diff --git a/datumaro/plugins/openvino/README.md b/datumaro/plugins/openvino/README.md
new file mode 100644
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+# OpenVINO™ Inference Interpreter 
+Interpreter samples to parse OpenVINO™ inference outputs.
+
+## Models supported from interpreter samples
+There are detection and image classification examples. 
+    
+- Detection (SSD-based)
+  - Intel Pre-trained Models > Object Detection  
+    - [face-detection-0200](https://docs.openvinotoolkit.org/latest/omz_models_intel_face_detection_0200_description_face_detection_0200.html)
+    - [face-detection-0202](https://docs.openvinotoolkit.org/latest/omz_models_intel_face_detection_0202_description_face_detection_0202.html)
+    - [face-detection-0204](https://docs.openvinotoolkit.org/latest/omz_models_intel_face_detection_0204_description_face_detection_0204.html)
+    - [person-detection-0200](https://docs.openvinotoolkit.org/latest/omz_models_intel_person_detection_0200_description_person_detection_0200.html)
+    - [person-detection-0201](https://docs.openvinotoolkit.org/latest/omz_models_intel_person_detection_0201_description_person_detection_0201.html)
+    - [person-detection-0202](https://docs.openvinotoolkit.org/latest/omz_models_intel_person_detection_0202_description_person_detection_0202.html) 
+    - [person-vehicle-bike-detection-2000](https://docs.openvinotoolkit.org/latest/omz_models_intel_person_vehicle_bike_detection_2000_description_person_vehicle_bike_detection_2000.html)
+    - [person-vehicle-bike-detection-2001](https://docs.openvinotoolkit.org/latest/omz_models_intel_person_vehicle_bike_detection_2001_description_person_vehicle_bike_detection_2001.html)
+    - [person-vehicle-bike-detection-2002](https://docs.openvinotoolkit.org/latest/omz_models_intel_person_vehicle_bike_detection_2002_description_person_vehicle_bike_detection_2002.html)
+    - [vehicle-detection-0200](https://docs.openvinotoolkit.org/latest/omz_models_intel_vehicle_detection_0200_description_vehicle_detection_0200.html)
+    - [vehicle-detection-0201](https://docs.openvinotoolkit.org/latest/omz_models_intel_vehicle_detection_0201_description_vehicle_detection_0201.html)
+    - [vehicle-detection-0202](https://docs.openvinotoolkit.org/latest/omz_models_intel_vehicle_detection_0202_description_vehicle_detection_0202.html)
+
+  - Public Pre-Trained Models(OMZ) > Object Detection
+    - [ssd_mobilenet_v1_coco](https://docs.openvinotoolkit.org/latest/omz_models_public_ssd_mobilenet_v1_coco_ssd_mobilenet_v1_coco.html)
+    - [ssd_mobilenet_v2_coco](https://docs.openvinotoolkit.org/latest/omz_models_public_ssd_mobilenet_v2_coco_ssd_mobilenet_v2_coco.html)
+
+- Image Classification
+  - Public Pre-Trained Models(OMZ) > Classification
+    - [mobilenet-v2-pytorch](https://docs.openvinotoolkit.org/latest/omz_models_public_mobilenet_v2_pytorch_mobilenet_v2_pytorch.html)
+
+You can find more OpenVINO™ Trained Models [here](https://docs.openvinotoolkit.org/latest/omz_models_intel_index.html)
+To run the inference with OpenVINO™, the model format should be Intermediate Representation(IR). 
+For the Caffe/TensorFlow/MXNet/Kaldi/ONNX models, please see the [Model Conversion Instruction](https://docs.openvinotoolkit.org/latest/openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model.html)
+
+You need to implement your own interpreter samples to support the other OpenVINO™ Trained Models.
+
+## Model download
+- Prerequisites
+    - OpenVINO™ (To install OpenVINO™, please see the [OpenVINO™ Installation Instruction](https://docs.openvinotoolkit.org/latest/openvino_docs_install_guides_installing_openvino_linux.html))
+    - OpenVINO™ models (To download OpenVINO™ models, please see the [Model Downloader Instruction](https://docs.openvinotoolkit.org/latest/omz_tools_downloader_README.html))
+    - PASCAL VOC 2012 dataset (To download VOC 2012 dataset, please go [VOC2012 download](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/#devkit))
+
+  ```bash
+  # cd <openvino_dir>/deployment_tools/open_model_zoo/tools/downloader
+  # ./downloader.py --name <model_name>
+  #
+  # Examples
+  cd /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader
+  ./downloader.py --name face-detection-0200
+  ```
+
+## Model inference
+- Prerequisites: 
+    - OpenVINO™ (To install OpenVINO™, please see the [OpenVINO™ Installation Instruction](https://docs.openvinotoolkit.org/latest/openvino_docs_install_guides_installing_openvino_linux.html))
+    - Datumaro (To install Datumaro, please see the [User Manual](docs/user_manual.md))
+    - OpenVINO™ models (To download OpenVINO™ models, please see the [Model Downloader Instruction](https://docs.openvinotoolkit.org/latest/omz_tools_downloader_README.html))
+    - PASCAL VOC 2012 dataset (To download VOC 2012 dataset, please go [VOC2012 download](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/#devkit))
+    
+- To run the inference with OpenVINO™ models and the interpreter samples, please follow the instructions below.
