 BlazePose ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ MediaPipe
065 ThreeDPoseUnityBarracuda ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ YouTube
080 tf_pose_estimation ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
084 EfficientPose ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ SinglePose
088 Mobilenetv3_Pose_Estimation ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
115 MoveNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ lightning,thunder
137 MoveNet_MultiPose ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ lightning,192x192,192x256,256x256,256x320,320x320,480x640,720x1280,1280x1920
156 MobileHumanPose ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 3D
157 3DMPPE_POSENET ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 3D,192x192/256x256/320x320/416x416/480x640/512x512
265 PoseAug ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 2D->3D/GCN,MLP,STGCN,VideoPose/Nx16x2
268 Lite-HRNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ COCO,MPII/Top-Down
269 Higher-HRNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 192x320,256x320,320x480,384x640,480x640,512x512,576x960,736x1280/Bottom-Up
271 HRNet ■しかく■しかく■しかく ⚫ COCO,MPII/Top-Down
333 E2Pose ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ COCO/CrowdPose,End-to-End
350 P-STMO ■しかく■しかく■しかく ⚫ 2D->3D,in_the_wild
355 MHFormer ■しかく■しかく■しかく ⚫ ⚫ ⚫ 2D->3D
365 HTNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ 2D->3D
392 STCFormer ■しかく■しかく■しかく ⚫ ⚫ ⚫ 2D->3D
393 RTMPose_WholeBody ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ 2D
394 RTMPose_Animal ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ 2D
402 trt_pose ■しかく■しかく■しかく ⚫ 2D
412 pytorch_cpn ■しかく■しかく■しかく ⚫ ⚫ ⚫ 2D
427 RTMPose_Hand ■しかく■しかく■しかく ⚫ 2D
440 ViTPose ■しかく■しかく■しかく ⚫ 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 ■しかく■しかく■しかく ⚫
014 tf-monodepth2 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
028 struct2depth ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫
064 Dense Depth ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
066 Footprints ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
067 MiDaS ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
081 MiDaS v2 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
135 CoEx ■しかく■しかく■しかく ⚫ ⚫ WIP, onnx/OpenVINO only
142 HITNET ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ WIP issue1,issue2,flyingthings_finalpass_xl/eth3d/middlebury_d400,120x160/240x320/256x256/480x640/720x1280
146 FastDepth ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 128x160,224x224,256x256,256x320,320x320,480x640,512x512,768x1280
147 PackNet-SfM ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ddad/kitti,Convert all ResNet18 backbones only
148 LapDepth ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ kitti/nyu,192x320/256x320/368x640/480x640/720x1280
149 depth_estimation ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ nyu,180x320/240x320/360x640/480x640/720x1280
150 MobileStereoNet ■しかく■しかく■しかく WIP. Conversion script only.
153 MegaDepth ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 192x256,384x512
158 HR-Depth ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
159 EPCDepth ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
160 msg_chn_wacv20 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 192x320,240x320,256x256,352x480,368x480,368x640,480x640,720x1280,1280x1920
162 PyDNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
164 MADNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Real-time-self-adaptive-deep-stereo (perform only inference mode, no-backprop, kitti)
165 RealtimeStereo ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 180x320,216x384,240x320,270x480,360x480,360x640,480x640,720x1280
166 Insta-DM ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 192x320,256x320,256x832,384x640,480x640,736x1280
167 DPT ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ dpt-hybrid,480x640,ViT,ONNX 96x128/256x320/384x480/480x640
173 MVDepthNet ■しかく■しかく■しかく ⚫ ⚫ 256x320
202 stereoDNN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ NVSmall_321x1025,NVTiny_161x513,ResNet18_321x1025,ResNet18_2d_257x513
203 SRHNet ■しかく■しかく■しかく ⚫ finetune2_kitti/sceneflow,maxdisp192,320x480/480x640
210 SC_Depth_pl ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ kitti/nyu,320x320,320x480,480x640,640x800
211 Lac-GwcNet ■しかく■しかく■しかく ⚫ kitti,240x320,320x480,480x640,720x1280
219 StereoNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Left/180x320,240x320,320x480,360x640,480x640
235 W-Stereo-Disp ■しかく■しかく■しかく ⚫ Kitti,Sceneflow/320x480,384x576,480x640
236 A-TVSNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ Stereo only/192x320,256x320,320x480,480x640
239 CasStereoNet ■しかく■しかく■しかく ⚫ Stereo KITTI only/256x320,384x480,480x640,736x1280
245 GLPDepth ■しかく■しかく■しかく ⚫ ⚫ Kitti,NYU/192x320,320x480,384x640,480x640,736x1280,non-commercial use only
258 TinyHITNet ■しかく■しかく■しかく ⚫ ⚫ 180x320,240x320,300x400,360x640,384x512,480x640,720x960,720x1280
266 ACVNet ■しかく■しかく■しかく ⚫ ⚫ sceneflow,kitti/240x320,320x480,384x640,480x640,544x960,720x1280
280 GASDA ■しかく■しかく■しかく ⚫ No-LICENSE
284 CREStereo ■しかく■しかく■しかく ⚫ ITER2,ITER5,ITER10,ITER20/240x320,320x480,360x640,480x640,480x640,720x1280
292 Graft-PSMNet ■しかく■しかく■しかく ⚫ 192x320,240x320,320x480,368x640,480x640,720x1280
294 FSRE-Depth ■しかく■しかく■しかく ⚫ ⚫ 192x320,256x320,320x480,368x640,480x640,736x1280
296 MGNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 240x320,360x480,360x640,360x1280,480x640,720x1280
312 NeWCRFs ■しかく■しかく■しかく ⚫ 384x384,384x576,384x768,384x960,576x768,768x1344
313 PyDNet2 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Mono-Depth
327 EMDC ■しかく■しかく■しかく ⚫ RGB+SarseDepth
338 Fast-ACVNet ■しかく■しかく■しかく ⚫ Stereo/grid_sample opset=16,no_grid_sample opset=11
358 CGI-Stereo ■しかく■しかく■しかく ⚫ ⚫ ⚫ Stereo
362 ZoeDepth ■しかく■しかく■しかく ⚫ Mono-Depth
364 IGEV ■しかく■しかく■しかく ⚫ Stereo
371 Lite-Mono ■しかく■しかく■しかく ⚫ Mono
384 TCMonoDepth ■しかく■しかく■しかく ⚫ Mono
397 MiDaSv3.1 ■しかく■しかく■しかく ⚫ Mono
415 High-frequency-Stereo-Matching-Network ■しかく■しかく■しかく ⚫ Stereo
439 Depth-Anything ■しかく■しかく■しかく ⚫ Mono

8. Semantic Segmentation

No. Model Name Link FP32 FP16 INT8 TPU DQ WQ OV CM TFJS TF-TRT ONNX Remarks
001 deeplabv3 ■しかく■しかく■しかく ⚫ ⚫
015 Faster-Grad-CAM ■しかく■しかく■しかく ⚫ ⚫ ⚫
020 EdgeTPU-Deeplab ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫
021 EdgeTPU-Deeplab-slim ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫
026 Mobile-Deeplabv3-plus ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
035 BodyPix ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ MediaPipe,MobileNet0.50/0.75/1.00,ResNet50
057 BiSeNetV2 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
060 Hair Segmentation ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ WIP,MediaPipe
061 U^2-Net ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
069 ENet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Cityscapes,512x1024
075 ERFNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Cityscapes,256x512,384x786,512x1024
078 MODNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 128x128,192x192,256x256,512x512
082 MediaPipe_Meet_Segmentation ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ MediaPipe,128x128,144x256,96x160
104 DeeplabV3-plus ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ cityscapes,200x400,400x800,800x1600
109 Selfie_Segmentation ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 256x256
136 road-segmentation-adas-0001 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
138 BackgroundMattingV2 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ 720x1280,2160x4096
181 models_edgetpu_checkpoint_and_tflite_vision_segmentation-edgetpu_tflite_default_argmax ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
182 models_edgetpu_checkpoint_and_tflite_vision_segmentation-edgetpu_tflite_fused_argmax ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
196 human_segmentation_pphumanseg ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
201 CityscapesSOTA ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 180x320,240x320,360x640,480x640,720x1280
206 Matting ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ PaddleSeg/modnet_mobilenetv2,modnet_hrnet_w18,modnet_resnet50_vd/256x256,384x384,512x512,640x640
228 Fast-SCNN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 192x384,384x384,384x576,576x576,576x768,768x1344
238 SUIM-Net ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ RSB,VGG/240x320,256x320,320x480,360x640,384x480,384x640,480x640,720x1280
242 RobustVideoMatting ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Mbnv3,ResNet50/192x320,240x320,320x480,384x640,480x640,720x1280,1088x1920,2160x3840
246 SqueezeSegV3 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 21,53/180x320,240x320,320x480,360x640,480x640,720x1280
267 LIOT ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 180x320,240x320,320x480,360x640,480x640,540x960,720x1280,1080x1920
287 Topformer ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Tiny,Small,Base/448x448,512x512
295 SparseInst ■しかく■しかく■しかく ⚫ ⚫ r50_giam_aug/192x384,384x384,384x576,384x768,576x576,576x768,768x1344
299 DGNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
313 IS-Net ■しかく■しかく■しかく ⚫ 180x320,240x320,320x480,360x640,480x640,720x1280,1080x1920,1080x2048,2160x4096,N-batch,Dynamic-HeightxWidth
335 PIDNet ■しかく■しかく■しかく ⚫ Cityscapes,CamVid/Dynamic-HeightxWidth
343 PP-MattingV2 ■しかく■しかく■しかく ⚫ ⚫ ⚫ HumanSeg
347 RGBX_Semantic_Segmentation ■しかく■しかく■しかく ⚫
369 Segment_Anything ■しかく■しかく■しかく ⚫
380 Skin-Clothes-Hair-Segmentation-using-SMP ■しかく■しかく■しかく ⚫ ⚫ ⚫
391 MagicTouch ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ MediaPipe
405 Ear_Segmentation ■しかく■しかく■しかく ⚫ Ear
417 PopNet ■しかく■しかく■しかく ⚫ Saliency
466 People_Segmentation ■しかく■しかく■しかく ⚫ UNet
467 Human_Parsing ■しかく■しかく■しかく ⚫
470 RHIS ■しかく■しかく■しかく ⚫ UNet+UNet

9. Anomaly Detection

No. Model Name Link FP32 FP16 INT8 TPU WQ OV CM TFJS TF-TRT ONNX Remarks
005 One_Class_Anomaly_Detection ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫
099 Efficientnet_Anomaly_Detection_Segmentation ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫

10. Artistic

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

11. Super Resolution

No. Model Name Link FP32 FP16 INT8 TPU DQ WQ OV CM TFJS TF-TRT ONNX Remarks
012 Fast_Accurate_and_Lightweight_Super-Resolution ■しかく■しかく■しかく ⚫ ⚫ ⚫
022 Learning_to_See_Moving_Objects_in_the_Dark ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫
071 Noise2Noise ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ srresnet/clear only
076 Deep_White_Balance ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
077 ESRGAN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 50x50->x4, 100x100->x4
079 MIRNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 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 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
090 Ghost-free_Shadow_Removal ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 256x256
111 SRN-Deblur ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 240x320,480x640,720x1280,1024x1280
112 DeblurGANv2 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ inception/mobilenetv2:256x256,320x320,480x640,736x1280,1024x1280
114 Two-branch-dehazing ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 240x320,480x640,720x1280
133 Real-ESRGAN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 16x16,32x32,64x64,128x128,240x320,256x256,320x320,480x640
152 DeepLPF ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫
170 Learning-to-See-in-the-Dark ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ sony/fuji, 240x320,360x480,360x640,480x640
171 Fast-SRGAN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 120x160,128x128,240x320,256x256,480x640,512x512
172 Real-Time-Super-Resolution ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 64x64,96x96,128x128,256x256,240x320,480x640
176 StableLLVE ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Low-light Image/Video Enhancement,180x240,240x320,360x640,480x640,720x1280
200 AGLLNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Low-light Image/Video Enhancement,256x256,256x384,384x512,512x640,768x768,768x1280
204 HINet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ DeBlur,DeNoise,DeRain/256x320,320x480,480x640
205 MBLLEN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Low-light Image/Video Enhancement,180x320,240x320,360x640,480x640,720x1280
207 GLADNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Low-light Image/Video Enhancement,180x320,240x320,360x640,480x640,720x1280,No-LICENSE
208 SAPNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ DeRain,180x320,240x320,360x640,480x640,720x1280
209 MSBDN-DFF ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Dehazing,192x320,240x320,320x480,384x640,480x640,720x1280,No-LICENSE
212 GFN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ DeBlur+SuperResolution,x4/64x64,96x96,128x128,192x192,240x320,256x256,480x640,720x1280
213 TBEFN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Low-light Image Enhancement/180x320,240x320,320x480,360x640,480x640,720x1280
214 EnlightenGAN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Low-light Image Enhancement/192x320,240x320,320x480,368x640,480x640,720x1280
215 AOD-Net ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ DeHazing/180x320,240x320,320x480,360x640,480x640,720x1280
216 Zero-DCE-TF ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Low-light Image Enhancement/180x320,240x320,320x480,360x640,480x640,720x1280
217 RUAS ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Low-light Image Enhancement/180x320,240x320,320x480,360x640,480x640,720x1280,No-LICENSE
218 DSLR ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Low-light Image Enhancement/256x256,256x384,256x512,384x640,512x640,768x1280
220 HEP ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Low-light Image Enhancement/180x320,240x320,320x480,360x640,480x640
222 LFT ■しかく■しかく■しかく ⚫ Transformer/2x,4x/65x65
223 DA_dahazing ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ DeHazing/192x320,240x320,320x480,360x640,480x640,720x1280,No-LICENSE
224 Y-net ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ DeHazing/192x320,240x320,320x480,384x640,480x640,720x1280
225 DRBL ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ DeHazing/192x320,240x320,320x480,384x640,480x640,720x1280
230 Single-Image-Desnowing-HDCWNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ DeSnowing/512x672
231 DRBL ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Low-light Image Enhancement/180x320,240x320,320x480,360x640,480x640,720x1280,No-LICENSE
232 MIMO-UNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ DeBlur/180x320,240x320,320x480,360x640,480x640,720x1280,No-LICENSE
234 FBCNN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ DeNoise/180x320,240x320,320x480,360x640,480x640,720x1280
240 BSRGAN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ x2,x4/64x64,96x96,128x128,160x160,180x320,240x320,No-LICENSE
241 SCL-LLE ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Low-light Image Enhancement/180x320,240x320,320x480,480x640,720x1280,No-LICENSE
243 Zero-DCE-improved ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Low-light Image Enhancement/180x320,240x320,320x480,360x640,480x640,720x1280,academic use only
249 Real-CUGAN ■しかく■しかく■しかく ⚫ ⚫ 2x,3x,4x/64x64,96x96,128x128,120x160,160x160,180x320,240x320
251 AU-GAN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Low-light Image Enhancement/128x256,240x320,240x640,256x512,480x640,512x1024,720x1280
253 TransWeather ■しかく■しかく■しかく ⚫ ⚫ DeRain,DeHaizing,DeSnow/192x320,256x320,320x480,384x640,480x640,736x1280
261 EfficientDerain ■しかく■しかく■しかく ⚫ v4_SPA,v4_rain100H,v4_rain1400/192x320,256x320,320x480,384x640,480x640,608x800,736x1280
270 HWMNet ■しかく■しかく■しかく ⚫ Low-light Image Enhancement/192x320,256x320,320x480,384x640,480x640,544x960,720x1280
275 FD-GAN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ DeHaizing/192x320,256x320,384x640,480x640,720x1280,1080x1920,No-LICENSE
277 EDN-GTM ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ DeHaizing/192x320,240x320,384x480,480x640,512x512,720x1280,1088x1920
281 IMDN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ x4/64x64,96x96,128x128,120x160,160x160,180x320,192x192,256x256,180x320,240x320,360x640,480x640
283 UIE-WD ■しかく■しかく■しかく ⚫ Underwater Image Enhancement/WIP issue #97/192x320,240x320,320x480,360x640,480x640,720x1280,1080x1920
285 Decoupled-Low-light-Image-Enhancement ■しかく■しかく■しかく ⚫ Low-light Image Enhancement/180x320,240x320,360x480,360x640,480x640,720x1280
286 SCI ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Low-light Image Enhancement/180x320,240x320,360x480,360x640,480x640,720x1280
315 Illumination-Adaptive-Transformer ■しかく■しかく■しかく ⚫ Low-light Image Enhancement
316 night_enhancement ■しかく■しかく■しかく ⚫ Low-light Image Enhancement
320 Dehamer ■しかく■しかく■しかく ⚫ Dehazing
323 Stripformer ■しかく■しかく■しかく ⚫ DeBlur
325 DehazeFormer ■しかく■しかく■しかく ⚫ Dehazing
344 XYDeblur ■しかく■しかく■しかく ⚫ DeBlur
348 Bread ■しかく■しかく■しかく ⚫ Low-light Image Enhancement
348 PMN ■しかく■しかく■しかく ⚫ DeNoise, Low-light Image Enhancement
351 RFDN ■しかく■しかく■しかく ⚫ x4
352 MAXIM ■しかく■しかく■しかく ⚫ ⚫ ⚫ Dehaze only
353 ShadowFormer ■しかく■しかく■しかく ⚫ Shadow Removal
354 DEA-Net ■しかく■しかく■しかく ⚫ ⚫ ⚫ DeHaze
359 MSPFN ■しかく■しかく■しかく ⚫ ⚫ ⚫ DeRain
361 KBNet ■しかく■しかく■しかく ⚫ Real Image Denoising
367 FLW-Net ■しかく■しかく■しかく ⚫ Low-light Image Enhancement
368 C2PNet ■しかく■しかく■しかく ⚫ DeHaze
370 Semantic-Guided-Low-Light-Image-Enhancement ■しかく■しかく■しかく ⚫ Low-light Image Enhancement
372 URetinex-Net ■しかく■しかく■しかく ⚫ Low-light Image Enhancement
375 SCANet ■しかく■しかく■しかく ⚫ DeHaze
377 DRSformer ■しかく■しかく■しかく ⚫ DeRain
385 PairLIE ■しかく■しかく■しかく ⚫ Low-light Image Enhancement
389 WGWS-Net ■しかく■しかく■しかく ⚫ DeRain,DeRainDrop,DeHaize,DeSnow
396 MixDehazeNet ■しかく■しかく■しかく ⚫ DeHaize
400 CSRNet ■しかく■しかく■しかく ⚫ Low-light Image Enhancement
404 HDR-Transformer ■しかく■しかく■しかく ⚫
409 nighttime_dehaze ■しかく■しかく■しかく ⚫ DeHaze
411 UDR-S2Former_deraining ■しかく■しかく■しかく ⚫ DeRain
418 Diffusion-Low-Light ■しかく■しかく■しかく ⚫ ⚫ ⚫ Diffusion, Low-light Image Enhancement
469 Face_Deblurring ■しかく■しかく■しかく ⚫ 64x64, HxW

12. Sound Classifier

No. Model Name Link FP32 FP16 INT8 TPU DQ WQ OV CM TFJS TF-TRT ONNX Remarks
013 ml-sound-classifier ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
097 YAMNet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
098 SPICE ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
118 Speech-enhancement ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ WIP,EdgeTPU(LeakyLeRU)
120 FRILL ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ nofrontend
177 BirdNET-Lite ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ non-flex
381 Whisper ■しかく■しかく■しかく ⚫
382 Light-SERNet ■しかく■しかく■しかく ⚫ ⚫ ⚫

13. Natural Language Processing

No. Model Name Link FP32 FP16 INT8 TPU WQ OV CM TFJS TF-TRT ONNX Remarks
048 Mobile_BERT ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
121 GPT2/DistillGPT2 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
122 DistillBert ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫

14. Text Recognition

No. Model Name Link FP32 FP16 INT8 TPU WQ OV CM TFJS TF-TRT ONNX Remarks
052 Handwritten_Text_Recognition ■しかく■しかく■しかく ⚫ ⚫ ⚫
055 Handwritten_Japanese_Recognition ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
093 ocr_japanese ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 120x160

15. Action Recognition

No. Model Name Link FP32 FP16 INT8 TPU DQ WQ OV CM TFJS TF-TRT ONNX Remarks
092 weld-porosity-detection-0001 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
247 PoseC3D ■しかく■しかく■しかく ⚫ Skeleton-based/FineGYM,NTU60_XSub,NTU120_XSub,UCF101,HMDB51/1x20x48x64x64
248 MS-G3D ■しかく■しかく■しかく ⚫ Skeleton-based/Kinetics,NTU60,NTU120/1x3xTx25x2

16. Inpainting

No. Model Name Link FP32 FP16 INT8 TPU DQ WQ OV CM TFJS TF-TRT ONNX Remarks
100 HiFill ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫
163 MST_inpainting ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
273 OPN(Onion-Peel Networks) ■しかく■しかく■しかく ⚫
274 DeepFillv2 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫

17. GAN

No. Model Name Link FP32 FP16 INT8 TPU WQ OV CM TFJS TF-TRT ONNX Remarks
105 MobileStyleGAN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
310 attentive-gan-derainnet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ DeRain/180x320,240x320,240x360,320x480,360x640,480x640,720x1280

18. Transformer

No. Model Name Link FP32 FP16 INT8 TPU WQ OV CM TFJS TF-TRT ONNX Remarks
127 dino ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ experimental,dino_deits8/dino_deits16

19. Others

No. Model Name Link FP32 FP16 INT8 TPU DQ WQ OV CM TFJS TF-TRT ONNX Remarks
091 gaze-estimation-adas-0002 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
102 Coconet ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ magenta
108 HAWP ■しかく■しかく■しかく ⚫ Line Parsing,WIP
110 L-CNN ■しかく■しかく■しかく ⚫ Line Parsing,WIP
117 DTLN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
119 M-LSD ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
131 CFNet ■しかく■しかく■しかく ⚫ 256x256,512x768
139 PSD-Principled-Synthetic-to-Real-Dehazing-Guided-by-Physical-Priors ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
140 Ultra-Fast-Lane-Detection ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 288x800
141 lanenet-lane-detection ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 256x512
154 driver-action-recognition-adas-0002-encoder ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
155 driver-action-recognition-adas-0002-decoder ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
167 LSTR ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 180x320,240x320,360x640,480x640,720x1280
229 DexiNed ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 160x320,320x480,368x640,480x640,720x1280
233 HRNet-for-Fashion-Landmark-Estimation ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 192x320,256x320,320x480,384x640,480x640,736x1280
237 piano_transcription ■しかく■しかく■しかく ⚫ ⚫ 1x160000,Nx160000
252 RAFT ■しかく■しかく■しかく ⚫ small,chairs,kitti,sintel,things/iters=10,20/240x320,360x480,480x640
254 FullSubNet-plus ■しかく■しかく■しかく ⚫ 1x1x257x100,200,500,1000,2000,3000,5000,7000,8000,10000
255 FILM ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ L1,Style,VGG/256x256,180x320,240x320,360x640,480x640,720x1280,1080x1920
260 KP2D ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ResNet/128x320,192x320,192x448,192x640,256x320,256x448,256x640,320x448,384x640,480x640,512x1280,736x1280
272 CSFlow ■しかく■しかく■しかく ⚫ chairs,kitti,things/iters=10,20/192x320,240x320,320x480,384x640,480x640,736x1280
276 HybridNets ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ anchor_HxW.npy/256x384,256x512,384x512,384x640,384x1024,512x640,768x1280,1152x1920
278 DWARF ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ StereoDepth+OpticalFlow,/192x320,256x320,384x640,512x640,512x640,768x1280
279 F-Clip ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Line Parsing/ALL/192x320,256x320,320x480,384x640,480x640,736x1280
288 perceptual-reflection-removal ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ Reflection-Removal/180x320,240x320,360x480,360x640,480x640,720x1280
291 SeAFusion ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ 180x320,240x320,360x480,360x640,480x640,720x1280
297 GazeNet ■しかく■しかく■しかく ⚫ 1x7x3x256x192/NxFx3x256x192
298 DEQ-Flow ■しかく■しかく■しかく ⚫ AGPL-3.0 license
306 GMFlowNet ■しかく■しかく■しかく ⚫ OpticalFlow/192x320,240x320,320x480,360x640,480x640,720x1280
309 ImageForensicsOSN ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ ⚫ forgery detection/180x320,240x320,320x480,360x640,480x640,720x1280
318 pips ■しかく■しかく■しかく ⚫
324 Ultra-Fast-Lane-Detection-v2 ■しかく■しかく■しかく ⚫
326 YOLOPv2 ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
328 Stable_Diffusion ■しかく■しかく■しかく ⚫
339 DeepLSD ■しかく■しかく■しかく ⚫
342 ALIKE ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ ⚫
357 Unimatch ■しかく■しかく■しかく ⚫ ⚫ ⚫ OpticalFlow, StereoDepth
360 PARSeq ■しかく■しかく■しかく ⚫ ⚫ ⚫ Scene Text Recognition
366 text_recognition_CRNN ■しかく■しかく■しかく ⚫ ⚫ ⚫ CN/CH/EN
373 LiteTrack ■しかく■しかく■しかく ⚫ ⚫ ⚫ Tracking
374 LaneSOD ■しかく■しかく■しかく ⚫ Lane Segmentation
378 P2PNet_tfkeras ■しかく■しかく■しかく ⚫ ⚫ ⚫
388 LightGlue ■しかく■しかく■しかく ⚫ Keypoint Matching
398 L2CS-Net ■しかく■しかく■しかく ⚫ ⚫ ⚫ ⚫ ⚫ Gaze Pose 448x448
401 CLRerNet ■しかく■しかく■しかく ⚫ Lane Detection
406 DeDoDe ■しかく■しかく■しかく ⚫ ⚫ ⚫ Keypoint Detection, Description, Matching
407 Generalizing_Gaze_Estimation ■しかく■しかく■しかく ⚫ ⚫ ⚫ Gaze Pose 160x160
408 UAED ■しかく■しかく■しかく ⚫ Edge Detectopm
413 DocShadow ■しかく■しかく■しかく ⚫ ⚫ Document Shadow Removal
416 GeoNet ■しかく■しかく■しかく ⚫ ⚫ MonoDepth, CameraPose, OpticalFlow
428 ISR ■しかく■しかく■しかく ⚫ Person ReID

Sample.1 - Object detection by video file

$ cd 006_mobilenetv2-ssdlite/02_voc/03_integer_quantization
$ ./download.sh && cd ..
$ python3 mobilenetv2ssdlite_movie_sync.py

004

Sample.2 - Object detection by USB Camera

$ cd 006_mobilenetv2-ssdlite/02_voc/03_integer_quantization
$ ./download.sh && cd ..
$ python3 mobilenetv2ssdlite_usbcam_sync.py

