Segmentation 101 a.k.a. SEG 101 is an exhaustive list we have created to make it easier for you to search publicly available Image Segmentation datasets.
Segmentation is one of the most time-consuming annotation tasks. Sometimes, before collecting your own dataset, you do want to experiment on a publicly available dataset.
So, we created this list which is searchable by class name, so you can quickly find a class that you need. It contains Instance Segmentation, Semantic Part Segmentation, Motion Segmentation, Vessel Segmentation, and many such variants.
So, if you want a head start for your AI app or for a hobby project which requires pixel-wise annotated data then, do make a quick stop at the below-mentioned datasets, and use them to build something great !!.
1. The COCO-Stuff Dataset
Dataset Characteristics:
Task Type: Instance Segmentation, Scene Understanding, Object Localization, Semantic Segmentation.
Image Count: 164K complex images from COCO 2017
Labeled Instances Count: 2.5 million
Categories: 172 classes: 80 things, 91 stuff, and 1 class unlabeled
*91 thing classes (1-91) Descriptions for stuff labels (92-182) are provided. Furthermore, 11 classes have been removed from COCO and therefore lack a preview image.
Id | Label name | Preview | Description |
---|---|---|---|
0 | unlabeled | Pixels that do not belong to any of the other classes | |
1 | person | (view) | |
2 | bicycle | (view) | |
3 | car | (view) | |
4 | motorcycle | (view) | |
5 | airplane | (view) | |
6 | bus | (view) | |
7 | train | (view) | |
8 | truck | (view) | |
9 | boat | (view) | |
10 | traffic light | (view) | |
11 | fire hydrant | (view) | |
12 | street sign | Removed from COCO. | |
13 | stop sign | (view) | |
14 | parking meter | (view) | |
15 | bench | (view) | |
16 | bird | (view) | |
17 | cat | (view) | |
18 | dog | (view) | |
19 | horse | (view) | |
20 | sheep | (view) | |
21 | cow | (view) | |
22 | elephant | (view) | |
23 | bear | (view) | |
24 | zebra | (view) | |
25 | giraffe | (view) | |
26 | hat | Removed from COCO. | |
27 | backpack | (view) | |
28 | umbrella | (view) | |
29 | shoe | Removed from COCO. | |
30 | eye glasses | Removed from COCO. | |
31 | handbag | (view) | |
32 | tie | (view) | |
33 | suitcase | (view) | |
34 | frisbee | (view) | |
35 | skis | (view) | |
36 | snowboard | (view) | |
37 | sports ball | (view) | |
38 | kite | (view) | |
39 | baseball bat | (view) | |
40 | baseball glove | (view) | |
41 | skateboard | (view) | |
42 | surfboard | (view) | |
43 | tennis racket | (view) | |
44 | bottle | (view) | |
45 | plate | Removed from COCO. | |
46 | wine glass | (view) | |
47 | cup | (view) | |
48 | fork | (view) | |
49 | knife | (view) | |
50 | spoon | (view) | |
51 | bowl | (view) | |
52 | banana | (view) | |
53 | apple | (view) | |
54 | sandwich | (view) | |
55 | orange | (view) | |
56 | broccoli | (view) | |
57 | carrot | (view) | |
58 | hot dog | (view) | |
59 | pizza | (view) | |
60 | donut | (view) | |
61 | cake | (view) | |
62 | chair | (view) | |
63 | couch | (view) | |
64 | potted plant | (view) | |
65 | bed | (view) | |
66 | mirror | Removed from COCO. | |
67 | dining table | (view) | |
68 | window | Removed from COCO. | |
69 | desk | Removed from COCO. | |
70 | toilet | (view) | |
71 | door | Removed from COCO. | |
72 | tv | (view) | |
73 | laptop | (view) | |
74 | mouse | (view) | |
75 | remote | (view) | |
76 | keyboard | (view) | |
77 | cell phone | (view) | |
78 | microwave | (view) | |
79 | oven | (view) | |
80 | toaster | (view) | |
81 | sink | (view) | |
82 | refrigerator | (view) | |
83 | blender | Removed from COCO. | |
84 | book | (view) | |
85 | clock | (view) | |
86 | vase | (view) | |
87 | scissors | (view) | |
88 | teddy bear | (view) | |
89 | hair drier | (view) | |
90 | toothbrush | (view) | |
91 | hair brush | Removed from COCO. | |
92 | banner | (view) | Any large sign, especially if constructed of soft material or fabric, often seen in stadiums and advertising. |
93 | blanket | (view) | A loosely woven fabric, used for warmth while sleeping. |
94 | branch | (view) | The woody part of a tree or bush, arising from the trunk and usually dividing. |
95 | bridge | (view) | A manmade construction that spans a divide (incl. train bridge, river bridge). |
96 | building-other | (view) | Any other type of building or structures. |
97 | bush | (view) | A woody plant distinguished from a tree by its multiple stems and lower height (incl. hedge, scrub). |
98 | cabinet | (view) | A storage closet, often hanging on the wall. |
99 | cage | (view) | An enclosure made of bars, often seen in zoos. |
100 | cardboard | (view) | A wood-based material resembling heavy paper, used in the manufacture of boxes, cartons and signs. |
101 | carpet | (view) | A fabric used as a floor covering. |
102 | ceiling-other | (view) | Other types of ceilings (incl. industrial ceilings, painted ceilings). |
103 | ceiling-tile | (view) | A ceiling made of regularly-shaped slabs. |
104 | cloth | (view) | A piece of cloth used for a particular purpose. (incl. cleaning cloth). |
105 | clothes | (view) | Items of clothing or apparel, not currently worn by a person. |
106 | clouds | (view) | A visible mass of water droplets suspended in the air. |
107 | counter | (view) | A surface in the kitchen or bathroom, often built into a wall or above a cabinet, which holds the washbasin or surface to prepare food. |
108 | cupboard | (view) | A piece of furniture used for storing dishware or a wardrobe for clothes, sometimes hanging on the wall. |
109 | curtain | (view) | A piece of cloth covering a window, bed or shower to offer privacy and keep out light. |
110 | desk-stuff | (view) | A piece of furniture with a flat surface and typically with drawers, at which one can read, write, or do other work. |
111 | dirt | (view) | Soil or earth (incl. dirt path). |
112 | door-stuff | (view) | A portal of entry into a building, room or vehicle, consisting of a rigid plane movable on a hinge (incl. the frame, replaces door). |
113 | fence | (view) | A thin, human-constructed barrier which separates two pieces of land. |
114 | floor-marble | (view) | The supporting surface of a room or outside, made of marble. |
115 | floor-other | (view) | Any other type of floor (incl. rubber-based floor). |
116 | floor-stone | (view) | The supporting surface of a room or outside, made of stone (incl. brick floor). |
117 | floor-tile | (view) | The supporting surface of a room or outside, made of regularly-shaped slabs (incl. tiled stone floor, tiled marble floor). |