+
+  ```bash
+  # source <openvino_dir>/bin/setupvars.sh
+  # datum create -o <proj_dir>
+  # datum model add -l <launcher> -p <proj_dir> --copy -- -d <path_to_xml> -w <path_to_bin> -i <path_to_interpreter_script>
+  # datum add path -p <proj_dir> -f <format> <path_to_dataset>
+  # datum model run -p <proj_dir> -m model-0
+  #
+  # Examples
+  # Detection> ssd_mobilenet_v2_coco
+  source /opt/intel/openvino/bin/setupvars.sh
+  cd datumaro/plugins/openvino
+  datum create -o proj_ssd_mobilenet_v2_coco_detection
+  datum model add -l openvino -p proj_ssd_mobilenet_v2_coco_detection --copy -- \
+      --output-layers=do_ExpandDims_conf/sigmoid \
+      -d model/ssd_mobilenet_v2_coco.xml \
+      -w model/ssd_mobilenet_v2_coco.bin \
+      -i samples/ssd_mobilenet_coco_detection_interp.py
+  datum add path -p proj_ssd_mobilenet_v2_coco_detection -f voc VOCdevkit/
+  datum model run -p proj_ssd_mobilenet_v2_coco_detection -m model-0
+  
+  # Classification> mobilenet-v2-pytorch
+  source /opt/intel/openvino/bin/setupvars.sh
+  cd datumaro/plugins/openvino
+  datum create -o proj_mobilenet_v2_classification
+  datum model add -l openvino -p proj_mobilenet_v2_classification --copy -- \
+      -d model/mobilenet-v2-pytorch.xml \
+      -w model/mobilenet-v2-pytorch.bin \
+      -i samples/mobilenet_v2_pytorch_interp.py
+  datum add path -p proj_mobilenet_v2_classification -f voc VOCdevkit/
+  datum model run -p proj_mobilenet_v2_classification -m model-0
+  ```
\ No newline at end of file
diff --git a/datumaro/plugins/openvino/__init__.py b/datumaro/plugins/openvino/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/datumaro/plugins/openvino_launcher.py b/datumaro/plugins/openvino/launcher.py
similarity index 100%
rename from datumaro/plugins/openvino_launcher.py
rename to datumaro/plugins/openvino/launcher.py
diff --git a/datumaro/plugins/openvino/samples/coco.class b/datumaro/plugins/openvino/samples/coco.class
new file mode 100644
index 0000000000..9682e2b88c
--- /dev/null
+++ b/datumaro/plugins/openvino/samples/coco.class
@@ -0,0 +1,91 @@
+person
+bicycle
+car
+motorcycle
+airplane
+bus
+train
+truck
+boat
+trafficlight
+firehydrant
+streetsign
+stopsign
+parkingmeter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+hat
+backpack
+umbrella
+shoe
+eyeglasses
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sportsball
+kite
+baseballbat
+baseballglove
+skateboard
+surfboard
+tennisracket
+bottle
+plate
+wineglass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hotdog
+pizza
+donut
+cake
+chair
+couch
+pottedplant
+bed
+mirror
+diningtable
+window
+desk
+toilet
+door
+tv
+laptop
+mouse
+remote
+keyboard
+cellphone
+microwave
+oven
+toaster
+sink
+refrigerator
+blender
+book
+clock
+vase
+scissors
+teddybear
+hairdrier
+toothbrush
+hairbrush
\ No newline at end of file
diff --git a/datumaro/plugins/openvino/samples/imagenet.class b/datumaro/plugins/openvino/samples/imagenet.class
new file mode 100644
index 0000000000..2571d1b729
--- /dev/null
+++ b/datumaro/plugins/openvino/samples/imagenet.class
@@ -0,0 +1,1000 @@
+tench
+goldfish
+great white shark
+tiger shark
+hammerhead
+electric ray
+stingray
+cock
+hen
+ostrich
+brambling
+goldfinch
+house finch
+junco
+indigo bunting
+robin
+bulbul
+jay
+magpie
+chickadee
+water ouzel
+kite
+bald eagle
+vulture
+great grey owl
+European fire salamander
+common newt
+eft
+spotted salamander
+axolotl
+bullfrog
+tree frog
+tailed frog
+loggerhead
+leatherback turtle
+mud turtle
+terrapin
+box turtle
+banded gecko
+common iguana
+American chameleon
+whiptail
+agama
+frilled lizard
+alligator lizard
+Gila monster
+green lizard
+African chameleon
+Komodo dragon
+African crocodile
+American alligator
+triceratops
+thunder snake
+ringneck snake
+hognose snake
+green snake
+king snake
+garter snake
+water snake
+vine snake
+night snake
+boa constrictor
+rock python
+Indian cobra
+green mamba
+sea snake
+horned viper
+diamondback
+sidewinder
+trilobite
+harvestman
+scorpion
+black and gold garden spider
+barn spider
+garden spider
+black widow
+tarantula
+wolf spider
+tick
+centipede
+black grouse
+ptarmigan
+ruffed grouse
+prairie chicken
+peacock
+quail
+partridge
+African grey
+macaw
+sulphur-crested cockatoo
+lorikeet
+coucal
+bee eater
+hornbill
+hummingbird
+jacamar
+toucan
+drake
+red-breasted merganser
+goose
+black swan
+tusker
+echidna
+platypus
+wallaby
+koala
+wombat
+jellyfish
+sea anemone
+brain coral
+flatworm
+nematode
+conch
+snail
+slug
+sea slug
+chiton
+chambered nautilus
+Dungeness crab
+rock crab
+fiddler crab
+king crab
+American lobster
+spiny lobster
+crayfish
+hermit crab
+isopod
+white stork
+black stork
+spoonbill
+flamingo