005

Sample.3 - Head Pose Estimation, Multi-stage inference with multi-model

  • RaspberryPi4 (CPU only)
  • Ubuntu 19.10 64bit
  • Tensorflow / Tensorflow Lite with multi-thread acceleration tuning for PythonAPI
  • [Model.1] MobileNetV2-SSDLite dm=0.5 300x300, Integer Quantization
  • [Model.2] Head Pose Estimation 128x128, Integer Quantization
  • WIDERFACE
  • USB Camera, 640x480
  • IPS 1080p HDMI Display
  • Approximately 13FPS for all processes from pre-processing, inference, post-processing, and display
$ cd 025_head_pose_estimation/03_integer_quantization
$ ./download.sh
$ python3 head_pose_estimation.py

006

Sample.4 - Semantic Segmentation, DeeplabV3-plus 256x256

  • RaspberryPi4 (CPU only)
  • Ubuntu 19.10 64bit
  • Tensorflow / Tensorflow Lite with multi-thread acceleration tuning for PythonAPI
  • DeeplabV3-plus (MobileNetV2) Decoder 256x256, Integer Quantization
  • USB Camera, 640x480
  • IPS 1080p HDMI Display
  • Approximately 8.5 FPS for all processes from pre-processing, inference, post-processing, and display
$ cd 026_mobile-deeplabv3-plus/03_integer_quantization
$ ./download.sh
$ python3 deeplabv3plus_usbcam.py

007

Sample.5 - MediaPipe/FaceMesh, face_detection_front_128_weight_quant, face_landmark_192_weight_quant

Sample.6 - MediaPipe/Objectron, object_detection_3d_chair_640x480_weight_quant

Sample.7 - MediaPipe/Objectron, object_detection_3d_chair_640x480_openvino_FP32

Sample.8 - MediaPipe/BlazeFace, face_detection_front_128_integer_quant

Sample.9 - MediaPipe/Hand_Detection_and_Tracking(3D Hand Pose), hand_landmark_3d_256_integer_quant.tflite + palm_detection_builtin_256_integer_quant.tflite

Sample.10 - DBFace, 640x480_openvino_FP32

Sample.11 - Human_Pose_Estimation_3D, 640x480, Tensorflow.js + WebGL + Browser

Sample.12 - BlazePose Full Body, 640x480, Tensorflow.js + WebGL + Browser

Sample.13 - Facial Cartoonization, 640x480, OpenVINO Corei7 CPU only

  • Ubuntu 18.04 x86_64
  • OpenVINO
  • USB Camera, 640x480
  • Test Code 015

1. Environment

  • Ubuntu 18.04 x86_64
  • RaspberryPi4 Raspbian Buster 32bit / Raspbian Buster 64bit / Ubuntu 19.10 aarch64
  • Tensorflow-GPU v1.15.2 or Tensorflow v2.3.1+
  • OpenVINO 2020.2+
  • PyTorch 1.6.0+
  • ONNX Opset12
  • Python 3.6.8
  • PascalVOC Dataset
  • COCO Dataset
  • Cityscapes Dataset
  • Imagenette Dataset
  • CelebA Dataset
  • Audio file (.wav)
  • WIDERFACE
  • Google Colaboratory

2. Procedure

Procedure examples

2-1. MobileNetV3+DeeplabV3+PascalVOC

2-1-1. Preparation

$ cd ~
$ mkdir deeplab;cd deeplab
$ git clone --depth 1 https://github.com/tensorflow/models.git
$ cd models/research/deeplab/datasets
$ mkdir pascal_voc_seg
$ curl -sc /tmp/cookie \
 "https://drive.google.com/uc?export=download&id=1rATNHizJdVHnaJtt-hW9MOgjxoaajzdh" > /dev/null
$ CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
$ curl -Lb /tmp/cookie \
 "https://drive.google.com/uc?export=download&confirm=${CODE}&id=1rATNHizJdVHnaJtt-hW9MOgjxoaajzdh" \
 -o pascal_voc_seg/VOCtrainval_11-May-2012.tar
$ sed -i -e "s/python .\/remove_gt_colormap.py/python3 .\/remove_gt_colormap.py/g" \
 -i -e "s/python .\/build_voc2012_data.py/python3 .\/build_voc2012_data.py/g" \
 download_and_convert_voc2012.sh
$ sh download_and_convert_voc2012.sh
$ cd ../..
$ mkdir -p deeplab/datasets/pascal_voc_seg/exp/train_on_train_set/train
$ mkdir -p deeplab/datasets/pascal_voc_seg/exp/train_on_train_set/eval
$ mkdir -p deeplab/datasets/pascal_voc_seg/exp/train_on_train_set/vis
$ export PATH_TO_TRAIN_DIR=${HOME}/deeplab/models/research/deeplab/datasets/pascal_voc_seg/exp/train_on_train_set/train
$ export PATH_TO_DATASET=${HOME}/deeplab/models/research/deeplab/datasets/pascal_voc_seg/tfrecord
$ export PYTHONPATH=${HOME}/deeplab/models/research:${HOME}/deeplab/models/research/deeplab:${HOME}/deeplab/models/research/slim:${PYTHONPATH}
# See feature_extractor.network_map for supported model variants.
# models/research/deeplab/core/feature_extractor.py
networks_map = {
 'mobilenet_v2': _mobilenet_v2,
 'mobilenet_v3_large_seg': mobilenet_v3_large_seg,
 'mobilenet_v3_small_seg': mobilenet_v3_small_seg,
 'resnet_v1_18': resnet_v1_beta.resnet_v1_18,
 'resnet_v1_18_beta': resnet_v1_beta.resnet_v1_18_beta,
 'resnet_v1_50': resnet_v1_beta.resnet_v1_50,
 'resnet_v1_50_beta': resnet_v1_beta.resnet_v1_50_beta,
 'resnet_v1_101': resnet_v1_beta.resnet_v1_101,
 'resnet_v1_101_beta': resnet_v1_beta.resnet_v1_101_beta,
 'xception_41': xception.xception_41,
 'xception_65': xception.xception_65,
 'xception_71': xception.xception_71,
 'nas_pnasnet': nas_network.pnasnet,
 'nas_hnasnet': nas_network.hnasnet,
}

2-1-2. "mobilenet_v3_small_seg" Float32 regular training

$ python3 deeplab/train.py \
 --logtostderr \
 --training_number_of_steps=500000 \
 --train_split="train" \
 --model_variant="mobilenet_v3_small_seg" \
 --decoder_output_stride=16 \
 --train_crop_size="513,513" \
 --train_batch_size=8 \
 --dataset="pascal_voc_seg" \
 --save_interval_secs=300 \
 --save_summaries_secs=300 \
 --save_summaries_images=True \
 --log_steps=100 \
 --train_logdir=${PATH_TO_TRAIN_DIR} \
 --dataset_dir=${PATH_TO_DATASET}

2-1-3. "mobilenet_v3_large_seg" Float32 regular training

$ python3 deeplab/train.py \
 --logtostderr \
 --training_number_of_steps=1000000 \
 --train_split="train" \
 --model_variant="mobilenet_v3_large_seg" \
 --decoder_output_stride=16 \
 --train_crop_size="513,513" \
 --train_batch_size=8 \
 --dataset="pascal_voc_seg" \
 --save_interval_secs=300 \
 --save_summaries_secs=300 \
 --save_summaries_images=True \
 --log_steps=100 \
 --train_logdir=${PATH_TO_TRAIN_DIR} \
 --dataset_dir=${PATH_TO_DATASET}

2-1-4. Visualize training status

$ tensorboard \
 --logdir ${HOME}/deeplab/models/research/deeplab/datasets/pascal_voc_seg/exp/train_on_train_set/train

2-2. MobileNetV3+DeeplabV3+Cityscaps - Post-training quantization

2-2-1. Preparation

$ cd ~
$ mkdir -p git/deeplab && cd git/deeplab
$ git clone --depth 1 https://github.com/tensorflow/models.git
$ cd models/research/deeplab/datasets
$ mkdir cityscapes && cd cityscapes
# Clone the script to generate Cityscapes Dataset.
$ git clone --depth 1 https://github.com/mcordts/cityscapesScripts.git
$ mv cityscapesScripts cityscapesScripts_ && \
 mv cityscapesScripts_/cityscapesscripts . && \
 rm -rf cityscapesScripts_
# Download Cityscapes Dataset.
# https://www.cityscapes-dataset.com/
# You will need to sign up and issue a userID and password to download the data set.
$ wget --keep-session-cookies --save-cookies=cookies.txt \
 --post-data 'username=(userid)&password=(password)&submit=Login' \
 https://www.cityscapes-dataset.com/login/
$ wget --load-cookies cookies.txt \
 --content-disposition https://www.cityscapes-dataset.com/file-handling/?packageID=1
$ wget --load-cookies cookies.txt \
 --content-disposition https://www.cityscapes-dataset.com/file-handling/?packageID=3
$ unzip gtFine_trainvaltest.zip && rm gtFine_trainvaltest.zip
$ rm README && rm license.txt
$ unzip leftImg8bit_trainvaltest.zip && rm leftImg8bit_trainvaltest.zip
$ rm README && rm license.txt
# Convert Cityscapes Dataset to TFRecords format.
$ cd ..
$ sed -i -e "s/python/python3/g" convert_cityscapes.sh
$ export PYTHONPATH=${HOME}/git/deeplab/models/research/deeplab/datasets/cityscapes:${PYTHONPATH}
$ sh convert_cityscapes.sh
# Create a checkpoint storage folder for training. If training is not required,
# there is no need to carry out.
$ cd ../..
$ mkdir -p deeplab/datasets/cityscapes/exp/train_on_train_set/train && \
 mkdir -p deeplab/datasets/cityscapes/exp/train_on_train_set/eval && \
 mkdir -p deeplab/datasets/cityscapes/exp/train_on_train_set/vis
# Download the DeepLabV3 trained model of the MobileNetV3 backbone.
$ curl -sc /tmp/cookie \
 "https://drive.google.com/uc?export=download&id=1f5ccaJmJBYwBmHvRQ77yGIUcXnqQIRY_" > /dev/null
$ CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
$ curl -Lb /tmp/cookie \
 "https://drive.google.com/uc?export=download&confirm=${CODE}&id=1f5ccaJmJBYwBmHvRQ77yGIUcXnqQIRY_" \
 -o deeplab_mnv3_small_cityscapes_trainfine_2019_11_15.tar.gz
$ tar -zxvf deeplab_mnv3_small_cityscapes_trainfine_2019_11_15.tar.gz
$ rm deeplab_mnv3_small_cityscapes_trainfine_2019_11_15.tar.gz
$ curl -sc /tmp/cookie \
 "https://drive.google.com/uc?export=download&id=1QxS3G55rUQvuiBF-hztQv5zCkfPfwlVU" > /dev/null
$ CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
$ curl -Lb /tmp/cookie \
 "https://drive.google.com/uc?export=download&confirm=${CODE}&id=1QxS3G55rUQvuiBF-hztQv5zCkfPfwlVU" \
 -o deeplab_mnv3_large_cityscapes_trainfine_2019_11_15.tar.gz
$ tar -zxvf deeplab_mnv3_large_cityscapes_trainfine_2019_11_15.tar.gz
$ rm deeplab_mnv3_large_cityscapes_trainfine_2019_11_15.tar.gz
$ export PATH_TO_INITIAL_CHECKPOINT=${HOME}/git/deeplab/models/research/deeplab_mnv3_small_cityscapes_trainfine/model.ckpt
$ export PATH_TO_DATASET=${HOME}/git/deeplab/models/research/deeplab/datasets/cityscapes/tfrecord
$ export PYTHONPATH=${HOME}/git/deeplab/models/research:${HOME}/git/deeplab/models/research/deeplab:${HOME}/git/deeplab/models/research/slim:${PYTHONPATH}
# Fix a bug in the data generator.
$ sed -i -e \
 "s/splits_to_sizes={'train_fine': 2975,/splits_to_sizes={'train': 2975,/g" \
 deeplab/datasets/data_generator.py
# Back up the trained model.
$ cd ${HOME}/git/deeplab/models/research
$ cp deeplab/export_model.py deeplab/export_model.py_org
$ cp deeplab_mnv3_small_cityscapes_trainfine/frozen_inference_graph.pb \
 deeplab_mnv3_small_cityscapes_trainfine/frozen_inference_graph_org.pb
$ cp deeplab_mnv3_large_cityscapes_trainfine/frozen_inference_graph.pb \
 deeplab_mnv3_large_cityscapes_trainfine/frozen_inference_graph_org.pb
# Customize "export_model.py" according to the input resolution. Must be (multiple of 8 + 1).
# (example.1) 769 = 8 * 96 + 1
# (example.2) 512 = 8 * 64 + 1
# (example.3) 320 = 8 * 40 + 1
# And it is necessary to change from tf.uint8 type to tf.float32 type.
$ sed -i -e \
 "s/tf.placeholder(tf.uint8, \[1, None, None, 3\], name=_INPUT_NAME)/tf.placeholder(tf.float32, \[1, 769, 769, 3\], name=_INPUT_NAME)/g" \
 deeplab/export_model.py