118 | floor-wood | (view) | The supporting surface of a room or outside, made of wood (incl. wooden tiles, parquet, laminate, wooden boards). |
119 | flower | (view) | The seed-bearing part of a plant (incl. the entire flower). |
120 | fog | (view) | A thick cloud of tiny water droplets suspended in the atmosphere near the earth’s surface. |
121 | food-other | (view) | Any other type of food. |
122 | fruit | (view) | The sweet and fleshy product of a tree or other plant. |
123 | furniture-other | (view) | Any other type of furniture (incl. oven). |
124 | grass | (view) | Vegetation consisting of typically short plants with long, narrow leaves (incl. lawn, pasture). |
125 | gravel | (view) | A loose aggregation of small water-worn or pounded stones. |
126 | ground-other | (view) | Any other type of ground found outside a building. |
127 | hill | (view) | A naturally raised area of land, not as high as a mountain, viewed at a distance and may be covered in trees, snow or grass. |
128 | house | (view) | A smaller size building for human habitation. |
129 | leaves | (view) | A structure of a higher plant, typically green and blade-like, that is attached to a stem or stalk. |
130 | light | (view) | A source of illumination, especially a lamp (incl. ceiling lights). |
131 | mat | (view) | A piece of coarse material placed on a floor for people to wipe their feet on. |
132 | metal | (view) | A raw metal material (incl. a pile of metal). |
133 | mirror-stuff | (view) | A glass coated surface which reflects a clear image (incl. the frame, replaces mirror). |
134 | moss | (view) | A small flowerless green plant which lacks true roots, growing in in damp habitats. |
135 | mountain | (view) | A large natural elevation rising abruptly from the surrounding level, viewed at a distance and may be covered in trees, snow or grass. |
136 | mud | (view) | A soft, sticky matter resulting from the mixing of earth and water. |
137 | napkin | (view) | A piece of cloth or paper used at a meal to wipe the fingers or lips. |
138 | net | (view) | An open-meshed fabric twisted, knotted, or woven together at regular intervals. |
139 | paper | (view) | A material manufactured in thin sheets from the pulp of wood. |
140 | pavement | (view) | A typically raised paved path for pedestrians at the side of a road. |
141 | pillow | (view) | A rectangular cloth bag stuffed with soft materials to support the head. |
142 | plant-other | (view) | Any other type of plant. |
143 | plastic | (view) | Raw plastic material. |
144 | platform | (view) | A raised level surface on which people or things can stand (incl. railroad platform). |
145 | playingfield | (view) | A ground marked off for various games (incl. indoor and outdoor). |
146 | railing | (view) | A fence or barrier made of typically metal rails. |
147 | railroad | (view) | A track made of steel rails along which trains run (incl. the wooden beams). |
148 | river | (view) | A stream of flowing water. |
149 | road | (view) | A paved way leading from one place to another. |
150 | rock | (view) | The solid mineral material forming part of the surface of the earth. |
151 | roof | (view) | The structure forming the upper covering of a building. |
152 | rug | (view) | A floor covering of thick woven material, typically not extending over the entire floor. |
153 | salad | (view) | A cold dish of various mixtures of raw or cooked vegetables. |
154 | sand | (view) | A loose granular substance, typically pale yellowish brown, resulting from erosion (incl. beach). |
155 | sea | (view) | Expanse of water that covers most of the earth’s surface. |
156 | shelf | (view) | An open piece of furniture that provides a surface for the storage or display of objects. |
157 | sky-other | (view) | Any other type of sky (incl. blue sky). |
158 | skyscraper | (view) | A very tall building of many storeys. |
159 | snow | (view) | Atmospheric water vapour frozen into ice crystals, falling or lying on the ground. |
160 | solid-other | (view) | Any other type of solid material. |
161 | stairs | (view) | A set of steps leading from one floor to another (incl. stairs inside or outside a building). |
162 | stone | (view) | A piece of stone shaped for a purpose. |
163 | straw | (view) | Dried stalks of grain. |
164 | structural-other | (view) | Any other type of structural connection (incl. arcs, pillars). |
165 | table | (view) | A piece of furniture with a flat top and one or more legs. |
166 | tent | (view) | A portable shelter made of cloth. |
167 | textile-other | (view) | Any other type of textile. |
168 | towel | (view) | A piece of thick absorbent cloth used for drying oneself. |
169 | tree | (view) | A woody plant, typically having a single trunk growing to a considerable height and bearing lateral branches at some distance from the ground. |
170 | vegetable | (view) | A part of a plant used as food. |
171 | wall-brick | (view) | A building wall made of bricks of clay. |
172 | wall-concrete | (view) | A building wall made of concrete. |
173 | wall-other | (view) | Any other type of wall. |
174 | wall-panel | (view) | A panel that is attached to a wall. |
175 | wall-stone | (view) | A building wall made of stone. |
176 | wall-tile | (view) | A building wall made of tiles, such as used in bathrooms and kitchens. |
177 | wall-wood | (view) | A building wall made of wooden material. |
178 | water-other | (view) | Any other type of water (incl. lake). |
179 | waterdrops | (view) | Sprinkles or drops of water not connected to a larger body of water. |
180 | window-blind | (view) | Blinds and shutters that cover a window. |
181 | window-other | (view) | Any type of window that must be visible in the image (replaces window). |
182 | wood | (view) | Raw wood materials (incl. logs). |
Code/Model/Publication URL : COCO-Stuff: Thing and Stuff Classes in Context
Source URL : The COCO-Stuff dataset
Dataset Examples :
2. The DUTS Image Dataset
Dataset Characteristics:
Task Type : Saliency detection
Image Attribute : 10,553 training images and 5,019 test images
Code/Model/Publication URL : Learning to Detect Salient Objects with Image-level Supervision
Source URL : The DUTS Image Dataset
Dataset Examples :
3. The CMU-Cornell iCoseg Dataset
Dataset Characteristics:
Task Type : cosegmentation
Image Attribute : 38 challenging groups with 643 total images (∼17 images per group), with associated pixel-level ground truth.