+little blue heron
+American egret
+bittern
+crane
+limpkin
+European gallinule
+American coot
+bustard
+ruddy turnstone
+red-backed sandpiper
+redshank
+dowitcher
+oystercatcher
+pelican
+king penguin
+albatross
+grey whale
+killer whale
+dugong
+sea lion
+Chihuahua
+Japanese spaniel
+Maltese dog
+Pekinese
+Shih-Tzu
+Blenheim spaniel
+papillon
+toy terrier
+Rhodesian ridgeback
+Afghan hound
+basset
+beagle
+bloodhound
+bluetick
+black-and-tan coonhound
+Walker hound
+English foxhound
+redbone
+borzoi
+Irish wolfhound
+Italian greyhound
+whippet
+Ibizan hound
+Norwegian elkhound
+otterhound
+Saluki
+Scottish deerhound
+Weimaraner
+Staffordshire bullterrier
+American Staffordshire terrier
+Bedlington terrier
+Border terrier
+Kerry blue terrier
+Irish terrier
+Norfolk terrier
+Norwich terrier
+Yorkshire terrier
+wire-haired fox terrier
+Lakeland terrier
+Sealyham terrier
+Airedale
+cairn
+Australian terrier
+Dandie Dinmont
+Boston bull
+miniature schnauzer
+giant schnauzer
+standard schnauzer
+Scotch terrier
+Tibetan terrier
+silky terrier
+soft-coated wheaten terrier
+West Highland white terrier
+Lhasa
+flat-coated retriever
+curly-coated retriever
+golden retriever
+Labrador retriever
+Chesapeake Bay retriever
+German short-haired pointer
+vizsla
+English setter
+Irish setter
+Gordon setter
+Brittany spaniel
+clumber
+English springer
+Welsh springer spaniel
+cocker spaniel
+Sussex spaniel
+Irish water spaniel
+kuvasz
+schipperke
+groenendael
+malinois
+briard
+kelpie
+komondor
+Old English sheepdog
+Shetland sheepdog
+collie
+Border collie
+Bouvier des Flandres
+Rottweiler
+German shepherd
+Doberman
+miniature pinscher
+Greater Swiss Mountain dog
+Bernese mountain dog
+Appenzeller
+EntleBucher
+boxer
+bull mastiff
+Tibetan mastiff
+French bulldog
+Great Dane
+Saint Bernard
+Eskimo dog
+malamute
+Siberian husky
+dalmatian
+affenpinscher
+basenji
+pug
+Leonberg
+Newfoundland
+Great Pyrenees
+Samoyed
+Pomeranian
+chow
+keeshond
+Brabancon griffon
+Pembroke
+Cardigan
+toy poodle
+miniature poodle
+standard poodle
+Mexican hairless
+timber wolf
+white wolf
+red wolf
+coyote
+dingo
+dhole
+African hunting dog
+hyena
+red fox
+kit fox
+Arctic fox
+grey fox
+tabby
+tiger cat
+Persian cat
+Siamese cat
+Egyptian cat
+cougar
+lynx
+leopard
+snow leopard
+jaguar
+lion
+tiger
+cheetah
+brown bear
+American black bear
+ice bear
+sloth bear
+mongoose
+meerkat
+tiger beetle
+ladybug
+ground beetle
+long-horned beetle
+leaf beetle
+dung beetle
+rhinoceros beetle
+weevil
+fly
+bee
+ant
+grasshopper
+cricket
+walking stick
+cockroach
+mantis
+cicada
+leafhopper
+lacewing
+dragonfly
+damselfly
+admiral
+ringlet
+monarch
+cabbage butterfly
+sulphur butterfly
+lycaenid
+starfish
+sea urchin
+sea cucumber
+wood rabbit
+hare
+Angora
+hamster
+porcupine
+fox squirrel
+marmot
+beaver
+guinea pig
+sorrel
+zebra
+hog
+wild boar
+warthog
+hippopotamus
+ox
+water buffalo
+bison
+ram
+bighorn
+ibex
+hartebeest
+impala
+gazelle
+Arabian camel
+llama
+weasel
+mink
+polecat
+black-footed ferret
+otter
+skunk
+badger
+armadillo
+three-toed sloth
+orangutan
+gorilla
+chimpanzee
+gibbon
+siamang
+guenon
+patas
+baboon
+macaque
+langur
+colobus
+proboscis monkey
+marmoset
+capuchin
+howler monkey
+titi
+spider monkey
+squirrel monkey
+Madagascar cat
+indri
+Indian elephant
+African elephant
+lesser panda
+giant panda
+barracouta
+eel
+coho
+rock beauty
+anemone fish
+sturgeon
+gar
+lionfish
+puffer
+abacus
+abaya
+academic gown
+accordion
+acoustic guitar
+aircraft carrier
+airliner
+airship
+altar
+ambulance
+amphibian
+analog clock
+apiary
+apron
+ashcan
+assault rifle
+backpack
+bakery
+balance beam
+balloon
+ballpoint
+Band Aid
+banjo
+bannister
+barbell
+barber chair
+barbershop
+barn
+barometer
+barrel
+barrow
+baseball
+basketball
+bassinet
+bassoon
+bathing cap
+bath towel
+bathtub
+beach wagon
+beacon
+beaker
+bearskin
+beer bottle
+beer glass
+bell cote
+bib
+bicycle-built-for-two
+bikini
+binder
+binoculars
+birdhouse
+boathouse
+bobsled
+bolo tie
+bonnet
+bookcase
+bookshop
+bottlecap
+bow
+bow tie
+brass
+brassiere
+breakwater
+breastplate
+broom
+bucket
+buckle
+bulletproof vest
+bullet train
+butcher shop
+cab
+caldron
+candle
+cannon
+canoe
+can opener
+cardigan
+car mirror
+carousel
+carpenter's kit
+carton
+car wheel
+cash machine
+cassette
+cassette player
+castle
+catamaran
+CD player
+cello
+cellular telephone
+chain
+chainlink fence
+chain mail
+chain saw
+chest
+chiffonier
+chime
+china cabinet
+Christmas stocking
+church
+cinema
+cleaver
+cliff dwelling
+cloak
+clog
+cocktail shaker
+coffee mug
+coffeepot
+coil
+combination lock
+computer keyboard
+confectionery
+container ship
+convertible
+corkscrew
+cornet