2-2-2. Parameter sheet

# crop_size and image_pooling_crop_size are multiples of --decoder_output_stride + 1
# 769 = 8 * 96 + 1
# 513 = 8 * 64 + 1
# 321 = 8 * 40 + 1
# --initialize_last_layer=True initializes the final layer with the weight of
# tf_initial_checkpoint (inherits the weight)
# Named tuple to describe the dataset properties.
# deeplab/datasets/data_generator.py
DatasetDescriptor = collections.namedtuple(
 'DatasetDescriptor',
 [
 'splits_to_sizes', # Splits of the dataset into training, val and test.
 'num_classes', # Number of semantic classes, including the
 # background class (if exists). For example, there
 # are 20 foreground classes + 1 background class in
 # the PASCAL VOC 2012 dataset. Thus, we set
 # num_classes=21.
 'ignore_label', # Ignore label value.
 ])
_CITYSCAPES_INFORMATION = DatasetDescriptor(
 splits_to_sizes={'train': 2975,
 'train_coarse': 22973,
 'trainval_fine': 3475,
 'trainval_coarse': 23473,
 'val_fine': 500,
 'test_fine': 1525},
 num_classes=19,
 ignore_label=255,
)
_PASCAL_VOC_SEG_INFORMATION = DatasetDescriptor(
 splits_to_sizes={
 'train': 1464,
 'train_aug': 10582,
 'trainval': 2913,
 'val': 1449,
 },
 num_classes=21,
 ignore_label=255,
)
_ADE20K_INFORMATION = DatasetDescriptor(
 splits_to_sizes={
 'train': 20210, # num of samples in images/training
 'val': 2000, # num of samples in images/validation
 },
 num_classes=151,
 ignore_label=0,
)
_DATASETS_INFORMATION = {
 'cityscapes': _CITYSCAPES_INFORMATION,
 'pascal_voc_seg': _PASCAL_VOC_SEG_INFORMATION,
 'ade20k': _ADE20K_INFORMATION,
}
# A map from network name to network function. model_variant.
# deeplab/core/feature_extractor.py
networks_map = {
 'mobilenet_v2': _mobilenet_v2,
 'mobilenet_v3_large_seg': mobilenet_v3_large_seg,
 'mobilenet_v3_small_seg': mobilenet_v3_small_seg,
 'resnet_v1_18': resnet_v1_beta.resnet_v1_18,
 'resnet_v1_18_beta': resnet_v1_beta.resnet_v1_18_beta,
 'resnet_v1_50': resnet_v1_beta.resnet_v1_50,
 'resnet_v1_50_beta': resnet_v1_beta.resnet_v1_50_beta,
 'resnet_v1_101': resnet_v1_beta.resnet_v1_101,
 'resnet_v1_101_beta': resnet_v1_beta.resnet_v1_101_beta,
 'xception_41': xception.xception_41,
 'xception_65': xception.xception_65,
 'xception_71': xception.xception_71,
 'nas_pnasnet': nas_network.pnasnet,
 'nas_hnasnet': nas_network.hnasnet,
}

2-2-3. "mobilenet_v3_small_seg" Export Model

Generate Freeze Graph (.pb) with INPUT Placeholder changed from checkpoint file (.ckpt).

$ python3 deeplab/export_model.py \
 --checkpoint_path=./deeplab_mnv3_small_cityscapes_trainfine/model.ckpt \
 --export_path=./deeplab_mnv3_small_cityscapes_trainfine/frozen_inference_graph.pb \
 --num_classes=19 \
 --crop_size=769 \
 --crop_size=769 \
 --model_variant="mobilenet_v3_small_seg" \
 --image_pooling_crop_size="769,769" \
 --image_pooling_stride=4,5 \
 --aspp_convs_filters=128 \
 --aspp_with_concat_projection=0 \
 --aspp_with_squeeze_and_excitation=1 \
 --decoder_use_sum_merge=1 \
 --decoder_filters=19 \
 --decoder_output_is_logits=1 \
 --image_se_uses_qsigmoid=1 \
 --image_pyramid=1 \
 --decoder_output_stride=8

2-2-4. "mobilenet_v3_large_seg" Export Model

Generate Freeze Graph (.pb) with INPUT Placeholder changed from checkpoint file (.ckpt).

$ python3 deeplab/export_model.py \
 --checkpoint_path=./deeplab_mnv3_large_cityscapes_trainfine/model.ckpt \
 --export_path=./deeplab_mnv3_large_cityscapes_trainfine/frozen_inference_graph.pb \
 --num_classes=19 \
 --crop_size=769 \
 --crop_size=769 \
 --model_variant="mobilenet_v3_large_seg" \
 --image_pooling_crop_size="769,769" \
 --image_pooling_stride=4,5 \
 --aspp_convs_filters=128 \
 --aspp_with_concat_projection=0 \
 --aspp_with_squeeze_and_excitation=1 \
 --decoder_use_sum_merge=1 \
 --decoder_filters=19 \
 --decoder_output_is_logits=1 \
 --image_se_uses_qsigmoid=1 \
 --image_pyramid=1 \
 --decoder_output_stride=8

If you follow the Google Colaboratory sample procedure, copy the "deeplab_mnv3_small_cityscapes_trainfine" folder and "deeplab_mnv3_large_cityscapes_trainfine" to your Google Drive "My Drive". It is not necessary if all procedures described in Google Colaboratory are performed in a PC environment. 001 002

2-2-5. Google Colaboratory - Post-training quantization - post_training_integer_quant.ipynb

  • Weight Quantization
  • Integer Quantization
  • Full Integer Quantization

https://colab.research.google.com/drive/1TtCJ-uMNTArpZxrf5DCNbZdn08DsiW8F

2-3. MobileNetV3+DeeplabV3+Cityscaps - Quantization-aware training

2-3-1. "mobilenet_v3_small_seg" Quantization-aware training

$ cd ${HOME}/git/deeplab/models/research
$ export PATH_TO_TRAINED_FLOAT_MODEL=${HOME}/git/deeplab/models/research/deeplab_mnv3_small_cityscapes_trainfine/model.ckpt
$ export PATH_TO_TRAIN_DIR=${HOME}/git/deeplab/models/research/deeplab/datasets/cityscapes/exp/train_on_train_set/train
$ export PATH_TO_DATASET=${HOME}/git/deeplab/models/research/deeplab/datasets/cityscapes/tfrecord
# deeplab_mnv3_small_cityscapes_trainfine
$ python3 deeplab/train.py \
 --logtostderr \
 --training_number_of_steps=5000 \
 --train_split="train" \
 --model_variant="mobilenet_v3_small_seg" \
 --train_crop_size="769,769" \
 --train_batch_size=8 \
 --dataset="cityscapes" \
 --initialize_last_layer=False \
 --base_learning_rate=3e-5 \
 --quantize_delay_step=0 \
 --image_pooling_crop_size="769,769" \
 --image_pooling_stride=4,5 \
 --aspp_convs_filters=128 \
 --aspp_with_concat_projection=0 \
 --aspp_with_squeeze_and_excitation=1 \
 --decoder_use_sum_merge=1 \
 --decoder_filters=19 \
 --decoder_output_is_logits=1 \
 --image_se_uses_qsigmoid=1 \
 --image_pyramid=1 \
 --decoder_output_stride=8 \
 --save_interval_secs=300 \
 --save_summaries_secs=300 \
 --save_summaries_images=True \
 --log_steps=100 \
 --tf_initial_checkpoint=${PATH_TO_TRAINED_FLOAT_MODEL} \
 --train_logdir=${PATH_TO_TRAIN_DIR} \
 --dataset_dir=${PATH_TO_DATASET}

2-3-2. "mobilenet_v3_large_seg" Quantization-aware training

$ cd ${HOME}/git/deeplab/models/research
$ export PATH_TO_TRAINED_FLOAT_MODEL=${HOME}/git/deeplab/models/research/deeplab_mnv3_large_cityscapes_trainfine/model.ckpt
$ export PATH_TO_TRAIN_DIR=${HOME}/git/deeplab/models/research/deeplab/datasets/cityscapes/exp/train_on_train_set/train
$ export PATH_TO_DATASET=${HOME}/git/deeplab/models/research/deeplab/datasets/cityscapes/tfrecord
# deeplab_mnv3_large_cityscapes_trainfine
$ python3 deeplab/train.py \
 --logtostderr \
 --training_number_of_steps=4350 \
 --train_split="train" \
 --model_variant="mobilenet_v3_large_seg" \
 --train_crop_size="769,769" \
 --train_batch_size=8 \
 --dataset="cityscapes" \
 --initialize_last_layer=False \
 --base_learning_rate=3e-5 \
 --quantize_delay_step=0 \
 --image_pooling_crop_size="769,769" \
 --image_pooling_stride=4,5 \
 --aspp_convs_filters=128 \
 --aspp_with_concat_projection=0 \
 --aspp_with_squeeze_and_excitation=1 \
 --decoder_use_sum_merge=1 \
 --decoder_filters=19 \
 --decoder_output_is_logits=1 \
 --image_se_uses_qsigmoid=1 \
 --image_pyramid=1 \
 --decoder_output_stride=8 \
 --save_interval_secs=300 \
 --save_summaries_secs=300 \
 --save_summaries_images=True \
 --log_steps=100 \
 --tf_initial_checkpoint=${PATH_TO_TRAINED_FLOAT_MODEL} \
 --train_logdir=${PATH_TO_TRAIN_DIR} \
 --dataset_dir=${PATH_TO_DATASET}

The orange line is "deeplab_mnv3_small_cityscapes_trainfine" loss. The blue line is "deeplab_mnv3_large_cityscapes_trainfine" loss. 003

2-4. MobileNetV2+DeeplabV3+coco/voc - Post-training quantization

2-4-1. Preparation

$ cd ${HOME}/git/deeplab/models/research
$ wget http://download.tensorflow.org/models/deeplabv3_mnv2_dm05_pascal_trainaug_2018_10_01.tar.gz
$ tar -zxvf deeplabv3_mnv2_dm05_pascal_trainaug_2018_10_01.tar.gz
$ rm deeplabv3_mnv2_dm05_pascal_trainaug_2018_10_01.tar.gz
$ wget http://download.tensorflow.org/models/deeplabv3_mnv2_dm05_pascal_trainval_2018_10_01.tar.gz
$ tar -zxvf deeplabv3_mnv2_dm05_pascal_trainval_2018_10_01.tar.gz
$ rm deeplabv3_mnv2_dm05_pascal_trainval_2018_10_01.tar.gz
$ wget http://download.tensorflow.org/models/deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz
$ tar -zxvf deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz
$ rm deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz
$ sed -i -e \
 "s/tf.placeholder(tf.uint8, \[1, None, None, 3\], name=_INPUT_NAME)/tf.placeholder(tf.float32, \[1, 257, 257, 3\], name=_INPUT_NAME)/g" \
 deeplab/export_model.py
$ export PYTHONPATH=${HOME}/git/deeplab/models/research:${HOME}/git/deeplab/models/research/deeplab:${HOME}/git/deeplab/models/research/slim:${PYTHONPATH}
$ python3 deeplab/export_model.py \
 --checkpoint_path=./deeplabv3_mnv2_dm05_pascal_trainaug/model.ckpt \
 --export_path=./deeplabv3_mnv2_dm05_pascal_trainaug/frozen_inference_graph.pb \
 --model_variant="mobilenet_v2" \
 --crop_size=257 \
 --crop_size=257 \
 --depth_multiplier=0.5
$ python3 deeplab/export_model.py \
 --checkpoint_path=./deeplabv3_mnv2_dm05_pascal_trainval/model.ckpt \
 --export_path=./deeplabv3_mnv2_dm05_pascal_trainval/frozen_inference_graph.pb \
 --model_variant="mobilenet_v2" \
 --crop_size=257 \
 --crop_size=257 \
 --depth_multiplier=0.5
$ python3 deeplab/export_model.py \
 --checkpoint_path=./deeplabv3_mnv2_pascal_train_aug/model.ckpt-30000 \
 --export_path=./deeplabv3_mnv2_pascal_train_aug/frozen_inference_graph.pb \
 --model_variant="mobilenet_v2" \
 --crop_size=257 \
 --crop_size=257

2-5. MobileNetV3-SSD+coco - Post-training quantization

2-5-1. Preparation

$ cd ~
$ sudo pip3 install tensorflow-gpu==1.15.0
$ git clone --depth 1 https://github.com/tensorflow/models.git
$ cd models/research
$ git clone https://github.com/cocodataset/cocoapi.git
$ cd cocoapi/PythonAPI
$ make
$ cp -r pycocotools ../..
$ cd ../..
$ wget -O protobuf.zip https://github.com/google/protobuf/releases/download/v3.0.0/protoc-3.0.0-linux-x86_64.zip
$ unzip protobuf.zip
$ ./bin/protoc object_detection/protos/*.proto --python_out=.
$ sudo apt-get install -y protobuf-compiler python3-pil python3-lxml python3-tk
$ sudo -H pip3 install Cython contextlib2 jupyter matplotlib
$ export PYTHONPATH=${PWD}:${PWD}/object_detection:${PWD}/slim:${PYTHONPATH}
$ mkdir -p ssd_mobilenet_v3_small_coco_2019_08_14 && cd ssd_mobilenet_v3_small_coco_2019_08_14
$ curl -sc /tmp/cookie "https://drive.google.com/uc?export=download&id=1uqaC0Y-yRtzkpu1EuZ3BzOyh9-i_3Qgi" > /dev/null
$ CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
$ curl -Lb /tmp/cookie "https://drive.google.com/uc?export=download&confirm=${CODE}&id=1uqaC0Y-yRtzkpu1EuZ3BzOyh9-i_3Qgi" -o ssd_mobilenet_v3_small_coco_2019_08_14.tar.gz
$ tar -zxvf ssd_mobilenet_v3_small_coco_2019_08_14.tar.gz
$ rm ssd_mobilenet_v3_small_coco_2019_08_14.tar.gz
$ cd ..
$ mkdir -p ssd_mobilenet_v3_large_coco_2019_08_14 && cd ssd_mobilenet_v3_large_coco_2019_08_14
$ curl -sc /tmp/cookie "https://drive.google.com/uc?export=download&id=1NGLjKRWDQZ_kibQHlLZ7Eetuuz1waC7X" > /dev/null
$ CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
$ curl -Lb /tmp/cookie "https://drive.google.com/uc?export=download&confirm=${CODE}&id=1NGLjKRWDQZ_kibQHlLZ7Eetuuz1waC7X" -o ssd_mobilenet_v3_large_coco_2019_08_14.tar.gz
$ tar -zxvf ssd_mobilenet_v3_large_coco_2019_08_14.tar.gz
$ rm ssd_mobilenet_v3_large_coco_2019_08_14.tar.gz
$ cd ..