Code/Model/Publication URL : iCoseg: Interactive Co-segmentation with Intelligent Scribble Guidance
Source URL : The CMU-Cornell iCoseg Dataset
Dataset Examples :
4. The PASCAL-S Dataset
Dataset Characteristics:
Task Type : fixation prediction, salient object segmentation
Image Attribute : 850 images from PASCAL VOC 2010
Object Instance : 1296
No of subjects : 12
Code/Model/Publication URL : The Secrets of Salient Object Segmentation
Source URL : The PASCAL-S Dataset
Dataset Examples :
Credits : The PASCAL-S Dataset
5. DAVIS: Densely Annotated Video Segmentation
Dataset Characteristics:
Task Type: video object segmentation
Video Attribute: 400 objects on 150 videos with∼10k frames
Code/Model/Publication URL: A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation
Source URL: DAVIS
Dataset Examples :
6. NYU Depth Dataset V1
Dataset Characteristics:
Task Type : Indoor Scene segmentation, Multi-class segmentation
Image Attribute : 2347 unique frames cover over 13 object classes, spread over 64 different indoor environments.
Scene class |
Scenes |
Bathroom | 6 |
Bedroom | 17 |
Bookstore | 3 |
Cafe | 1 |
Kitchen | 10 |
Living Room | 13 |
Office | 14 |
Total | 64 |
Object Class |
Bed |
Blind |
Bookshelf |
Cabinet |
Ceiling |
Floor |
Picture |
Sofa |
Table |
Television |
Wall |
Window |
Background |
Code/Model/Publication URL : Indoor Scene Segmentation using a Structured Light Sensor
Source URL : NYU Depth V1
Dataset Examples :
Credits : Indoor Scene Segmentation using a Structured Light Sensor
7. PASCAL-Part Dataset
Dataset Characteristics:
Task Type: Body parts segmentation
Image Attribute: 10, 103
Label Attribute: 4,203
Categories: 20 with their individual body parts
Objects | Parts |
---|---|
aeroplane |
|
body |
|
engine* |
|
left wing |
|
right wing |
|
stern |
|
tail |
|
wheel* |
|
bicycle |
|
back wheel |
|
chain wheel |
|
front wheel |
|
handlebar |
|
headlight* |
|
saddle |
|
bird |
|
beak |
|
head |
|
left eye |
|
left foot |
|
left leg |
|
left wing |
|
neck |
|
right eye |
|
right foot |
|
right leg |
|
right wing |
|
tail |
|
torso |
|
boat |
|
bottle |
|
body |
|
cap |
|
bus |
|
back license plate |
|
back side |
|
door* |
|
front license plate |
|
front side |
|
headlight* |
|
left mirror |
|
left side |
|
right mirror |
|
right side |
|
roof side |
|
wheel* |
|
window* |
|
car |
|
back license plate |
|
back side |
|
door* |
|
front license plate |
|
front side |
|
headlight* |
|
left mirror |
|
left side |
|
right mirror |
|
right side |
|
roof side |
|
wheel* |
|
window* |
|
cat |
|
head |
|
left back leg |
|
left back paw |
|
left ear |
|
left eye |
|
left front leg |
|
left front paw |
|
neck |
|
nose |
|
right back leg |
|
right back paw |
|
right ear |
|
right eye |
|
right front leg |
|
right front paw |
|
tail |
|
torso |
|
chair |
|
cow |
|
head |
|
left back lower leg |
|
left back upper leg |
|
left ear |
|
left eye |
|
left front lower leg |
|
left front upper leg |
|
left horn |
|
muzzle |
|
neck |
|
right back lower leg |
|
right back upper leg |
|
right ear |
|
right eye |
|
right front lower leg |
|
right front upper leg |
|
right horn |
|
tail |
|
torso |
|
diningtable |
|
dog |
|
head |
|
left back leg |
|
left back paw |
|
left ear |
|
left eye |
|
left front leg |
|
left front paw |
|
muzzle |
|
neck |
|
nose |
|
right back leg |
|
right back paw |
|
right ear |
|
right eye |
|
right front leg |
|
right front paw |
|
tail |
|
torso |
|
horse |
|
head |
|
left back hoof |
|
left back lower leg |
|
left back upper leg |
|
left ear |
|
left eye |
|
left front hoof |
|
left front lower leg |
|
left front upper leg |
|
muzzle |
|
neck |
|
right back hoof |
|
right back lower leg |
|
right back upper leg |
|
right ear |
|
right eye |
|
right front hoof |
|
right front lower leg |
|
right front upper leg |
|
tail |
|
torso |
|
motorbike |
|
back wheel |
|
front wheel |
|
handlebar |
|
headlight* |
|
saddle |
|
person |
|
hair |
|
head |
|
left ear |
|
left eye |
|
left eyebrow |
|
left foot |
|
left hand |
|
left lower arm |
|
left lower leg |
|
left upper arm |
|
left upper leg |
|
mouth |
|
neck |
|
nose |
|
right ear |
|
right eye |
|
right eyebrow |
|
right foot |
|
right hand |
|
right lower arm |
|
right lower leg |
|
right upper arm |
|
right upper leg |
|
torso |
|
pottedplant |
|
plant |
|
pot |
|
sheep |
|
head |
|
left back lower leg |
|
left back upper leg |
|
left ear |
|
left eye |
|
left front lower leg |
|
left front upper leg |
|
left horn |
|
muzzle |
|
neck |
|
right back lower leg |
|
right back upper leg |
|
right ear |
|
right eye |
|
right front lower leg |
|
right front upper leg |
|
right horn |
|
tail |
|
torso |
|
sofa |
|
train |
|
coach back side* |
|
coach front side* |
|
coach left side* |
|
coach right side* |
|
coach roof side* |
|
coach* |
|
head |
|
head back side |
|
head front side |
|
head left tside |
|
head right side |
|
head roof side |
|
headlight* |
|
tvmonitor |
|
screen |
(* denotes a part can appear multiple times in one object instance)
Code/Model/Publication URL : Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts
Source URL : PASCAL-Part Dataset
Dataset Examples :
Credits: PASCAL-Part Dataset
8. Matting Human Datasets
Dataset Characteristics:
Task Type: Human segmentation
Image Attribute: 34,427 images and corresponding matting results
Code/Model/Publication URL: matting human dataset
Source URL: matting_human_half
Dataset Examples :
9. Automatic Portrait Segmentation Dataset
Dataset Characteristics:
Task Type: Automatic Portrait Segmentation for styling images
Image Attribute: 1800
Code/Model/Publication URL : Automatic Portrait Segmentation for Image Stylization
Source URL: Automatic Portrait Segmentation
Dataset Examples :
Credits : Automatic Portrait Segmentation
10. ADE20K Dataset
Dataset Characteristics:
Task Type: image segmentation
Image Attribute: 22,210 images, 900 scene categories defined in the SUN database, more than 200 object classes.