+cowboy boot
+cowboy hat
+cradle
+crane2
+crash helmet
+crate
+crib
+Crock Pot
+croquet ball
+crutch
+cuirass
+dam
+desk
+desktop computer
+dial telephone
+diaper
+digital clock
+digital watch
+dining table
+dishrag
+dishwasher
+disk brake
+dock
+dogsled
+dome
+doormat
+drilling platform
+drum
+drumstick
+dumbbell
+Dutch oven
+electric fan
+electric guitar
+electric locomotive
+entertainment center
+envelope
+espresso maker
+face powder
+feather boa
+file
+fireboat
+fire engine
+fire screen
+flagpole
+flute
+folding chair
+football helmet
+forklift
+fountain
+fountain pen
+four-poster
+freight car
+French horn
+frying pan
+fur coat
+garbage truck
+gasmask
+gas pump
+goblet
+go-kart
+golf ball
+golfcart
+gondola
+gong
+gown
+grand piano
+greenhouse
+grille
+grocery store
+guillotine
+hair slide
+hair spray
+half track
+hammer
+hamper
+hand blower
+hand-held computer
+handkerchief
+hard disc
+harmonica
+harp
+harvester
+hatchet
+holster
+home theater
+honeycomb
+hook
+hoopskirt
+horizontal bar
+horse cart
+hourglass
+iPod
+iron
+jack-o'-lantern
+jean
+jeep
+jersey
+jigsaw puzzle
+jinrikisha
+joystick
+kimono
+knee pad
+knot
+lab coat
+ladle
+lampshade
+laptop
+lawn mower
+lens cap
+letter opener
+library
+lifeboat
+lighter
+limousine
+liner
+lipstick
+Loafer
+lotion
+loudspeaker
+loupe
+lumbermill
+magnetic compass
+mailbag
+mailbox
+maillot
+maillot2
+manhole cover
+maraca
+marimba
+mask
+matchstick
+maypole
+maze
+measuring cup
+medicine chest
+megalith
+microphone
+microwave
+military uniform
+milk can
+minibus
+miniskirt
+minivan
+missile
+mitten
+mixing bowl
+mobile home
+Model T
+modem
+monastery
+monitor
+moped
+mortar
+mortarboard
+mosque
+mosquito net
+motor scooter
+mountain bike
+mountain tent
+mouse
+mousetrap
+moving van
+muzzle
+nail
+neck brace
+necklace
+nipple
+notebook
+obelisk
+oboe
+ocarina
+odometer
+oil filter
+organ
+oscilloscope
+overskirt
+oxcart
+oxygen mask
+packet
+paddle
+paddlewheel
+padlock
+paintbrush
+pajama
+palace
+panpipe
+paper towel
+parachute
+parallel bars
+park bench
+parking meter
+passenger car
+patio
+pay-phone
+pedestal
+pencil box
+pencil sharpener
+perfume
+Petri dish
+photocopier
+pick
+pickelhaube
+picket fence
+pickup
+pier
+piggy bank
+pill bottle
+pillow
+ping-pong ball
+pinwheel
+pirate
+pitcher
+plane
+planetarium
+plastic bag
+plate rack
+plow
+plunger
+Polaroid camera
+pole
+police van
+poncho
+pool table
+pop bottle
+pot
+potter's wheel
+power drill
+prayer rug
+printer
+prison
+projectile
+projector
+puck
+punching bag
+purse
+quill
+quilt
+racer
+racket
+radiator
+radio
+radio telescope
+rain barrel
+recreational vehicle
+reel
+reflex camera
+refrigerator
+remote control
+restaurant
+revolver
+rifle
+rocking chair
+rotisserie
+rubber eraser
+rugby ball
+rule
+running shoe
+safe
+safety pin
+saltshaker
+sandal
+sarong
+sax
+scabbard
+scale
+school bus
+schooner
+scoreboard
+screen
+screw
+screwdriver
+seat belt
+sewing machine
+shield
+shoe shop
+shoji
+shopping basket
+shopping cart
+shovel
+shower cap
+shower curtain
+ski
+ski mask
+sleeping bag
+slide rule
+sliding door
+slot
+snorkel
+snowmobile
+snowplow
+soap dispenser
+soccer ball
+sock
+solar dish
+sombrero
+soup bowl
+space bar
+space heater
+space shuttle
+spatula
+speedboat
+spider web
+spindle
+sports car
+spotlight
+stage
+steam locomotive
+steel arch bridge
+steel drum
+stethoscope
+stole
+stone wall
+stopwatch
+stove
+strainer
+streetcar
+stretcher
+studio couch
+stupa
+submarine
+suit
+sundial
+sunglass
+sunglasses
+sunscreen
+suspension bridge
+swab
+sweatshirt
+swimming trunks
+swing
+switch
+syringe
+table lamp
+tank
+tape player
+teapot
+teddy
+television
+tennis ball
+thatch
+theater curtain
+thimble
+thresher
+throne
+tile roof
+toaster
+tobacco shop
+toilet seat
+torch
+totem pole
+tow truck
+toyshop
+tractor
+trailer truck
+tray
+trench coat
+tricycle
+trimaran
+tripod
+triumphal arch
+trolleybus
+trombone
+tub
+turnstile
+typewriter keyboard
+umbrella
+unicycle
+upright
+vacuum
+vase
+vault
+velvet
+vending machine
+vestment
+viaduct
+violin
+volleyball
+waffle iron
+wall clock
+wallet
+wardrobe
+warplane
+washbasin
+washer
+water bottle
+water jug
+water tower
+whiskey jug
+whistle
+wig
+window screen
+window shade
+Windsor tie
+wine bottle
+wing
+wok
+wooden spoon
+wool
+worm fence
+wreck
+yawl
+yurt
+web site
+comic book
+crossword puzzle
+street sign
+traffic light
+book jacket
+menu
+plate
+guacamole
+consomme
+hot pot
+trifle
+ice cream
+ice lolly
+French loaf
+bagel
+pretzel
+cheeseburger
+hotdog
+mashed potato
+head cabbage
+broccoli
+cauliflower
+zucchini
+spaghetti squash
+acorn squash
+butternut squash
+cucumber
+artichoke
+bell pepper
+cardoon
+mushroom