2-5-2. Create a conversion script from checkpoint format to saved_model format

import tensorflow as tf
import os
import shutil
from tensorflow.python.saved_model import tag_constants
from tensorflow.python.tools import freeze_graph
from tensorflow.python import ops
from tensorflow.tools.graph_transforms import TransformGraph
def freeze_model(saved_model_dir, output_node_names, output_filename):
 output_graph_filename = os.path.join(saved_model_dir, output_filename)
 initializer_nodes = ''
 freeze_graph.freeze_graph(
 input_saved_model_dir=saved_model_dir,
 output_graph=output_graph_filename,
 saved_model_tags = tag_constants.SERVING,
 output_node_names=output_node_names,
 initializer_nodes=initializer_nodes,
 input_graph=None,
 input_saver=False,
 input_binary=False,
 input_checkpoint=None,
 restore_op_name=None,
 filename_tensor_name=None,
 clear_devices=True,
 input_meta_graph=False,
 )
def get_graph_def_from_file(graph_filepath):
 tf.reset_default_graph()
 with ops.Graph().as_default():
 with tf.gfile.GFile(graph_filepath, 'rb') as f:
 graph_def = tf.GraphDef()
 graph_def.ParseFromString(f.read())
 return graph_def
def optimize_graph(model_dir, graph_filename, transforms, input_name, output_names, outname='optimized_model.pb'):
 input_names = [input_name] # change this as per how you have saved the model
 graph_def = get_graph_def_from_file(os.path.join(model_dir, graph_filename))
 optimized_graph_def = TransformGraph(
 graph_def,
 input_names,
 output_names,
 transforms)
 tf.train.write_graph(optimized_graph_def,
 logdir=model_dir,
 as_text=False,
 name=outname)
 print('Graph optimized!')
def convert_graph_def_to_saved_model(export_dir, graph_filepath, input_name, outputs):
 graph_def = get_graph_def_from_file(graph_filepath)
 with tf.Session(graph=tf.Graph()) as session:
 tf.import_graph_def(graph_def, name='')
 tf.compat.v1.saved_model.simple_save(
 session,
 export_dir,# change input_image to node.name if you know the name
 inputs={input_name: session.graph.get_tensor_by_name('{}:0'.format(node.name))
 for node in graph_def.node if node.op=='Placeholder'},
 outputs={t.rstrip(":0"):session.graph.get_tensor_by_name(t) for t in outputs}
 )
 print('Optimized graph converted to SavedModel!')
tf.compat.v1.enable_eager_execution()
# Look up the name of the placeholder for the input node
graph_def=get_graph_def_from_file('./ssd_mobilenet_v3_small_coco_2019_08_14/frozen_inference_graph.pb')
input_name_small=""
for node in graph_def.node:
 if node.op=='Placeholder':
 print("##### ssd_mobilenet_v3_small_coco_2019_08_14 - Input Node Name #####", node.name) # this will be the input node
 input_name_small=node.name
# Look up the name of the placeholder for the input node
graph_def=get_graph_def_from_file('./ssd_mobilenet_v3_large_coco_2019_08_14/frozen_inference_graph.pb')
input_name_large=""
for node in graph_def.node:
 if node.op=='Placeholder':
 print("##### ssd_mobilenet_v3_large_coco_2019_08_14 - Input Node Name #####", node.name) # this will be the input node
 input_name_large=node.name
# ssd_mobilenet_v3 output names
output_node_names = ['raw_outputs/class_predictions','raw_outputs/box_encodings']
outputs = ['raw_outputs/class_predictions:0','raw_outputs/box_encodings:0']
# Optimizing the graph via TensorFlow library
transforms = []
optimize_graph('./ssd_mobilenet_v3_small_coco_2019_08_14', 'frozen_inference_graph.pb', transforms, input_name_small, output_node_names, outname='optimized_model_small.pb')
optimize_graph('./ssd_mobilenet_v3_large_coco_2019_08_14', 'frozen_inference_graph.pb', transforms, input_name_large, output_node_names, outname='optimized_model_large.pb')
# convert this to a s TF Serving compatible mode - ssd_mobilenet_v3_small_coco_2019_08_14
shutil.rmtree('./ssd_mobilenet_v3_small_coco_2019_08_14/0', ignore_errors=True)
convert_graph_def_to_saved_model('./ssd_mobilenet_v3_small_coco_2019_08_14/0',
 './ssd_mobilenet_v3_small_coco_2019_08_14/optimized_model_small.pb', input_name_small, outputs)
# convert this to a s TF Serving compatible mode - ssd_mobilenet_v3_large_coco_2019_08_14
shutil.rmtree('./ssd_mobilenet_v3_large_coco_2019_08_14/0', ignore_errors=True)
convert_graph_def_to_saved_model('./ssd_mobilenet_v3_large_coco_2019_08_14/0',
 './ssd_mobilenet_v3_large_coco_2019_08_14/optimized_model_large.pb', input_name_large, outputs)

2-5-3. Confirm the structure of saved_model 【ssd_mobilenet_v3_small_coco_2019_08_14】

$ saved_model_cli show --dir ./ssd_mobilenet_v3_small_coco_2019_08_14/0 --all
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:
signature_def['serving_default']:
 The given SavedModel SignatureDef contains the following input(s):
 inputs['normalized_input_image_tensor'] tensor_info:
 dtype: DT_FLOAT
 shape: (1, 320, 320, 3)
 name: normalized_input_image_tensor:0
 The given SavedModel SignatureDef contains the following output(s):
 outputs['raw_outputs/box_encodings'] tensor_info:
 dtype: DT_FLOAT
 shape: (1, 2034, 4)
 name: raw_outputs/box_encodings:0
 outputs['raw_outputs/class_predictions'] tensor_info:
 dtype: DT_FLOAT
 shape: (1, 2034, 91)
 name: raw_outputs/class_predictions:0
 Method name is: tensorflow/serving/predict

2-5-4. Confirm the structure of saved_model 【ssd_mobilenet_v3_large_coco_2019_08_14】

$ saved_model_cli show --dir ./ssd_mobilenet_v3_large_coco_2019_08_14/0 --all
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:
signature_def['serving_default']:
 The given SavedModel SignatureDef contains the following input(s):
 inputs['normalized_input_image_tensor'] tensor_info:
 dtype: DT_FLOAT
 shape: (1, 320, 320, 3)
 name: normalized_input_image_tensor:0
 The given SavedModel SignatureDef contains the following output(s):
 outputs['raw_outputs/box_encodings'] tensor_info:
 dtype: DT_FLOAT
 shape: (1, 2034, 4)
 name: raw_outputs/box_encodings:0
 outputs['raw_outputs/class_predictions'] tensor_info:
 dtype: DT_FLOAT
 shape: (1, 2034, 91)
 name: raw_outputs/class_predictions:0
 Method name is: tensorflow/serving/predict

2-5-5. Creating the destination path for the calibration test dataset 6GB

$ curl -sc /tmp/cookie "https://drive.google.com/uc?export=download&id=1Uk9F4Tc-9UgnvARIVkloSoePUynyST6E" > /dev/null
$ CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
$ curl -Lb /tmp/cookie "https://drive.google.com/uc?export=download&confirm=${CODE}&id=1Uk9F4Tc-9UgnvARIVkloSoePUynyST6E" -o TFDS.tar.gz
$ tar -zxvf TFDS.tar.gz
$ rm TFDS.tar.gz

2-5-6. Quantization

2-5-6-1. ssd_mobilenet_v3_small_coco_2019_08_14
import tensorflow as tf
import tensorflow_datasets as tfds
import numpy as np
def representative_dataset_gen():
 for data in raw_test_data.take(100):
 image = data['image'].numpy()
 image = tf.image.resize(image, (320, 320))
 image = image[np.newaxis,:,:,:]
 yield [image]
tf.compat.v1.enable_eager_execution()
# Generating a calibration data set
#raw_test_data, info = tfds.load(name="coco/2017", with_info=True, split="test", data_dir="./TFDS")
raw_test_data, info = tfds.load(name="coco/2017", with_info=True, split="test", data_dir="./TFDS", download=False)
print(info)
# Weight Quantization - Input/Output=float32
converter = tf.lite.TFLiteConverter.from_saved_model('./ssd_mobilenet_v3_small_coco_2019_08_14/0')
converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
tflite_quant_model = converter.convert()
with open('./ssd_mobilenet_v3_small_coco_2019_08_14/mobilenet_v3_small_weight_quant.tflite', 'wb') as w:
 w.write(tflite_quant_model)
print("Weight Quantization complete! - mobilenet_v3_small_weight_quant.tflite")
# Integer Quantization - Input/Output=float32
converter = tf.lite.TFLiteConverter.from_saved_model('./ssd_mobilenet_v3_small_coco_2019_08_14/0')
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset_gen
tflite_quant_model = converter.convert()
with open('./ssd_mobilenet_v3_small_coco_2019_08_14/mobilenet_v3_small_integer_quant.tflite', 'wb') as w:
 w.write(tflite_quant_model)
print("Integer Quantization complete! - mobilenet_v3_small_integer_quant.tflite")
# Full Integer Quantization - Input/Output=int8
converter = tf.lite.TFLiteConverter.from_saved_model('./ssd_mobilenet_v3_small_coco_2019_08_14/0')
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset_gen
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8
tflite_quant_model = converter.convert()
with open('./ssd_mobilenet_v3_small_coco_2019_08_14/mobilenet_v3_small_full_integer_quant.tflite', 'wb') as w:
 w.write(tflite_quant_model)
print("Full Integer Quantization complete! - mobilenet_v3_small_full_integer_quant.tflite")
2-5-6-2. ssd_mobilenet_v3_large_coco_2019_08_14
import tensorflow as tf
import tensorflow_datasets as tfds
import numpy as np
def representative_dataset_gen():
 for data in raw_test_data.take(100):
 image = data['image'].numpy()
 image = tf.image.resize(image, (320, 320))
 image = image[np.newaxis,:,:,:]
 yield [image]
tf.compat.v1.enable_eager_execution()
# Generating a calibration data set
#raw_test_data, info = tfds.load(name="coco/2017", with_info=True, split="test", data_dir="./TFDS")
raw_test_data, info = tfds.load(name="coco/2017", with_info=True, split="test", data_dir="./TFDS", download=False)
# Weight Quantization - Input/Output=float32
converter = tf.lite.TFLiteConverter.from_saved_model('./ssd_mobilenet_v3_large_coco_2019_08_14/0')
converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
tflite_quant_model = converter.convert()
with open('./ssd_mobilenet_v3_large_coco_2019_08_14/mobilenet_v3_large_weight_quant.tflite', 'wb') as w:
 w.write(tflite_quant_model)
print("Weight Quantization complete! - mobilenet_v3_large_weight_quant.tflite")
# Integer Quantization - Input/Output=float32
converter = tf.lite.TFLiteConverter.from_saved_model('./ssd_mobilenet_v3_large_coco_2019_08_14/0')
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset_gen
tflite_quant_model = converter.convert()
with open('./ssd_mobilenet_v3_large_coco_2019_08_14/mobilenet_v3_large_integer_quant.tflite', 'wb') as w:
 w.write(tflite_quant_model)
print("Integer Quantization complete! - mobilenet_v3_large_integer_quant.tflite")
# Full Integer Quantization - Input/Output=int8
converter = tf.lite.TFLiteConverter.from_saved_model('./ssd_mobilenet_v3_large_coco_2019_08_14/0')
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset_gen
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8
tflite_quant_model = converter.convert()
with open('./ssd_mobilenet_v3_large_coco_2019_08_14/mobilenet_v3_large_full_integer_quant.tflite', 'wb') as w:
 w.write(tflite_quant_model)
print("Full Integer Quantization complete! - mobilenet_v3_large_full_integer_quant.tflite")

2-6. MobileNetV2-SSDLite+VOC - Training -> Integer Quantization

2-6-1. Training

Learning with the MobileNetV2-SSDLite Pascal-VOC dataset [Remake of Docker version]

2-6-2. Export model (--add_postprocessing_op=True)

06_mobilenetv2-ssdlite/02_voc/01_float32/00_export_tflite_model.txt

2-6-3. Integer Quantization

06_mobilenetv2-ssdlite/02_voc/01_float32/03_integer_quantization_with_postprocess.py