Label attribute: 22,210 Fully annotated with objects and parts
Code/Model/Publication URL: Semantic Understanding of Scenes through ADE20K Dataset
Source URL: ADE20K Dataset
Dataset Examples :
11. Pedestrian Parsing Dataset
Dataset Characteristics:
Task Type: pedestrian detection, pose estimation, and body segmentation.
Image Attribute: contains 3,673 images from 171 videos of different Surveillance Scenes (PPSS), where 2,064 images are occluded and 1,609 are not.
Code/Model/Publication URL: Pedestrian Parsing via Deep Decompositional Network
Source URL: Pedestrian Parsing Dataset
Dataset Examples :
12. iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
Dataset Characteristics:
Task Type: instance-level object detection and pixel-level segmentation task on aerial images
Image Attribute: 2806 high-resolution aerial images
Label attribute: 655,451 object instances
Categories :
iSAID Dataset | Large-vehicle, swimming pool, Helicopter, Bridge, Plane, Ship, Soccer-ball field, Storage tank |
Basketball court, Ground track field, Small-vehicle, Harbor, Baseball Diamond, Tennis court, Roundabout |
Code/Model/Publication URL : iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
Source URL: iSAID
Dataset Examples :
Credits: iSAID
13. Pascal VOC (2007-2012)
Dataset Characteristics:
Task Type: object segmentation, class segmentation
Image Count: 54k Images
Labeled Instances Count: 19,377
Categories :
-
-
- Person: person
- Animal: bird, cat, cow, dog, horse, sheep
- Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train
- Indoor: bottle, chair, dining table, potted plant, sofa, TV/monitor
- Background, void
-
Code/Model/Publication URL: The 2005 PASCAL Visual Object Classes Challenge
Source URL: Pascal VOC Dataset
Dataset Examples :
14. Open Images Dataset V6
Dataset Characteristics:
Task Type: object detection, segmentation, visual relationship, local narratives
Image Count: ~9M images
Labeled Instances Count: segmentation masks for 2.8M object instances in 350 classes, 16M bounding boxes in 600 object classes
Categories : 19,957 classes
Code/Model/Publication URL : Open Images
Source URL: Open Images Dataset
Dataset Examples :
15. OASIS: A Large-Scale Dataset for Single Image 3D in the Wild
Dataset Characteristics:
Task Type: Depth Estimation, Surface Normal Estimation, Fold & Occlusion detection, Planar Instance segmentation
Image Count : 140,000 images
Code/Model/Publication URL : OASIS: A Large-Scale Dataset for Single Image 3D in the Wild
Source URL : OASIS: A Large-Scale Dataset for Single Image 3D in the Wild
Dataset Examples :
Credits : OASIS: A Large-Scale Dataset for Single Image 3D in the Wild
16. KAIST Salient Pedestrian Dataset
Dataset Characteristics :
Task Type: pedestrian detector in thermal images
Image Attribute: 1702 images (913 day images and 789 night images)
Label instance: 4170 instances of pedestrians
Code/Model/Publication URL: Pedestrian Detection from Thermal Images using Saliency Maps
Source URL: Salient-Pedestrian-Detection
Dataset Examples :
Credits: Pedestrian Detection from Thermal Images using Saliency Maps
17. Clothing Attributes Dataset
Dataset Characteristics :
Task Type: Dressing Style Analysis using Semantic Attributes
Image Attribute : 1856 images
Label instance : 283,107 label
Categories : 26 attributes in total, including 23 binary-class attributes (6 for pattern, 11 for color and 6 miscellaneous attributes), 3 multi-class attributes (sleeve length, neckline shape and clothing category)
Describing Clothing by Semantic Attributes
Clothing pattern (Positive / Negative) | Solid (1052 / 441), Floral (69 / 1649), Spotted (101 / 1619)Plaid (105 / 1635), Striped (140 / 1534), Graphics (110 / 1668) |
Major color(Positive / Negative) | Red (93 / 1651), Yellow (67 / 1677), Green (83 / 1661), Cyan (90 / 1654)Blue (150 / 1594), Purple (77 / 1667), Brown (168 / 1576), White (466 / 1278)Gray (345 / 1399), Black (620 / 1124),>2Colors (203 / 1541) |
Wearing necktie | Yes 211, No 1528 |
Collar presence | Yes 895, No 567 |
Gender | Male 762, Female 1032 |
Wearing scarf | Yes 234, No 1432 |
Skin exposure | High 193, Low 1497 |
Placket presence | Yes 1159, No 624 |
Sleeve length | No sleeve (188), Short sleeve (323), Long sleeve (1270) |
Neckline shape | V-shape (626), Round (465), Others (223) |
Clothing category | Shirt (134), Sweater (88), T-shirt (108), Outerwear (220)Suit (232), Tank Top (62), Dress (260) |
Code/Model/Publication URL : Describing Clothing by Semantic Attributes
Source URL : Clothing Attributes Dataset
Dataset Examples :
18. TACO: Trash Annotations in Context for Litter Detection
Dataset Characteristics :
Task Type : litter detection and segmentation
Image Attribute : 2285 images
Label instance : 7426 annotations
Categories: 60 categories which belong to 28 super (top) categories
Super category | Category | Notes |
---|---|---|
Aluminum foil | Aluminum foil | – |
Battery | Battery | – |
Blister pack | Aluminum blister pack | Containers used to store capsules (e.g. pills) |
Blister pack | Carded blister pack | Paper-back package |
Bottle | Clear plastic bottle | Water and soft drink bottles made of PET |
Bottle | Glass bottle | Includes beer and wine bottles |
Bottle | Other plastic bottle | Opaque or translucent. Generally made of HDPE. Includes detergent bottles |
Bottle cap | Plastic bottle cap | – |
Bottle cap | Metal bottle cap | – |
Broken glass | Broken glass | – |
Can | Aerosol | – |
Can | Drink can | Aluminum soda can |
Can | Food can | Steel can |
Carton | Corrugated carton | Includes cardboard boxes |
Carton | Drink carton | Tetrapak composites |
Carton | Egg carton | – |
Carton | Meal carton | Includes sandwich boxes, paper plates, take-out boxes |
Carton | Pizza box | – |
Carton | Toilet tube | – |
Carton | Other carton | Paperboard boxes |
Cigarette | Cigarette | Cigarette butts |
Cup | Paper cup | – |
Cup | Disposable plastic cup | Generally made of PET |
Cup | Foam cup | Polystyrene Cup |
Cup | Glass cup | – |