+Granny Smith
+strawberry
+orange
+lemon
+fig
+pineapple
+banana
+jackfruit
+custard apple
+pomegranate
+hay
+carbonara
+chocolate sauce
+dough
+meat loaf
+pizza
+potpie
+burrito
+red wine
+espresso
+cup
+eggnog
+alp
+bubble
+cliff
+coral reef
+geyser
+lakeside
+promontory
+sandbar
+seashore
+valley
+volcano
+ballplayer
+groom
+scuba diver
+rapeseed
+daisy
+yellow lady's slipper
+corn
+acorn
+hip
+buckeye
+coral fungus
+agaric
+gyromitra
+stinkhorn
+earthstar
+hen-of-the-woods
+bolete
+ear
+toilet tissue
\ No newline at end of file
diff --git a/datumaro/plugins/openvino/samples/mobilenet_v2_pytorch_interp.py b/datumaro/plugins/openvino/samples/mobilenet_v2_pytorch_interp.py
new file mode 100644
index 0000000000..43ce43fd21
--- /dev/null
+++ b/datumaro/plugins/openvino/samples/mobilenet_v2_pytorch_interp.py
@@ -0,0 +1,38 @@
+# Copyright (C) 2021 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+
+from datumaro.components.extractor import Label, LabelCategories, AnnotationType
+from datumaro.util.annotation_util import softmax
+
+
+def process_outputs(inputs, outputs):
+    # inputs = model input; array or images; shape = (B, H, W, C)
+    # outputs = model output; shape = (1, 1, N, 7); N is the number of detected bounding boxes.
+    # det = [image_id, label(class id), conf, x_min, y_min, x_max, y_max]
+    # results = conversion result; [[ Annotation, ... ], ... ]
+
+    results = []
+    for input, output in zip(inputs, outputs):
+        image_results = []
+        output = softmax(output).tolist()
+        label = output.index(max(output))
+        image_results.append(Label(label=label, attributes={"scores": output}))
+
+        results.append(image_results)
+
+    return results
+
+
+def get_categories():
+    # output categories - label map etc.
+
+    label_categories = LabelCategories()
+
+    with open("samples/imagenet.class", "r") as file:
+        for line in file.readlines():
+            label = line.strip()
+            label_categories.add(label)
+
+    return {AnnotationType.label: label_categories}
diff --git a/datumaro/plugins/openvino/samples/ssd_face_detection_interp.py b/datumaro/plugins/openvino/samples/ssd_face_detection_interp.py
new file mode 100644
index 0000000000..abb4604f8d
--- /dev/null
+++ b/datumaro/plugins/openvino/samples/ssd_face_detection_interp.py
@@ -0,0 +1,83 @@
+# Copyright (C) 2021 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+
+from datumaro.components.extractor import *
+
+conf_thresh = 0.02
+
+
+def _match_confs(confs, detections):
+    matches = [-1] * len(detections)
+
+    queries = {}
+    for i, det in enumerate(detections):
+        queries.setdefault(int(det[1]), []).append((det[2], i))
+
+    found_count = 0
+    for i, v in enumerate(confs):
+        if found_count == len(detections):
+            break
+
+        for cls_id, query in queries.items():
+            if found_count == len(detections):
+                break
+
+            for q_id, (conf, det_idx) in enumerate(query):
+                if v[cls_id] == conf:
+                    matches[det_idx] = i
+                    query.pop(q_id)
+                    found_count += 1
+                    break
+
+    return matches
+
+
+def process_outputs(inputs, outputs):
+    # inputs = model input; array or images; shape = (B, H, W, C)
+    # outputs = model output; shape = (1, 1, N, 7); N is the number of detected bounding boxes.
+    # det = [image_id, label(class id), conf, x_min, y_min, x_max, y_max]
+    # results = conversion result; [[ Annotation, ... ], ... ]
+
+    results = []
+    for input, detections in zip(inputs, outputs["detection_out"]):
+
+        input_height, input_width = input.shape[:2]
+
+        confs = outputs["Softmax_189/Softmax_"]
+        detections = detections[0]
+
+        conf_ids = _match_confs(confs, detections)
+
+        image_results = []
+        for i, det in enumerate(detections):
+            image_id = int(det[0])
+            label = int(det[1])
+            conf = float(det[2])
+            det_confs = confs[conf_ids[i]]
+
+            if conf <= conf_thresh:
+                continue
+
+            x = max(int(det[3] * input_width), 0)
+            y = max(int(det[4] * input_height), 0)
+            w = min(int(det[5] * input_width - x), input_width)
+            h = min(int(det[6] * input_height - y), input_height)
+
+            image_results.append(Bbox(x, y, w, h, label=label, 
+                attributes={ 'score': conf, 'scores': list(map(float, det_confs)) }
+            ))
+            
+            results.append(image_results)
+
+    return results
+
+
+def get_categories():
+    # output categories - label map etc.