3. TFLite Model Benchmark

$ sudo apt-get install python-future
## Bazel for Ubuntu18.04 x86_64 install
$ wget https://github.com/bazelbuild/bazel/releases/download/2.0.0/bazel-2.0.0-installer-linux-x86_64.sh
$ sudo chmod +x bazel-2.0.0-installer-linux-x86_64.sh
$ ./bazel-2.0.0-installer-linux-x86_64.sh
$ sudo apt-get install -y openjdk-8-jdk
## Bazel for RaspberryPi3/4 Raspbian/Debian Buster armhf install
$ wget https://github.com/PINTO0309/Bazel_bin/raw/main/3.1.0/Raspbian_Debian_Buster_armhf/openjdk-8-jdk/install.sh
$ ./install.sh
$ curl -sc /tmp/cookie \
 "https://drive.google.com/uc?export=download&id=1LQUSal55R6fmawZS9zZuk6-5ZFOdUqRK" > /dev/null
$ CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
$ curl -Lb /tmp/cookie \
 "https://drive.google.com/uc?export=download&confirm=${CODE}&id=1LQUSal55R6fmawZS9zZuk6-5ZFOdUqRK" \
 -o adoptopenjdk-8-hotspot_8u222-b10-2_armhf.deb
$ sudo apt-get install -y ./adoptopenjdk-8-hotspot_8u222-b10-2_armhf.deb
## Bazel for RaspberryPi3/4 Raspbian/Debian Buster aarch64 install
$ wget https://github.com/PINTO0309/Bazel_bin/raw/main/3.1.0/Raspbian_Debian_Buster_aarch64/openjdk-8-jdk/install.sh
$ ./install.sh
$ curl -sc /tmp/cookie \
 "https://drive.google.com/uc?export=download&id=1VwLxzT3EOTbhSzwvRF2H4ChTQyTQBt3x" > /dev/null
$ CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
$ curl -Lb /tmp/cookie \
 "https://drive.google.com/uc?export=download&confirm=${CODE}&id=1VwLxzT3EOTbhSzwvRF2H4ChTQyTQBt3x" \
 -o adoptopenjdk-8-hotspot_8u222-b10-2_arm64.deb
$ sudo apt-get install -y ./adoptopenjdk-8-hotspot_8u222-b10-2_arm64.deb
## Clone Tensorflow v2.1.0+
$ git clone --depth 1 https://github.com/tensorflow/tensorflow.git
$ cd tensorflow
## Build and run TFLite Model Benchmark Tool
$ bazel run -c opt tensorflow/lite/tools/benchmark:benchmark_model -- \
 --graph=${HOME}/Downloads/deeplabv3_257_mv_gpu.tflite \
 --num_threads=4 \
 --warmup_runs=1 \
 --enable_op_profiling=true
$ bazel run -c opt tensorflow/lite/tools/benchmark:benchmark_model -- \
 --graph=${HOME}/Downloads/deeplabv3_257_mv_gpu.tflite \
 --num_threads=4 \
 --warmup_runs=1 \
 --use_xnnpack=true \
 --enable_op_profiling=true
$ bazel run \
 -c opt \
 --config=noaws \
 --config=nohdfs \
 --config=nonccl \
 tensorflow/lite/tools/benchmark:benchmark_model_plus_flex -- \
 --graph=${HOME}/git/tf-monodepth2/monodepth2_flexdelegate_weight_quant.tflite \
 --num_threads=4 \
 --warmup_runs=1 \
 --enable_op_profiling=true
$ bazel run \
 -c opt \
 --config=noaws \
 --config=nohdfs \
 --config=nonccl \
 tensorflow/lite/tools/benchmark:benchmark_model_plus_flex -- \
 --graph=${HOME}/git/tf-monodepth2/monodepth2_flexdelegate_weight_quant.tflite \
 --num_threads=4 \
 --warmup_runs=1 \
 --use_xnnpack=true \
 --enable_op_profiling=true
x86_64 deeplab_mnv3_small_weight_quant_769.tflite Benchmark
Number of nodes executed: 171
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 45	 1251.486	 67.589%	 67.589%	 0.000	 0
	 DEPTHWISE_CONV_2D	 11	 438.764	 23.696%	 91.286%	 0.000	 0
	 HARD_SWISH	 16	 54.855	 2.963%	 94.248%	 0.000	 0
	 ARG_MAX	 1	 24.850	 1.342%	 95.591%	 0.000	 0
	 RESIZE_BILINEAR	 5	 23.805	 1.286%	 96.876%	 0.000	 0
	 MUL	 30	 14.914	 0.805%	 97.682%	 0.000	 0
	 ADD	 18	 10.646	 0.575%	 98.257%	 0.000	 0
	 SPACE_TO_BATCH_ND	 7	 9.567	 0.517%	 98.773%	 0.000	 0
	 BATCH_TO_SPACE_ND	 7	 7.431	 0.401%	 99.175%	 0.000	 0
	 SUB	 2	 6.131	 0.331%	 99.506%	 0.000	 0
	 AVERAGE_POOL_2D	 10	 5.435	 0.294%	 99.799%	 0.000	 0
	 RESHAPE	 6	 2.171	 0.117%	 99.916%	 0.000	 0
	 PAD	 1	 0.660	 0.036%	 99.952%	 0.000	 0
	 CAST	 2	 0.601	 0.032%	 99.985%	 0.000	 0
	 STRIDED_SLICE	 1	 0.277	 0.015%	 100.000%	 0.000	 0
	 Misc Runtime Ops	 1	 0.008	 0.000%	 100.000%	 33.552	 0
	 DEQUANTIZE	 8	 0.000	 0.000%	 100.000%	 0.000	 0
Timings (microseconds): count=52 first=224 curr=1869070 min=224 max=2089397 avg=1.85169e+06 std=373988
Memory (bytes): count=0
171 nodes observed
x86_64 deeplab_mnv3_large_weight_quant_769.tflite Benchmark
Number of nodes executed: 194
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 51	 4123.348	 82.616%	 82.616%	 0.000	 0
	 DEPTHWISE_CONV_2D	 15	 628.139	 12.586%	 95.202%	 0.000	 0
	 HARD_SWISH	 15	 90.448	 1.812%	 97.014%	 0.000	 0
	 MUL	 32	 29.393	 0.589%	 97.603%	 0.000	 0
	 ARG_MAX	 1	 22.866	 0.458%	 98.061%	 0.000	 0
	 ADD	 25	 22.860	 0.458%	 98.519%	 0.000	 0
	 RESIZE_BILINEAR	 5	 22.494	 0.451%	 98.970%	 0.000	 0
	 SPACE_TO_BATCH_ND	 8	 18.518	 0.371%	 99.341%	 0.000	 0
	 BATCH_TO_SPACE_ND	 8	 15.522	 0.311%	 99.652%	 0.000	 0
	 AVERAGE_POOL_2D	 9	 7.855	 0.157%	 99.809%	 0.000	 0
	 SUB	 2	 5.896	 0.118%	 99.928%	 0.000	 0
	 RESHAPE	 6	 2.133	 0.043%	 99.970%	 0.000	 0
	 PAD	 1	 0.631	 0.013%	 99.983%	 0.000	 0
	 CAST	 2	 0.575	 0.012%	 99.994%	 0.000	 0
	 STRIDED_SLICE	 1	 0.260	 0.005%	 100.000%	 0.000	 0
	 Misc Runtime Ops	 1	 0.012	 0.000%	 100.000%	 38.304	 0
	 DEQUANTIZE	 12	 0.003	 0.000%	 100.000%	 0.000	 0
Timings (microseconds): count=31 first=193 curr=5276579 min=193 max=5454605 avg=4.99104e+06 std=1311782
Memory (bytes): count=0
194 nodes observed
Ubuntu 19.10 aarch64 + RaspberryPi4 deeplab_v3_plus_mnv3_decoder_256_integer_quant.tflite Benchmark
Number of nodes executed: 180
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 38	 37.595	 45.330%	 45.330%	 0.000	 38
	 ADD	 37	 12.319	 14.854%	 60.184%	 0.000	 37
	 DEPTHWISE_CONV_2D	 17	 11.424	 13.774%	 73.958%	 0.000	 17
	 RESIZE_BILINEAR	 4	 7.336	 8.845%	 82.804%	 0.000	 4
	 MUL	 9	 4.204	 5.069%	 87.873%	 0.000	 9
	 QUANTIZE	 13	 3.976	 4.794%	 92.667%	 0.000	 13
	 AVERAGE_POOL_2D	 9	 1.809	 2.181%	 94.848%	 0.000	 9
	 DIV	 9	 1.167	 1.407%	 96.255%	 0.000	 9
	 ARG_MAX	 1	 1.137	 1.371%	 97.626%	 0.000	 1
	 CONCATENATION	 2	 0.780	 0.940%	 98.566%	 0.000	 2
	 FULLY_CONNECTED	 16	 0.715	 0.862%	 99.428%	 0.000	 16
	 DEQUANTIZE	 9	 0.473	 0.570%	 99.999%	 0.000	 9
	 RESHAPE	 16	 0.001	 0.001%	 100.000%	 0.000	 16
Timings (microseconds): count=50 first=83065 curr=82874 min=82675 max=85743 avg=83036 std=499
Memory (bytes): count=0
180 nodes observed
Ubuntu 19.10 aarch64 + RaspberryPi4 deeplab_v3_plus_mnv2_decoder_256_integer_quant.tflite Benchmark
Number of nodes executed: 81
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 41	 47.427	 65.530%	 65.530%	 0.000	 41
	 DEPTHWISE_CONV_2D	 19	 11.114	 15.356%	 80.887%	 0.000	 19
	 RESIZE_BILINEAR	 4	 7.342	 10.145%	 91.031%	 0.000	 4
	 QUANTIZE	 3	 2.953	 4.080%	 95.112%	 0.000	 3
	 ADD	 10	 1.633	 2.256%	 97.368%	 0.000	 10
	 ARG_MAX	 1	 1.137	 1.571%	 98.939%	 0.000	 1
	 CONCATENATION	 2	 0.736	 1.017%	 99.956%	 0.000	 2
	 AVERAGE_POOL_2D	 1	 0.032	 0.044%	 100.000%	 0.000	 1
Timings (microseconds): count=50 first=72544 curr=72425 min=72157 max=72745 avg=72412.9 std=137
Memory (bytes): count=0
81 nodes observed
Ubuntu 19.10 aarch64 + RaspberryPi4 mobilenet_v3_small_full_integer_quant.tflite Benchmark
Number of nodes executed: 176
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 61	 10.255	 36.582%	 36.582%	 0.000	 61
	 DEPTHWISE_CONV_2D	 27	 5.058	 18.043%	 54.625%	 0.000	 27
	 MUL	 26	 5.056	 18.036%	 72.661%	 0.000	 26
	 ADD	 14	 4.424	 15.781%	 88.442%	 0.000	 14
	 QUANTIZE	 13	 1.633	 5.825%	 94.267%	 0.000	 13
	 HARD_SWISH	 10	 0.918	 3.275%	 97.542%	 0.000	 10
	 LOGISTIC	 1	 0.376	 1.341%	 98.883%	 0.000	 1
	 AVERAGE_POOL_2D	 9	 0.199	 0.710%	 99.593%	 0.000	 9
	 CONCATENATION	 2	 0.084	 0.300%	 99.893%	 0.000	 2
	 RESHAPE	 13	 0.030	 0.107%	 100.000%	 0.000	 13
Timings (microseconds): count=50 first=28827 curr=28176 min=27916 max=28827 avg=28121.2 std=165
Memory (bytes): count=0
176 nodes observed
Ubuntu 19.10 aarch64 + RaspberryPi4 mobilenet_v3_small_weight_quant.tflite Benchmark
Number of nodes executed: 186
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 61	 82.600	 79.265%	 79.265%	 0.000	 61
	 DEPTHWISE_CONV_2D	 27	 8.198	 7.867%	 87.132%	 0.000	 27
	 MUL	 26	 4.866	 4.670%	 91.802%	 0.000	 26
	 ADD	 14	 4.863	 4.667%	 96.469%	 0.000	 14
	 LOGISTIC	 1	 1.645	 1.579%	 98.047%	 0.000	 1
	 AVERAGE_POOL_2D	 9	 0.761	 0.730%	 98.777%	 0.000	 9
	 HARD_SWISH	 10	 0.683	 0.655%	 99.433%	 0.000	 10
	 CONCATENATION	 2	 0.415	 0.398%	 99.831%	 0.000	 2
	 RESHAPE	 13	 0.171	 0.164%	 99.995%	 0.000	 13
	 DEQUANTIZE	 23	 0.005	 0.005%	 100.000%	 0.000	 23
Timings (microseconds): count=50 first=103867 curr=103937 min=103708 max=118926 avg=104299 std=2254
Memory (bytes): count=0
186 nodes observed
Ubuntu 19.10 aarch64 + RaspberryPi4 Posenet model-mobilenet_v1_101_257_integer_quant.tflite Benchmark
Number of nodes executed: 38
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 18	 31.906	 83.360%	 83.360%	 0.000	 0
	 DEPTHWISE_CONV_2D	 13	 5.959	 15.569%	 98.929%	 0.000	 0
	 QUANTIZE	 1	 0.223	 0.583%	 99.511%	 0.000	 0
	 Misc Runtime Ops	 1	 0.148	 0.387%	 99.898%	 96.368	 0
	 DEQUANTIZE	 4	 0.030	 0.078%	 99.976%	 0.000	 0
	 LOGISTIC	 1	 0.009	 0.024%	 100.000%	 0.000	 0
Timings (microseconds): count=70 first=519 curr=53370 min=519 max=53909 avg=38296 std=23892
Memory (bytes): count=0
38 nodes observed
Ubuntu 19.10 aarch64 + RaspberryPi4 MobileNetV2-SSDLite ssdlite_mobilenet_v2_coco_300_integer_quant.tflite Benchmark
Number of nodes executed: 128
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 55	 27.253	 71.185%	 71.185%	 0.000	 0
	 DEPTHWISE_CONV_2D	 33	 8.024	 20.959%	 92.143%	 0.000	 0
	 ADD	 10	 1.565	 4.088%	 96.231%	 0.000	 0
	 QUANTIZE	 11	 0.546	 1.426%	 97.657%	 0.000	 0
	 Misc Runtime Ops	 1	 0.368	 0.961%	 98.618%	 250.288	 0
	 LOGISTIC	 1	 0.253	 0.661%	 99.279%	 0.000	 0
	 DEQUANTIZE	 2	 0.168	 0.439%	 99.718%	 0.000	 0
	 CONCATENATION	 2	 0.077	 0.201%	 99.919%	 0.000	 0
	 RESHAPE	 13	 0.031	 0.081%	 100.000%	 0.000	 0
Timings (microseconds): count=70 first=1289 curr=53049 min=1289 max=53590 avg=38345.2 std=23436
Memory (bytes): count=0
128 nodes observed
Ubuntu 19.10 aarch64 + RaspberryPi4 ml-sound-classifier mobilenetv2_fsd2018_41cls_weight_quant.tflite Benchmark
Number of nodes executed: 111
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 MINIMUM	 35	 10.020	 45.282%	 45.282%	 0.000	 35
	 CONV_2D	 34	 8.376	 37.852%	 83.134%	 0.000	 34
	 DEPTHWISE_CONV_2D	 18	 1.685	 7.615%	 90.749%	 0.000	 18
	 MEAN	 1	 1.422	 6.426%	 97.176%	 0.000	 1
	 FULLY_CONNECTED	 2	 0.589	 2.662%	 99.837%	 0.000	 2
	 ADD	 10	 0.031	 0.140%	 99.977%	 0.000	 10
	 SOFTMAX	 1	 0.005	 0.023%	 100.000%	 0.000	 1
	 DEQUANTIZE	 10	 0.000	 0.000%	 100.000%	 0.000	 10
Timings (microseconds): count=50 first=22417 curr=22188 min=22041 max=22417 avg=22182 std=70
Memory (bytes): count=0
111 nodes observed
Ubuntu 19.10 aarch64 + RaspberryPi4 ml-sound-classifier mobilenetv2_fsd2018_41cls_integer_quant.tflite Benchmark
Number of nodes executed: 173
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 QUANTIZE	 70	 1.117	 23.281%	 23.281%	 0.000	 0
	 MINIMUM	 35	 1.104	 23.010%	 46.290%	 0.000	 0
	 CONV_2D	 34	 0.866	 18.049%	 64.339%	 0.000	 0
	 MEAN	 1	 0.662	 13.797%	 78.137%	 0.000	 0
	 DEPTHWISE_CONV_2D	 18	 0.476	 9.921%	 88.058%	 0.000	 0
	 FULLY_CONNECTED	 2	 0.251	 5.231%	 93.289%	 0.000	 0
	 Misc Runtime Ops	 1	 0.250	 5.211%	 98.499%	 71.600	 0
	 ADD	 10	 0.071	 1.480%	 99.979%	 0.000	 0
	 SOFTMAX	 1	 0.001	 0.021%	 100.000%	 0.000	 0
	 DEQUANTIZE	 1	 0.000	 0.000%	 100.000%	 0.000	 0
Timings (microseconds): count=198 first=477 curr=9759 min=477 max=10847 avg=4876.6 std=4629
Memory (bytes): count=0
173 nodes observed
Raspbian Buster aarch64 + RaspberryPi4 deeplabv3_mnv2_pascal_trainval_257_integer_quant.tflite Benchmark
Number of nodes executed: 82
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 38	 103.576	 56.077%	 56.077%	 0.000	 38
	 DEPTHWISE_CONV_2D	 17	 33.151	 17.948%	 74.026%	 0.000	 17
	 RESIZE_BILINEAR	 3	 15.143	 8.199%	 82.224%	 0.000	 3
	 SUB	 2	 10.908	 5.906%	 88.130%	 0.000	 2
	 ADD	 11	 9.821	 5.317%	 93.447%	 0.000	 11
	 ARG_MAX	 1	 8.824	 4.777%	 98.225%	 0.000	 1
	 PAD	 1	 1.024	 0.554%	 98.779%	 0.000	 1
	 QUANTIZE	 2	 0.941	 0.509%	 99.289%	 0.000	 2
	 MUL	 1	 0.542	 0.293%	 99.582%	 0.000	 1
	 CONCATENATION	 1	 0.365	 0.198%	 99.780%	 0.000	 1
	 AVERAGE_POOL_2D	 1	 0.150	 0.081%	 99.861%	 0.000	 1
	 RESHAPE	 2	 0.129	 0.070%	 99.931%	 0.000	 2
	 EXPAND_DIMS	 2	 0.128	 0.069%	 100.000%	 0.000	 2
Timings (microseconds): count=50 first=201226 curr=176476 min=176476 max=201226 avg=184741 std=4791
Memory (bytes): count=0
82 nodes observed
Ubuntu 18.04 x86_64 + XNNPACK enabled + 10 Threads deeplabv3_257_mv_gpu.tflite Benchmark
Number of nodes executed: 8
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 DELEGATE	 3	 6.716	 61.328%	 61.328%	 0.000	 3
	 RESIZE_BILINEAR	 3	 3.965	 36.207%	 97.534%	 0.000	 3
	 CONCATENATION	 1	 0.184	 1.680%	 99.215%	 0.000	 1
	 AVERAGE_POOL_2D	 1	 0.086	 0.785%	 100.000%	 0.000	 1
Timings (microseconds): count=91 first=11051 curr=10745 min=10521 max=12552 avg=10955.4 std=352
Memory (bytes): count=0
8 nodes observed
Note: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.
Peak memory footprint (MB): init=3.58203 overall=56.0703
Ubuntu 18.04 x86_64 + XNNPACK disabled + 10 Threads deeplabv3_257_mv_gpu.tflite Benchmark
Number of nodes executed: 70
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 DEPTHWISE_CONV_2D	 17	 41.704	 68.372%	 68.372%	 0.000	 17
	 CONV_2D	 38	 15.932	 26.120%	 94.491%	 0.000	 38