Cup | Other plastic cup | Reusable plastic cups, thicker than disposable ones |
Food waste | Food waste | – |
Glass jar | Glass jar | – |
Lid | Plastic lid | Includes cup lids |
Lid | Metal lid | Generally glass jar lids |
Paper | Normal paper | – |
Paper | Tissues | – |
Paper | Wrapping paper | – |
Paper | Magazine paper | Plastified paper used in catalogues |
Paper bag | Paper bag | Brown bag |
Paper bag | Plastified paper bag | Bakery bags that come with transparent film |
Plastic bag & wrapper | Garbage bag | – |
Plastic bag & wrapper | Single-use carrier bag | – |
Plastic bag & wrapper | Polypropylene bag | Reusable bags |
Plastic bag & wrapper | Plastic Film | May be transparent or opaque. Inludes bread bags, cereal bags and produce bags |
Plastic bag & wrapper | Six pack rings | – |
Plastic bag & wrapper | Crisp packet | So common that it needs its own category |
Plastic bag & wrapper | Other plastic wrapper | Can be made of aluminium. Includes candy wrappers, retort pouches and yoghurt lids |
Plastic container | Spread tub | Includes margarine tubs and yoghurt pots |
Plastic container | Tupperware | HDPE microwavable tub |
Plastic container | Disposable food container | Includes black trays and PET containers |
Plastic container | Foam food container | Styrofoam takeaway boxes |
Plastic container | Other plastic container | – |
Plastic glooves | Plastic glooves | – |
Plastic utensils | Plastic utensils | – |
Pop tab | Pop tab | – |
Rope | Rope | Includes fishing nets |
Scrap metal | Scrap metal | Includes all metal except cans |
Shoe | Shoe | – |
Squeezable tube | Squeezable tube | Includes toothpaste and glue tubes |
Straw | Plastic straw | – |
Straw | Paper straw | – |
Styrofoam piece | Styrofoam piece | – |
Other plastic | Other plastic | Includes other objects or fragments made of plastic |
Unlabeled litter | Unlabeled litter | Unknown object of unknown material. Any ambiguous object. |
Code/Model/Publication URL : TACO: Trash Annotations in Context for Litter Detection
Source URL: TACO
Dataset Examples :
19. Crowd Instance-level Human Parsing (CIHP) Dataset
Dataset Characteristics :
Task Type: Instance-level Human Parsing, Semantic Part Segmentation
Image Count: 38280 multiple-person images with pixel-wise annotations of 19 semantic parts and of 20 categories in instance-level.
Categories: pixel-wise annotations on 20 categories.
Semantic Part Label |
|
Hat | Pants |
Hair | Gloves |
Sunglasses | Scarf |
Upper-clothes | Skirt |
Dress | Torso-skin |
Coat | Face |
Socks | Right arm |
Right leg | Left shoe |
Right shoe | Left leg |
Left arm |
Code/Model/Publication URL: Instance-level Human Parsing via Part Grouping Network
Source URL: Crowd Instance-level Human Parsing (CIHP) Dataset
Dataset Examples :
Credits: Crowd Instance-level Human Parsing (CIHP) Dataset
20. ModaNet
Dataset Characteristics :
Task Type: semantic segmentation on street fashion images in detail.
Image Count: 55,176 street image fully annotated with polygons.
Categories: Each polygon (segmentation mask) annotation is assigned to one of the following labels:
Label | Description | Fine-Grained-categories |
---|---|---|
1 | bag | bag |
2 | belt | belt |
3 | boots | boots |
4 | footwear | footwear |
5 | outer | coat/jacket/suit/blazers/cardigan/sweater/Jumpsuits/Rompers/vest |
6 | dress | dress/t-shirt dress |
7 | sunglasses | sunglasses |
8 | pants | pants/jeans/leggings |
9 | top | top/blouse/t-shirt/shirt |
10 | shorts | shorts |
11 | skirt | skirt |
12 | headwear | headwear |
13 | scarf & tie | scarf & tie |
Code/Model/Publication URL : ModaNet: A Large-scale Street Fashion Dataset with Polygon Annotations
Source URL: modanet
Dataset Examples :
Credits : ModaNet: A Large-scale Street Fashion Dataset with Polygon Annotations
21. Embrapa Wine Grape Instance Segmentation Dataset
Dataset Characteristics :
Task Type: instance segmentation for image-based monitoring and field robotics in viticulture.
Image Count: 300 images
Label Count: 2,020 binary masks for instance segmentation
Categories: Each polygon (segmentation mask) annotation is assigned to one of the following labels:
Prefix | Variety |
---|---|
CDY | Chardonnay |
CFR | Cabernet Franc |
CSV | Cabernet Sauvignon |
SVB | Sauvignon Blanc |
SYH | Syrah |
Code/Model/Publication URL: Grape detection, segmentation and tracking using deep neural networks and three-dimensional association
Source URL : Embrapa WGISD
Dataset Examples :
22. MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
Dataset Characteristics :
Task Type: fruit segmentation
Image Count: 1671 images of Apples
Label Count: 41,000 annotated object instances
Code/Model/Publication URL : MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
Source URL: MinneApple
Dataset Examples :
Credits : MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
23. FSS-1000: A 1000 Class Dataset for Few-shot Segmentation
Dataset Characteristics :
Task Type: image segmentation
Image Count: 10000 images with pixelwise segmentation labels
Categories: 1,000, with instance segmentation labels in 758 out of the 1,000 classes
Hierarchy of FSS-1000
Code/Model/Publication URL : FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation
Source URL: FSS-1000
Dataset Examples :
Credits : FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation
24. KINS Dataset
Dataset Characteristics :
Task Type: Amodal instance segmentation
Image Count: 14,991images from KITTI dataset
Label Count: On average, each image has 12.53 labeled instances, and each object polygon consists of 33.70 points.
Categories :
Category | People | Vehicle |
Subcategory | Pedestrian
Cyclist person-siting |
Car
Van Tram truck misc |
Code/Model/Publication URL: Amodal Instance Segmentation with KINS Dataset
Source URL: KINS Dataset
Dataset Examples :
Credits: Amodal Instance Segmentation with KINS Dataset
25. MSeg
Dataset Characteristics :
Task Type: semantic segmentation
Image Count : 80,000 images
Label Count: 220,000 object masks
Category: 194 categories.