+
+    label_categories = LabelCategories()
+    label_categories.add("face")
+
+    return {AnnotationType.label: label_categories}
diff --git a/datumaro/plugins/openvino/samples/ssd_mobilenet_coco_detection_interp.py b/datumaro/plugins/openvino/samples/ssd_mobilenet_coco_detection_interp.py
new file mode 100644
index 0000000000..3b3e5de252
--- /dev/null
+++ b/datumaro/plugins/openvino/samples/ssd_mobilenet_coco_detection_interp.py
@@ -0,0 +1,90 @@
+# Copyright (C) 2021 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+
+from datumaro.components.extractor import *
+
+conf_thresh = 0.3
+model_class_num = 91
+
+
+def _match_confs(confs, detections):
+    matches = [-1] * len(detections)
+
+    queries = {}
+    for i, det in enumerate(detections):
+        queries.setdefault(int(det[1]), []).append((det[2], i))
+
+    found_count = 0
+    for i, v in enumerate(confs):
+        if found_count == len(detections):
+            break
+
+        for cls_id, query in queries.items():
+            if found_count == len(detections):
+                break
+
+            for q_id, (conf, det_idx) in enumerate(query):
+                if v[cls_id] == conf:
+                    matches[det_idx] = i
+                    query.pop(q_id)
+                    found_count += 1
+                    break
+
+    return matches
+
+
+def process_outputs(inputs, outputs):
+    # inputs = model input; array or images; shape = (B, H, W, C)
+    # outputs = model output; shape = (1, 1, N, 7); N is the number of detected bounding boxes.
+    # det = [image_id, label(class id), conf, x_min, y_min, x_max, y_max]
+    # results = conversion result; [[ Annotation, ... ], ... ]
+
+    results = []
+    for input, confs, detections in zip(
+        inputs, outputs["do_ExpandDims_conf/sigmoid"], outputs["DetectionOutput"]
+    ):
+
+        input_height, input_width = input.shape[:2]
+
+        confs = confs[0].reshape(-1, model_class_num)
+        detections = detections[0]
+
+        conf_ids = _match_confs(confs, detections)
+
+        image_results = []
+        for i, det in enumerate(detections):
+            image_id = int(det[0])
+            label = int(det[1])
+            conf = float(det[2])
+            det_confs = confs[conf_ids[i]]
+
+            if conf <= conf_thresh:
+                continue
+
+            x = max(int(det[3] * input_width), 0)
+            y = max(int(det[4] * input_height), 0)
+            w = min(int(det[5] * input_width - x), input_width)
+            h = min(int(det[6] * input_height - y), input_height)
+
+            image_results.append(Bbox(x, y, w, h, label=label, 
+                attributes={ 'score': conf, 'scores': list(map(float, det_confs)) }
+            ))
+            
+            results.append(image_results)
+
+    return results
+
+
+def get_categories():
+    # output categories - label map etc.
+
+    label_categories = LabelCategories()
+
+    with open("samples/coco.class", "r") as file:
+        for line in file.readlines():
+            label = line.strip()
+            label_categories.add(label)
+
+    return {AnnotationType.label: label_categories}
diff --git a/datumaro/plugins/openvino/samples/ssd_person_detection_interp.py b/datumaro/plugins/openvino/samples/ssd_person_detection_interp.py
new file mode 100644
index 0000000000..3888944df4
--- /dev/null
+++ b/datumaro/plugins/openvino/samples/ssd_person_detection_interp.py
@@ -0,0 +1,83 @@
+# Copyright (C) 2021 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+
+from datumaro.components.extractor import *
+
+conf_thresh = 0.02
+
+
+def _match_confs(confs, detections):
+    matches = [-1] * len(detections)
+
+    queries = {}
+    for i, det in enumerate(detections):
+        queries.setdefault(int(det[1]), []).append((det[2], i))
+
+    found_count = 0
+    for i, v in enumerate(confs):
+        if found_count == len(detections):
+            break
+
+        for cls_id, query in queries.items():
+            if found_count == len(detections):
+                break
+
+            for q_id, (conf, det_idx) in enumerate(query):
+                if v[cls_id] == conf:
+                    matches[det_idx] = i
+                    query.pop(q_id)
+                    found_count += 1
+                    break
+
+    return matches
+
+
+def process_outputs(inputs, outputs):
+    # inputs = model input; array or images; shape = (B, H, W, C)
+    # outputs = model output; shape = (1, 1, N, 7); N is the number of detected bounding boxes.
+    # det = [image_id, label(class id), conf, x_min, y_min, x_max, y_max]
+    # results = conversion result; [[ Annotation, ... ], ... ]
+
+    results = []
+    for input, detections in zip(inputs, outputs["detection_out"]):
+
+        input_height, input_width = input.shape[:2]
+
+        confs = outputs["Softmax_189/Softmax_"]
+        detections = detections[0]
+
+        conf_ids = _match_confs(confs, detections)
+
+        image_results = []
+        for i, det in enumerate(detections):
+            image_id = int(det[0])
+            label = int(det[1])
+            conf = float(det[2])
+            det_confs = confs[conf_ids[i]]
+
+            if conf <= conf_thresh:
+                continue
+
+            x = max(int(det[3] * input_width), 0)
+            y = max(int(det[4] * input_height), 0)
+            w = min(int(det[5] * input_width - x), input_width)
+            h = min(int(det[6] * input_height - y), input_height)
+
+            image_results.append(Bbox(x, y, w, h, label=label, 
+                attributes={ 'score': conf, 'scores': list(map(float, det_confs)) }
+            ))
+            
+            results.append(image_results)
+
+    return results
+
+
+def get_categories():
+    # output categories - label map etc.