	 RESIZE_BILINEAR	 3	 3.060	 5.017%	 99.508%	 0.000	 3
	 ADD	 10	 0.149	 0.244%	 99.752%	 0.000	 10
	 CONCATENATION	 1	 0.109	 0.179%	 99.931%	 0.000	 1
	 AVERAGE_POOL_2D	 1	 0.042	 0.069%	 100.000%	 0.000	 1
Timings (microseconds): count=50 first=59929 curr=60534 min=59374 max=63695 avg=61031.6 std=1182
Memory (bytes): count=0
70 nodes observed
Note: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.
Peak memory footprint (MB): init=0 overall=13.7109
Ubuntu 18.04 x86_64 + XNNPACK enabled + 4 Threads Faster-Grad-CAM weights_weight_quant.tflite Benchmark
umber of nodes executed: 74
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 31	 4.947	 77.588%	 77.588%	 0.000	 31
	 DELEGATE	 17	 0.689	 10.806%	 88.394%	 0.000	 17
	 DEPTHWISE_CONV_2D	 10	 0.591	 9.269%	 97.663%	 0.000	 10
	 MEAN	 1	 0.110	 1.725%	 99.388%	 0.000	 1
	 PAD	 5	 0.039	 0.612%	 100.000%	 0.000	 5
	 DEQUANTIZE	 10	 0.000	 0.000%	 100.000%	 0.000	 10
Timings (microseconds): count=155 first=6415 curr=6443 min=6105 max=6863 avg=6409.22 std=69
Memory (bytes): count=0
74 nodes observed
RaspberryPi4 + Ubuntu 19.10 aarch64 + 4 Threads Faster-Grad-CAM weights_integer_quant.tflite Benchmark
Number of nodes executed: 72
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 35	 0.753	 34.958%	 34.958%	 0.000	 0
	 PAD	 5	 0.395	 18.338%	 53.296%	 0.000	 0
	 MEAN	 1	 0.392	 18.199%	 71.495%	 0.000	 0
	 Misc Runtime Ops	 1	 0.282	 13.092%	 84.587%	 89.232	 0
	 DEPTHWISE_CONV_2D	 17	 0.251	 11.653%	 96.240%	 0.000	 0
	 ADD	 10	 0.054	 2.507%	 98.747%	 0.000	 0
	 QUANTIZE	 1	 0.024	 1.114%	 99.861%	 0.000	 0
	 DEQUANTIZE	 2	 0.003	 0.139%	 100.000%	 0.000	 0
Timings (microseconds): count=472 first=564 curr=3809 min=564 max=3950 avg=2188.51 std=1625
Memory (bytes): count=0
72 nodes observed
Ubuntu 18.04 x86_64 + XNNPACK enabled + 4 Threads EfficientNet-lite efficientnet-lite0-fp32.tflite Benchmark
Number of nodes executed: 5
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 DELEGATE	 2	 5.639	 95.706%	 95.706%	 0.000	 2
	 FULLY_CONNECTED	 1	 0.239	 4.056%	 99.762%	 0.000	 1
	 AVERAGE_POOL_2D	 1	 0.014	 0.238%	 100.000%	 0.000	 1
	 RESHAPE	 1	 0.000	 0.000%	 100.000%	 0.000	 1
Timings (microseconds): count=168 first=5842 curr=5910 min=5749 max=6317 avg=5894.55 std=100
Memory (bytes): count=0
5 nodes observed
Ubuntu 18.04 x86_64 + XNNPACK enabled + 4 Threads EfficientNet-lite efficientnet-lite4-fp32.tflite Benchmark
Number of nodes executed: 5
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 DELEGATE	 2	 33.720	 99.235%	 99.235%	 0.000	 2
	 FULLY_CONNECTED	 1	 0.231	 0.680%	 99.915%	 0.000	 1
	 AVERAGE_POOL_2D	 1	 0.029	 0.085%	 100.000%	 0.000	 1
	 RESHAPE	 1	 0.000	 0.000%	 100.000%	 0.000	 1
Timings (microseconds): count=50 first=32459 curr=34867 min=31328 max=35730 avg=33983.5 std=1426
Memory (bytes): count=0
5 nodes observed
Ubuntu 18.04 x86_64 + XNNPACK enabled + 4 Threads White-box-Cartoonization white_box_cartoonization_weight_quant.tflite Benchmark
Number of nodes executed: 47
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 18	 10731.842	 97.293%	 97.293%	 0.000	 18
	 LEAKY_RELU	 13	 236.792	 2.147%	 99.440%	 0.000	 13
	 TfLiteXNNPackDelegate	 10	 45.534	 0.413%	 99.853%	 0.000	 10
	 RESIZE_BILINEAR	 2	 11.237	 0.102%	 99.954%	 0.000	 2
	 SUB	 3	 4.053	 0.037%	 99.991%	 0.000	 3
	 DIV	 1	 0.977	 0.009%	 100.000%	 0.000	 1
Timings (microseconds): count=14 first=10866837 curr=11292015 min=10697744 max=12289882 avg=1.10305e+07 std=406791
Memory (bytes): count=0
47 nodes observed
RaspberryPi4 + Ubuntu 19.10 aarch64 + 4 Threads edgetpu_deeplab_257_os16_integer_quant.tflite Benchmark
Number of nodes executed: 91
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 49	 54.679	 58.810%	 58.810%	 0.000	 49
	 SUB	 2	 11.043	 11.877%	 70.687%	 0.000	 2
	 ADD	 16	 8.909	 9.582%	 80.269%	 0.000	 16
	 ARG_MAX	 1	 7.184	 7.727%	 87.996%	 0.000	 1
	 RESIZE_BILINEAR	 3	 6.654	 7.157%	 95.153%	 0.000	 3
	 DEPTHWISE_CONV_2D	 13	 3.409	 3.667%	 98.819%	 0.000	 13
	 MUL	 1	 0.548	 0.589%	 99.408%	 0.000	 1
	 QUANTIZE	 2	 0.328	 0.353%	 99.761%	 0.000	 2
	 RESHAPE	 2	 0.162	 0.174%	 99.935%	 0.000	 2
	 AVERAGE_POOL_2D	 1	 0.043	 0.046%	 99.982%	 0.000	 1
	 CONCATENATION	 1	 0.017	 0.018%	 100.000%	 0.000	 1
Timings (microseconds): count=50 first=92752 curr=93058 min=92533 max=94478 avg=93021.2 std=274
Memory (bytes): count=0
91 nodes observed
RaspberryPi4 + Ubuntu 19.10 aarch64 + 4 Threads edgetpu_deeplab_257_os32_integer_quant.tflite Benchmark
Number of nodes executed: 91
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 49	 39.890	 52.335%	 52.335%	 0.000	 49
	 SUB	 2	 11.043	 14.488%	 66.823%	 0.000	 2
	 ADD	 16	 8.064	 10.580%	 77.403%	 0.000	 16
	 ARG_MAX	 1	 7.011	 9.198%	 86.601%	 0.000	 1
	 RESIZE_BILINEAR	 3	 6.623	 8.689%	 95.290%	 0.000	 3
	 DEPTHWISE_CONV_2D	 13	 2.503	 3.284%	 98.574%	 0.000	 13
	 MUL	 1	 0.544	 0.714%	 99.288%	 0.000	 1
	 QUANTIZE	 2	 0.313	 0.411%	 99.698%	 0.000	 2
	 RESHAPE	 2	 0.178	 0.234%	 99.932%	 0.000	 2
	 AVERAGE_POOL_2D	 1	 0.041	 0.054%	 99.986%	 0.000	 1
	 CONCATENATION	 1	 0.011	 0.014%	 100.000%	 0.000	 1
Timings (microseconds): count=50 first=75517 curr=75558 min=75517 max=97776 avg=76262.5 std=3087
Memory (bytes): count=0
91 nodes observed
RaspberryPi4 + Ubuntu 19.10 aarch64 + 4 Threads human_pose_estimation_3d_0001_256x448_integer_quant.tflite Benchmark
Number of nodes executed: 165
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 69	 343.433	 78.638%	 78.638%	 0.000	 69
	 PAD	 38	 51.637	 11.824%	 90.462%	 0.000	 38
	 DEPTHWISE_CONV_2D	 14	 15.306	 3.505%	 93.967%	 0.000	 14
	 ADD	 15	 14.535	 3.328%	 97.295%	 0.000	 15
	 ELU	 6	 5.071	 1.161%	 98.456%	 0.000	 6
	 QUANTIZE	 11	 4.481	 1.026%	 99.482%	 0.000	 11
	 DEQUANTIZE	 9	 1.851	 0.424%	 99.906%	 0.000	 9
	 CONCATENATION	 3	 0.410	 0.094%	 100.000%	 0.000	 3
Timings (microseconds): count=50 first=425038 curr=423469 min=421348 max=969226 avg=436808 std=77255
Memory (bytes): count=0
165 nodes observed
RaspberryPi4 + Ubuntu 19.10 aarch64 + 4 Threads + BlazeFace face_detection_front_128_integer_quant.tflite Benchmark
Number of nodes executed: 79
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 ADD	 16	 2.155	 34.120%	 34.120%	 0.000	 16
	 CONV_2D	 21	 2.017	 31.935%	 66.054%	 0.000	 21
	 PAD	 11	 1.014	 16.054%	 82.109%	 0.000	 11
	 DEPTHWISE_CONV_2D	 16	 0.765	 12.112%	 94.221%	 0.000	 16
	 QUANTIZE	 4	 0.186	 2.945%	 97.166%	 0.000	 4
	 MAX_POOL_2D	 3	 0.153	 2.422%	 99.588%	 0.000	 3
	 DEQUANTIZE	 2	 0.017	 0.269%	 99.857%	 0.000	 2
	 CONCATENATION	 2	 0.006	 0.095%	 99.952%	 0.000	 2
	 RESHAPE	 4	 0.003	 0.047%	 100.000%	 0.000	 4
Timings (microseconds): count=144 first=6415 curr=6319 min=6245 max=6826 avg=6359.12 std=69
Memory (bytes): count=0
79 nodes observed
RaspberryPi4 + Ubuntu 19.10 aarch64 + 4 Threads + ssd_mobilenet_v2_mnasfpn_shared_box_predictor_320_coco_integer_quant.tflite Benchmark
Number of nodes executed: 588
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 119	 109.253	 52.671%	 52.671%	 0.000	 119
	 DEPTHWISE_CONV_2D	 61	 33.838	 16.313%	 68.984%	 0.000	 61
	TFLite_Detection_PostProcess	 1	 22.711	 10.949%	 79.933%	 0.000	 1
	 LOGISTIC	 1	 17.696	 8.531%	 88.465%	 0.000	 1
	 ADD	 59	 12.300	 5.930%	 94.395%	 0.000	 59
	 RESHAPE	 8	 4.175	 2.013%	 96.407%	 0.000	 8
	 CONCATENATION	 2	 3.416	 1.647%	 98.054%	 0.000	 2
	 RESIZE_NEAREST_NEIGHBOR	 12	 1.873	 0.903%	 98.957%	 0.000	 12
	 MAX_POOL_2D	 13	 1.363	 0.657%	 99.614%	 0.000	 13
	 MUL	 16	 0.737	 0.355%	 99.970%	 0.000	 16
	 DEQUANTIZE	 296	 0.063	 0.030%	 100.000%	 0.000	 296
Timings (microseconds): count=50 first=346007 curr=196005 min=192539 max=715157 avg=207709 std=75605
Memory (bytes): count=0
588 nodes observed
RaspberryPi4 + Ubuntu 19.10 aarch64 + 4 Threads + object_detection_3d_chair_640x480_integer_quant.tflite Benchmark
Number of nodes executed: 126
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 60	 146.537	 63.805%	 63.805%	 0.000	 60
	 DEPTHWISE_CONV_2D	 26	 45.022	 19.604%	 83.409%	 0.000	 26
	 ADD	 23	 23.393	 10.186%	 93.595%	 0.000	 23
	 TRANSPOSE_CONV	 3	 9.930	 4.324%	 97.918%	 0.000	 3
	 QUANTIZE	 5	 3.103	 1.351%	 99.269%	 0.000	 5
	 CONCATENATION	 4	 1.541	 0.671%	 99.940%	 0.000	 4
	 DEQUANTIZE	 3	 0.117	 0.051%	 99.991%	 0.000	 3
	 EXP	 1	 0.018	 0.008%	 99.999%	 0.000	 1
	 NEG	 1	 0.002	 0.001%	 100.000%	 0.000	 1
Timings (microseconds): count=50 first=218224 curr=217773 min=217174 max=649357 avg=229732 std=62952
Memory (bytes): count=0
126 nodes observed
RaspberryPi4 + Ubuntu 19.10 aarch64 + 4 Threads + ssdlite_mobiledet_cpu_320x320_coco_integer_quant.tflite Benchmark
Number of nodes executed: 288
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 96	 22.996	 33.342%	 33.342%	 0.000	 96
	 HARD_SWISH	 57	 11.452	 16.604%	 49.946%	 0.000	 57
	 MUL	 19	 9.423	 13.662%	 63.608%	 0.000	 19
	 AVERAGE_POOL_2D	 19	 8.439	 12.236%	 75.843%	 0.000	 19
	 DEPTHWISE_CONV_2D	 35	 7.810	 11.324%	 87.167%	 0.000	 35
	TFLite_Detection_PostProcess	 1	 5.650	 8.192%	 95.359%	 0.000	 1
	 ADD	 12	 1.690	 2.450%	 97.809%	 0.000	 12
	 QUANTIZE	 12	 0.879	 1.274%	 99.084%	 0.000	 12
	 LOGISTIC	 20	 0.277	 0.402%	 99.485%	 0.000	 20
	 DEQUANTIZE	 2	 0.234	 0.339%	 99.825%	 0.000	 2
	 CONCATENATION	 2	 0.079	 0.115%	 99.939%	 0.000	 2
	 RESHAPE	 13	 0.042	 0.061%	 100.000%	 0.000	 13
Timings (microseconds): count=50 first=69091 curr=68590 min=68478 max=83971 avg=69105.3 std=2147
Memory (bytes): count=0
288 nodes observed
RaspberryPi4 + Ubuntu 19.10 aarch64 + 4 Threads + mobilenet_v2_pose_256_256_dm100_integer_quant.tflite Benchmark
Number of nodes executed: 189
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 86	 51.819	 70.575%	 70.575%	 0.000	 86
	 DEPTHWISE_CONV_2D	 73	 18.207	 24.797%	 95.372%	 0.000	 73
	 ADD	 8	 1.243	 1.693%	 97.065%	 0.000	 8
	 QUANTIZE	 13	 1.132	 1.542%	 98.607%	 0.000	 13
	 CONCATENATION	 7	 0.607	 0.827%	 99.433%	 0.000	 7
	 RESIZE_BILINEAR	 1	 0.354	 0.482%	 99.916%	 0.000	 1
	 DEQUANTIZE	 1	 0.062	 0.084%	 100.000%	 0.000	 1
Timings (microseconds): count=50 first=73752 curr=73430 min=73191 max=75764 avg=73524.8 std=485
Memory (bytes): count=0
189 nodes observed
RaspberryPi4 + Ubuntu 19.10 aarch64 + 4 Threads + mobilenet_v2_pose_368_432_dm100_integer_quant.tflite Benchmark
Number of nodes executed: 189
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 86	 141.296	 69.289%	 69.289%	 0.000	 86
	 DEPTHWISE_CONV_2D	 73	 53.244	 26.110%	 95.399%	 0.000	 73
	 QUANTIZE	 13	 3.059	 1.500%	 96.899%	 0.000	 13
	 ADD	 8	 3.014	 1.478%	 98.377%	 0.000	 8
	 CONCATENATION	 7	 2.302	 1.129%	 99.506%	 0.000	 7
	 RESIZE_BILINEAR	 1	 0.852	 0.418%	 99.924%	 0.000	 1
	 DEQUANTIZE	 1	 0.155	 0.076%	 100.000%	 0.000	 1
Timings (microseconds): count=50 first=189613 curr=579873 min=189125 max=579873 avg=204021 std=70304
Memory (bytes): count=0
189 nodes observed
RaspberryPi4 + Ubuntu 19.10 aarch64 + 4 Threads + mobilenet_v2_pose_256_256_dm050_integer_quant.tflite Benchmark
Number of nodes executed: 189
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 86	 40.952	 71.786%	 71.786%	 0.000	 86
	 DEPTHWISE_CONV_2D	 73	 13.508	 23.679%	 95.465%	 0.000	 73
	 QUANTIZE	 13	 1.123	 1.969%	 97.434%	 0.000	 13
	 ADD	 8	 0.710	 1.245%	 98.678%	 0.000	 8
	 CONCATENATION	 7	 0.498	 0.873%	 99.551%	 0.000	 7
	 RESIZE_BILINEAR	 1	 0.193	 0.338%	 99.890%	 0.000	 1
	 DEQUANTIZE	 1	 0.063	 0.110%	 100.000%	 0.000	 1
Timings (microseconds): count=50 first=57027 curr=57048 min=56773 max=58042 avg=57135 std=229
Memory (bytes): count=0
189 nodes observed
RaspberryPi4 + Ubuntu 19.10 aarch64 + 4 Threads + mobilenet_v2_pose_368_432_dm050_integer_quant.tflite Benchmark
Number of nodes executed: 189
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 86	 104.618	 71.523%	 71.523%	 0.000	 86
	 DEPTHWISE_CONV_2D	 73	 34.527	 23.605%	 95.128%	 0.000	 73
	 QUANTIZE	 13	 2.572	 1.758%	 96.886%	 0.000	 13
	 CONCATENATION	 7	 2.257	 1.543%	 98.429%	 0.000	 7
	 ADD	 8	 1.683	 1.151%	 99.580%	 0.000	 8
	 RESIZE_BILINEAR	 1	 0.460	 0.314%	 99.894%	 0.000	 1
	 DEQUANTIZE	 1	 0.155	 0.106%	 100.000%	 0.000	 1
Timings (microseconds): count=50 first=172545 curr=146065 min=145260 max=172545 avg=146362 std=3756
Memory (bytes): count=0
189 nodes observed
RaspberryPi4 + Ubuntu 19.10 aarch64 + 4 Threads + yolov4_tiny_voc_416x416_integer_quant.tflite Benchmark
Number of nodes executed: 71
============================== Summary by node type ==============================
	 [Node type]	 [count]	 [avg ms]	 [avg %]	 [cdf %]	 [mem KB]	[times called]
	 CONV_2D	 21	 149.092	 61.232%	 61.232%	 0.000	 21
	 LEAKY_RELU	 19	 77.644	 31.888%	 93.121%	 0.000	 19
	 PAD	 2	 8.036	 3.300%	 96.421%	 0.000	 2
	 QUANTIZE	 10	 4.580	 1.881%	 98.302%	 0.000	 10
	 CONCATENATION	 7	 2.415	 0.992%	 99.294%	 0.000	 7
	 MAX_POOL_2D	 3	 0.982	 0.403%	 99.697%	 0.000	 3
	 SPLIT	 3	 0.615	 0.253%	 99.950%	 0.000	 3
	 DEQUANTIZE	 2	 0.082	 0.034%	 99.984%	 0.000	 2
	 RESIZE_NEAREST_NEIGHBOR	 1	 0.032	 0.013%	 99.997%	 0.000	 1
	 STRIDED_SLICE	 1	 0.004	 0.002%	 99.998%	 0.000	 1
	 MUL	 1	 0.004	 0.002%	 100.000%	 0.000	 1
	 SHAPE	 1	 0.000	 0.000%	 100.000%	 0.000	 1
Timings (microseconds): count=50 first=233307 curr=233318 min=232446 max=364068 avg=243522 std=33354
Memory (bytes): count=0
71 nodes observed