Universal Class Name | Description |
airplane | an aircraft that has a fixed wing and is powered by propellers or jets |
animal-other | Any animal that is not a bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, or giraffe |
apparel | clothing in general, not currently being worn by a human being |
apple | fruit with red or yellow or green skin and sweet to tart crisp whitish flesh |
arcade machine | |
armchair | chair with a support on each side for arms (that is not a swivel chair, seat, or sofa) |
autorickshaw | in South Asia, a small motor vehicle with three wheels that is used as a taxi |
awning | a sheet of canvas or other material stretched on a frame and used to keep the sun or rain off a storefront, window, doorway, or deck. Should be attached to a building. |
backpack | a bag carried by a strap on your back or shoulder |
bag | |
banana | Elongated crescent-shaped yellow fruit with soft sweet flesh |
banner | long strip of cloth or paper used for decoration or advertising |
barrel | a cylindrical container that holds liquids |
base | |
baseball bat | an implement used in baseball by the batter |
baseball glove | the handwear used by fielders in playing baseball |
basket | a container used to hold or carry things, typically made from interwoven strips of cane or wire. |
bathtub | |
bathroom counter | a long, flat, narrow surface in a bathroom at waist-height |
bear | massive plantigrade carnivorous or omnivorous mammals with long shaggy coats and strong claws |
bed | |
bench | a long seat for more than one person |
bicycle | a wheeled vehicle that has two wheels and is moved by foot pedals |
bicyclist | a person who rides a bicycle |
bike rack | a rack for parking bicycles |
billboard | |
bird | Warm-blooded egg-laying vertebrates characterized by feathers and forelimbs modified as wings |
blanket | |
boat-ship | |
book | |
bookshelf | |
bottle | a glass or plastic vessel used for storing drinks or other liquids; typically cylindrical without handles and with a narrow neck that can be plugged or capped |
bowl | |
box | |
bridge | a structure that allows people or vehicles to cross an obstacle such as a river or canal or railway etc. |
broccoli | plant with dense clusters of tight green flower buds |
building | a structure that has a roof and walls and stands more or less permanently in one place. Includes houses, grandstands, booths, towers, and skyscrapers |
bulletin board | |
bus | a large vehicle carrying many passengers; used for public transport. Typically has swing doors, more than 4 wheels, tires larger than a regular car, and an illuminated front sign |
cabinet | A case or cupboard usually having doors and shelves. Can be found in a bathroom, kitchen, or other room. Cabinets are not for storing clothes. |
cake | baked goods made from or based on a mixture of flour, sugar, eggs, and fat |
car | |
carrot | deep orange edible root of the cultivated carrot plant |
case | a glass container used to store and display items in a shop or museum or home |
cat | feline mammal usually having thick soft fur and no ability to roar: domestic cats |
cctv camera | camera that can produce images or recordings for surveillance or other private purposes |
ceiling | |
cell phone | a phone with access to a cellular radio system so it can be used over a wide area, without a physical connection to a network; a mobile phone. |
chair_other | Any chair that does NOT fall into the following categories: armchair, stool, seat, sofa. A chair is defined as a seat for one person, with a support for the back. |
chandelier | branched lighting fixture; often ornate; hangs from the ceiling |
chest of drawers | furniture with drawers for keeping clothes |
clock | a timepiece that shows the time of day |
column | (architecture) a tall vertical cylindrical structure standing upright and used to support a structure |
conveyor belt | a moving belt that transports people or objects (as in a factory). Includes moving walk-ways and baggage carousels |
couch | an upholstered seat for more than one person (also known as sofa) |
counter-other | a long, flat, narrow surface used for making transactions in a store or in a home kitchen (fixed against a wall for preparing food). |
cow | cattle that are reared for their meat |
crib | baby bed with high sides made of slats |
cup | a container for holding liquids while drinking; includes mugs, and could be made of plastic, glass, ceramic, paper, or styrofoam. Cups include drinking glasses without a stem; if the cup/glass has a stem, it is a wine glass |
curtain-other | hanging cloth used as a blind (especially for a window). does not include shower curtains. |
desk | A piece of furniture with a flat or sloped surface with drawers, at which one can read,write, or do other work. A table, frame, or case with a sloping or horizontal surface especially for writing and reading
and often with drawers, compartments, and pigeon holes.Includes tables used as desks. |
dishwasher | a machine for washing dishes |
dog | a member of the genus Canis (probably descended from the common wolf) that has been domesticated by man since prehistoric times; occurs in many breeds |
donut | a small ring-shaped friedcake |
door | a swinging or sliding barrier that will close the entrance to a room or building or vehicle |
elephant | five-toed pachyderm |
escalator | a stairway whose steps move continuously on a circulating belt |
fan | a device for creating a current of air by movement of a surface or surfaces |
fence | |
fire hydrant | an upright hydrant for drawing water to use in fighting a fire |
fireplace | an open recess in a wall at the base of a chimney where a fire can be built |
flag | emblem usually consisting of a rectangular piece of cloth of distinctive design |
floor | The inside lower horizontal surface (as of a room, hallway, tent, or other structure). Must be indoors. This includes homogenous floor coverings that span an entire room, such as carpet, linoleum, or marble. |
food other | any food that is not a banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, or fruit |
fork | cutlery used for serving and eating food |
fountain | an ornamental structure in a pool or lake from which one or more jets of water are pumped into the air. Includes the jet of water. |
frisbee | a light, plastic disk propelled with a flip of the wrist for recreation or competition |
fruit other | any fruit, not an apple, orange, or banana (e.g. pineapple, melon, pear, kiwi, avocado) |
giraffe | tallest living quadruped; having a spotted coat and small horns and very long neck and legs; of savannahs of tropical Africa |
gravel | very small rock fragments and pebbles |
guard_rail | a strong fence/railing at the side of a road or in the middle of an expressway, intended to reduce the risk of serious accidents. A guardrail sits low to the ground and is made of thick horizontal metal rails. |
hair_dryer | |
horse | a hand-held electric blower that can blow warm air onto the hair |