+
+    label_categories = LabelCategories()
+    label_categories.add("person")
+
+    return {AnnotationType.label: label_categories}
diff --git a/datumaro/plugins/openvino/samples/ssd_person_vehicle_bike_detection_interp.py b/datumaro/plugins/openvino/samples/ssd_person_vehicle_bike_detection_interp.py
new file mode 100644
index 0000000000..a2de43dd6d
--- /dev/null
+++ b/datumaro/plugins/openvino/samples/ssd_person_vehicle_bike_detection_interp.py
@@ -0,0 +1,85 @@
+# Copyright (C) 2021 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+
+from datumaro.components.extractor import *
+
+conf_thresh = 0.02
+
+
+def _match_confs(confs, detections):
+    matches = [-1] * len(detections)
+
+    queries = {}
+    for i, det in enumerate(detections):
+        queries.setdefault(int(det[1]), []).append((det[2], i))
+
+    found_count = 0
+    for i, v in enumerate(confs):
+        if found_count == len(detections):
+            break
+
+        for cls_id, query in queries.items():
+            if found_count == len(detections):
+                break
+
+            for q_id, (conf, det_idx) in enumerate(query):
+                if v[cls_id] == conf:
+                    matches[det_idx] = i
+                    query.pop(q_id)
+                    found_count += 1
+                    break
+
+    return matches
+
+
+def process_outputs(inputs, outputs):
+    # inputs = model input; array or images; shape = (B, H, W, C)
+    # outputs = model output; shape = (1, 1, N, 7); N is the number of detected bounding boxes.
+    # det = [image_id, label(class id), conf, x_min, y_min, x_max, y_max]
+    # results = conversion result; [[ Annotation, ... ], ... ]
+
+    results = []
+    for input, detections in zip(inputs, outputs["detection_out"]):
+
+        input_height, input_width = input.shape[:2]
+
+        confs = outputs["Softmax_189/Softmax_"]
+        detections = detections[0]
+
+        conf_ids = _match_confs(confs, detections)
+
+        image_results = []
+        for i, det in enumerate(detections):
+            image_id = int(det[0])
+            label = int(det[1])
+            conf = float(det[2])
+            det_confs = confs[conf_ids[i]]
+
+            if conf <= conf_thresh:
+                continue
+
+            x = max(int(det[3] * input_width), 0)
+            y = max(int(det[4] * input_height), 0)
+            w = min(int(det[5] * input_width - x), input_width)
+            h = min(int(det[6] * input_height - y), input_height)
+
+            image_results.append(Bbox(x, y, w, h, label=label, 
+                attributes={ 'score': conf, 'scores': list(map(float, det_confs)) }
+            ))
+            
+            results.append(image_results)
+
+    return results
+
+
+def get_categories():
+    # output categories - label map etc.
+
+    label_categories = LabelCategories()
+    label_categories.add("vehicle")
+    label_categories.add("person")
+    label_categories.add("bike")
+
+    return {AnnotationType.label: label_categories}
diff --git a/datumaro/plugins/openvino/samples/ssd_vehicle_detection_interp.py b/datumaro/plugins/openvino/samples/ssd_vehicle_detection_interp.py
new file mode 100644
index 0000000000..2866133542
--- /dev/null
+++ b/datumaro/plugins/openvino/samples/ssd_vehicle_detection_interp.py
@@ -0,0 +1,83 @@
+# Copyright (C) 2021 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+
+from datumaro.components.extractor import *
+
+conf_thresh = 0.02
+
+
+def _match_confs(confs, detections):
+    matches = [-1] * len(detections)
+
+    queries = {}
+    for i, det in enumerate(detections):
+        queries.setdefault(int(det[1]), []).append((det[2], i))
+
+    found_count = 0
+    for i, v in enumerate(confs):
+        if found_count == len(detections):
+            break
+
+        for cls_id, query in queries.items():
+            if found_count == len(detections):
+                break
+
+            for q_id, (conf, det_idx) in enumerate(query):
+                if v[cls_id] == conf:
+                    matches[det_idx] = i
+                    query.pop(q_id)
+                    found_count += 1
+                    break
+
+    return matches
+
+
+def process_outputs(inputs, outputs):
+    # inputs = model input; array or images; shape = (B, H, W, C)
+    # outputs = model output; shape = (1, 1, N, 7); N is the number of detected bounding boxes.
+    # det = [image_id, label(class id), conf, x_min, y_min, x_max, y_max]
+    # results = conversion result; [[ Annotation, ... ], ... ]
+
+    results = []
+    for input, detections in zip(inputs, outputs["detection_out"]):
+
+        input_height, input_width = input.shape[:2]
+
+        confs = outputs["Softmax_189/Softmax_"]
+        detections = detections[0]
+
+        conf_ids = _match_confs(confs, detections)
+
+        image_results = []
+        for i, det in enumerate(detections):
+            image_id = int(det[0])
+            label = int(det[1])
+            conf = float(det[2])
+            det_confs = confs[conf_ids[i]]
+
+            if conf <= conf_thresh:
+                continue
+
+            x = max(int(det[3] * input_width), 0)
+            y = max(int(det[4] * input_height), 0)
+            w = min(int(det[5] * input_width - x), input_width)
+            h = min(int(det[6] * input_height - y), input_height)
+
+            image_results.append(Bbox(x, y, w, h, label=label, 
+                attributes={ 'score': conf, 'scores': list(map(float, det_confs)) }
+            ))
+            
+            results.append(image_results)
+
+    return results
+
+
+def get_categories():
+    # output categories - label map etc.