4. Reference articles

  1. [deeplab] what's the parameters of the mobilenetv3 pretrained model?
  2. When you want to fine-tune DeepLab on other datasets, there are a few cases
  3. [deeplab] Training deeplab model with ADE20K dataset
  4. Running DeepLab on PASCAL VOC 2012 Semantic Segmentation Dataset
  5. Quantize DeepLab model for faster on-device inference
  6. https://github.com/tensorflow/models/blob/main/research/deeplab/g3doc/model_zoo.md
  7. https://github.com/tensorflow/models/blob/main/research/deeplab/g3doc/quantize.md
  8. the quantized form of Shape operation is not yet implemented
  9. Post-training quantization
  10. Converter command line reference
  11. Quantization-aware training
  12. Converting a .pb file to .meta in TF 1.3
  13. Minimal code to load a trained TensorFlow model from a checkpoint and export it with SavedModelBuilder
  14. How to restore Tensorflow model from .pb file in python?
  15. Error with tag-sets when serving model using tensorflow_model_server tool
  16. ValueError: No 'serving_default' in the SavedModel's SignatureDefs. Possible values are 'name_of_my_model'
  17. kerasのモデルをデプロイする手順 - Signature作成方法解説
  18. TensorFlow で学習したモデルのグラフを tf.train.import_meta_graph でロードする
  19. Tensorflowのグラフ操作 Part1
  20. Configure input_map when importing a tensorflow model from metagraph file
  21. TFLite Model Benchmark Tool
  22. How to install Ubuntu 19.10 aarch64 (64bit) on RaspberryPi4
  23. https://github.com/rwightman/posenet-python.git
  24. https://github.com/sayakpaul/Adventures-in-TensorFlow-Lite.git

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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.

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