hot dog | Solid-hoofed Herbivorous quadruped domesticated since prehistoric times |
junction box | A metal box outdoors, containing a junction of electric wires or cables |
keyboard | a freestanding panel of keys that operate a computer or Typewriter. Should not be part of a laptop (to prevent clashes). Connected with a cord to a computer. |
kitchen-island | an unattached counter in a kitchen that permits access from all sides |
kite | plaything consisting of a light frame covered with tissue paper; flown in wind at end of a string. Includes kites for wind-surfing. |
knife | edge tool used as a cutting instrument; has a pointed blade with a sharp edge and a handle |
lamp | a device for giving light that has a covering, but allows light to shine through or around. Could be freestanding on a floor and be covered with a shade, or could hang from the ceiling, or be placed on a table/desk/nightstand. |
laptop | a portable computer small enough to use in your lap. Includes the laptop keyboard. |
light-other | Any other type of light, e.g. fluorescent ceiling lights, bathroom lights embedded into ceiling, etc |
mailbox | a private box for delivery of mail |
microwave | kitchen appliance that cooks food by passing an electromagnetic wave through it |
mirror | polished surface that forms images by reflecting light |
motorcycle | a motor vehicle with two wheels and a strong frame |
motorcyclist | a human relying on a motorcycle/moped for movement. Must be actively riding the motorcycle/moped (not standing nearby it). |
mountain hill | |
mouse | a computer input device that controls an on-screen pointer |
net | an open fabric of string or rope or wire woven together at regular intervals |
night stand | a small low bedside table, typically having drawers. a small bedside table or stand |
orange | round yellow to orange fruit of any of several citrus trees |
ottoman | a low upholstered seat or thick cushion used as a seat |
oven | |
painting | graphic art consisting of an artistic composition made by applying paints to a surface |
paper | a material made of cellulose pulp derived mainly from wood or rags or certain grasses. Includes loose paper, reams of paper, paper towels, and napkins |
parking meter | a money-operated timer located next to a parking space; depositing money into it entitles you to park your car therefore a specified length of time |
person_nonrider | a human being who is not a motorcyclist, bicyclist, or rider other (could be a motor vehicle passenger) |
pier_wharf | Either a pier or a wharf. A pier is a platform/low structure built out from the shore into the water and supported by piles; provides access to ships and boats. A wharf is a level quayside area to which a ship may be moored to load and unload at the edge of water. |
pillow | |
pizza | Italian open pie made of thin bread dough spread with a spiced mixture of e.g. tomato sauce and cheese |
plate | |
platform | area alongside a railway track providing convenient access to trains, or a ramp/raised surface at a skatepark |
playingfield | a field used for outdoor team games. Includes tennis courts, baseball fields/diamonds, and fields for soccer, ultimate frisbee, and equestrian events. |
plaything_other | a child’s toy, or something used like a toy, that is not a teddy bear. |
pole | a long (usually round) rod of wood or metal or plastic. includes lamp posts (excluding the light) and streetlight poles (excluding the light) |
pool table | game equipment consisting of a heavy table on which pool is played |
poster | |
potted plant | a plant that is planted and grown in a container rather than in the ground |
radiator | heater consisting of a series of pipes for circulating steam or hot water to heat rooms or buildings |
railing-banister | a barrier consisting of a horizontal bar and supports; a railing at the side of a staircase or balcony to prevent people from falling |
railroad | a line of track providing a runway for wheels |
range_hood | exhaust hood over a kitchen range |
refrigerator | a refrigerator in which the coolant is pumped around by an electric motor |
remote | a device that can be used to control a machine or apparatus from a distance |
rider_other | a human relying upon a segway, skateboards, electric scooter, lawn mower, rickshaw, wheelchair etc (any other device that is not a motorcycle, bicycle, or vehicle)for movement on land. |
river_lake | River or lake. River: a large natural stream of water, with possible rapids. Lake: a body of (usually fresh) water
surrounded by land. Lakes generally have very still water. Should appear naturally formed. |
road | a long, narrow stretch with a smoothed or paved surface, made for traveling by motor vehicle, between two or more points; street or highway. Often bounded by curbs. |
road_barrier | concrete safety barriers on roads and highways; also known as Jersey barrier |
rock | a lump or mass of hard consolidated mineral matter |
rug_floormat | a floor covering of thick woven material or animal skin– just with limited size, covers only a small section of the floor. Includes rugs, and floormats (door mats, bath mat). Does not include carpet. |
runway | a strip of level paved surface where planes can take off,land, or taxi from a terminal;or a narrow platform extending from the stage into the audience in a theater or nightclub etc. |
sandwich | two (or more) slices of bread with a filling between them |
scissors | an edge tool having two crossed pivoting blades |
sconce | A decorative wall bracket/object for holding light bulbs, candles or other sources of light. Must be attached to the side of a wall. |
sculpture | statue or a three-dimensional work of art |
sea | A sea or ocean. Often has large amounts of waves/surf and a sandy beach. May have surfers on surfboards. Unlike a river or a lake, one generally cannot see the far bank of a sea/ocean. |
seat | a space reserved for sitting (as in a theater, auditorium, stadium, on a train or airplane, or in a car). (that is not a bench,chair, stool, sofa, or armchair) |
sheep | woolly usually horned ruminant mammal related to the goat |
shelf | |
shower | |
shower_curtain | fabric found next to a bathtub or shower to keep water from spilling out into the bathroom |
sidewalk_pavement | walk consisting of a paved area for pedestrians; usually beside a street or roadway (not a road) |
sink | plumbing fixture consisting of a water basin fixed to a wall or floor and having a drain-pipe (usually in a bathroom or kitchen) |
skateboard | a board with wheels that is ridden in a standing or crouching position and propelled by foot |
skis | sports equipment for skiing on snow |
sky | the atmosphere and outer space as viewed from the earth |
slow wheeled object | any slow-moving wheeled object, such as strollers, wheelchairs, and carts |
snow | a layer of snowflakes (white crystals of frozen water) covering the ground |
snowboard | a board that resembles a broad ski or a small surfboard; used in a standing position to slide down snow-covered slopes |