+
+    label_categories = LabelCategories()
+    label_categories.add("vehicle")
+
+    return {AnnotationType.label: label_categories}
diff --git a/datumaro/plugins/sampler/sampler.py b/datumaro/plugins/sampler/sampler.py
index 99791a8b0f..2fefa0e1dc 100644
--- a/datumaro/plugins/sampler/sampler.py
+++ b/datumaro/plugins/sampler/sampler.py
@@ -123,10 +123,10 @@ def _load_inference_from_subset(extractor, subset_name):
                 raise Exception(f"Item {item.id} does not have annotations")
 
             for annotation in item.annotations:
-                if 'score' not in annotation.attributes:
+                if 'scores' not in annotation.attributes:
                     raise Exception(f"Item {item.id} - an annotation "
-                        "does not have 'score' attribute")
-                probs = annotation.attributes['score']
+                        "does not have 'scores' attribute")
+                probs = annotation.attributes['scores']
 
                 infer_df['ImageID'].append(item.id)
 
diff --git a/docs/user_manual.md b/docs/user_manual.md
index 99eb220523..29af84fa99 100644
--- a/docs/user_manual.md
+++ b/docs/user_manual.md
@@ -850,6 +850,7 @@ datum model add \
 ```
 
 Interpretation script for an OpenVINO detection model (`convert.py`):
+You can find OpenVINO™ model interpreter samples in datumaro/plugins/openvino/samples. [Instruction](datumaro/plugins/openvino/README.md) 
 
 ``` python
 from datumaro.components.extractor import *
diff --git a/tests/test_sampler.py b/tests/test_sampler.py
index 31672e1b6d..3f3d316d28 100644
--- a/tests/test_sampler.py
+++ b/tests/test_sampler.py
@@ -42,7 +42,7 @@ def _get_probs(out_range=False):
         return probs
 
     def _generate_classification_dataset(self, config, subset=None,
-            empty_score=False, out_range=False, no_attr=False, no_img=False):
+            empty_scores=False, out_range=False, no_attr=False, no_img=False):
         probs = self._get_probs(out_range)
         if subset is None:
             self.subset = ["train", "val", "test"]
@@ -56,11 +56,11 @@ def _generate_classification_dataset(self, config, subset=None,
             num_item = config[label]
             label_cat.add(label, attributes=None)
             for _ in range(num_item):
-                score = probs[idx]
+                scores = probs[idx]
                 idx += 1
-                if empty_score:
-                    score = []
-                attr = {"score": score}
+                if empty_scores:
+                    scores = []
+                attr = {"scores": scores}
                 if no_attr:
                     attr = {}
                 img = Image(path=f"test/dataset/{idx}.jpg", size=(90, 90))
@@ -341,7 +341,7 @@ def test_sampler_gives_error(self):
                     data_df["ImagePath"].append(data.image.path)
 
                     for annotation in data.annotations:
-                        probs = annotation.attributes["score"]
+                        probs = annotation.attributes["scores"]
                         infer_df["ImageID"].append(data.id)
 
                         for prob_idx, prob in enumerate(probs):
@@ -368,7 +368,7 @@ def test_sampler_gives_error(self):
                     data_df["ImagePath"].append(data.image.path)
 
                     for annotation in data.annotations:
-                        probs = annotation.attributes["score"]
+                        probs = annotation.attributes["scores"]
 
                         for prob_idx, prob in enumerate(probs):
                             infer_df[f"ClassProbability{prob_idx+1}"].append(prob)
@@ -401,14 +401,14 @@ def test_sampler_get_invalid_data(self):
                 result = iter(result)
                 next(result)
 
-        with self.subTest("Dataset without Score (Probability)"):
+        with self.subTest("Dataset without Scores (Probability)"):
             config = {
                 "label1": 10,
                 "label2": 10,
                 "label3": 10,
             }
 
-            source = self._generate_classification_dataset(config, empty_score=True)
+            source = self._generate_classification_dataset(config, empty_scores=True)
             with self.assertRaisesRegex(Exception, "ClassProbability"):
                 result = Sampler(
                     source,
@@ -431,7 +431,7 @@ def test_sampler_get_invalid_data(self):
             }
 
             source = self._generate_classification_dataset(
-                config, empty_score=False, out_range=True
+                config, empty_scores=False, out_range=True
             )
             with self.assertRaisesRegex(Exception, "Invalid data"):
                 result = Sampler(
@@ -447,7 +447,7 @@ def test_sampler_get_invalid_data(self):
                 result = iter(result)
                 next(result)
 
-        with self.subTest("No Score Attribute Data"):
+        with self.subTest("No Scores Attribute Data"):
             config = {
                 "label1": 10,
                 "label2": 10,
@@ -455,7 +455,7 @@ def test_sampler_get_invalid_data(self):
             }
 
             source = self._generate_classification_dataset(config, no_attr=True)
-            with self.assertRaisesRegex(Exception, "does not have 'score'"):
+            with self.assertRaisesRegex(Exception, "does not have 'scores'"):
                 result = Sampler(
                     source,
                     algorithm="entropy",