spoon | a piece of cutlery with a shallow bowl-shaped container and a handle; used to stir or serve or take up food |
sports ball | round object that is hit or thrown or kicked in games |
stage | a large platform on which people can stand and can be seen by an audience |
stairs | |
stool | a simple seat without a back or arms; or a high seat at a bar without arms |
storage_tank | a large (usually metallic) vessel for holding gases or liquids |
stove | |
streetlight | a lamp or light supported on a lamppost/pole; for illuminating a street |
suitcase | a portable rectangular container for carrying clothes |
surfboard | a narrow buoyant board for riding surf |
swimming_pool | pool that provides a facility for swimming |
swivel_chair | a chair that swivels on its base. Can be turned around a central point to face in a different direction without moving the legs |
table | A piece of furniture consisting of a smooth flat slab fixed on legs. A piece of furniture with a flat top and one or more legs, providing a level surface on which objects may be placed, and that can be used for such purposes as eating, writing, working, or playing games. |
teddy_bear | Stuffed toy in the form of a bear. It is a type of plaything/child’s toy (usually plush and stuffed with soft materials). |
television | |
tennis racket | a racket used to play tennis |
tent | a portable shelter (usually of canvas stretched over supporting poles and fastened to the ground with ropes and pegs). For example, a camping tent. |
terrain | dirt, sand, grass, or any kind of horizontally spreading vegetation |
tie | neckwear consisting of a long narrow piece of material worn (mostly by men) under a collar and tied in knot at the front |
toaster | |
toilet | a plumbing fixture for defecation and urination |
toothbrush | small brush; has long handle;used to clean teeth |
towel | a thick, rectangular piece of absorbent cloth for drying or wiping |
traffic light | a visual signal to control the flow of traffic at intersections (includes stoplights, pedestrian crossing lights, etc) |
traffic sign | a sign usually on the side of a street or highway bearing symbols or words of warning or direction to motorists or pedestrians |
trailer | |
train | All vehicles that move on rails,
e.g. trams, trains. |
trash can | a bin that holds rubbish until it is collected |
tray | a flat, shallow container with a raised rim, typically used for carrying food and drink, or for holding small items. |
truck | |
tunnel | a passageway through or under something, usually underground (especially one for trains or cars) |
umbrella | a lightweight collapsible canopy. Could be handheld or mounted above a table outdoors. |
vase | an open jar of glass, porcelain,or metal used as an ornament or to hold flowers |
vegetation | Trees, hedges, and all kinds of vertically growing vegetation (for example, must be higher than lawn grass) |
wall | Either: one of the sides of a room or building connecting the floor and ceiling. Or: a masonry fence around a garden,park, or estate. |
wardrobe | a tall piece of furniture that provides storage space for clothes; has a door and rails or hooks for hanging clothes |
washer-dryer | a home appliance for washing or drying clothes and linens automatically |
waterfall | a steep descent of the water of a river |
water_other | all other kinds of water, e.g. puddles, ditchwater, aquariums, shallow artificially made ponds, zoo ponds, flooded areas, bathtub water, water coming out of a fire hydrant, water from a kitchen faucet, etc.is not a lake, river, fountain,swimming pool, or sea. |
whiteboard | a hard smooth white surface used for writing or drawing on with markers. |
window | a framework of wood or metal that contains a glass window-pane and is built into a wall or roof to admit light or air |
window-blind | A blind for privacy or to keep out light. Placed indoors on a window. |
wine_glass | a glass that has a stem and in which wine is served |
zebra | any of several fleet black-and-white striped African equines |
Code/Model/Publication URL : MSeg: A Composite Dataset for Multi-domain Semantic Segmentation
Source URL: MSeg
Dataset Examples :
Credits : MSeg: A Composite Dataset for Multi-domain Semantic Segmentation
26. PASCAL-Context Dataset
Dataset Characteristics :
Task Type: semantic segmentation, object segmentation
Image Count : 10,103
Label Count : 4,203
Categories : 540
Code/Model/Publication URL: The Role of Context for Object Detection and Semantic Segmentation in the Wild
Source URL: PASCAL-Context Dataset
Dataset Examples :
Credits : PASCAL-Context Dataset
27. LVIS: A DATASET FOR LARGE VOCABULARY INSTANCE SEGMENTATION
Dataset Characteristics :
Task Type: Instance segmentation
Image Count: ~160k images
Label Count: ~2M instance annotations
Categories: 1203
Code/Model/Publication URL : LVIS: A Dataset for Large Vocabulary Instance Segmentation
Source URL: LVIS Dataset
Dataset Examples :
Credits: LVIS Dataset
28. Flower Datasets
Dataset Characteristics :
Task Type: flower segmentation
Image Count : Dataset 1 : 1360 images consisting of 17 flower species
Dataset 2 : 8,189 images consisting of 102 flower species
Categories : 119 categories
Dataset 1 :
Flowers Class |
Buttercup |
Colts’ Foot |
Daffodil |
Dandelion |
Daisy |
fritillary |
Iris |
Pansy |
Sunflower |
Windflower |
Snowdrop |
Lily Valley |
Bluebell |
Crocus |
Tigerlily |
Tulip |
Cowslip |
Dataset 2 :
Category | #images | Category | #images | Category | #images | |||
---|---|---|---|---|---|---|---|---|
alpine sea holly | 43 | buttercup | 71 | fire lily | 40 | |||
anthurium | 105 | californian poppy | 102 | foxglove | 162 | |||
artichoke | 78 | camellia | 91 | frangipani | 166 | |||
azalea | 96 | canna lily | 82 | fritillary | 91 | |||
ball moss | 46 | canterbury bells | 40 | garden phlox | 45 | |||
balloon flower | 49 | cape flower | 108 | gaura | 67 | |||
barbeton daisy | 127 | carnation | 52 | gazania | 78 | |||
bearded iris | 54 | cautleya spicata | 50 | geranium | 114 | |||
bee balm | 66 | clematis | 112 | giant white arum lily | 56 | |||
bird of paradise | 85 | colt’s foot | 87 | globe thistle | 45 | |||
bishop of llandaff | 109 | columbine | 86 | globe-flower | 41 | |||
black-eyed susan | 54 | common dandelion | 92 | grape hyacinth | 41 | |||
blackberry lily | 48 | corn poppy | 41 | great masterwort | 56 | |||
blanket flower | 49 | cyclamen | 154 | hard-leaved pocket orchid | 60 | |||
bolero deep blue | 40 | daffodil | 59 | hibiscus | 131 | |||
bougainvillea | 128 | desert-rose | 63 | hippeastrum | 76 | |||
bromelia | 63 | english marigold | 65 | japanese anemone | 55 | |||
king protea | 49 | peruvian lily | 82 | stemless gentian | 66 | |||
lenten rose | 67 | petunia | 258 | sunflower | 61 | |||
lotus | 137 | pincushion flower | 59 | sweet pea | 56 | |||
love in the mist | 46 | pink primrose | 40 | sweet william | 85 | |||
magnolia | 63 | pink-yellow dahlia? | 109 | sword lily | 130 | |||
mallow | 66 | poinsettia | 93 | thorn apple | 120 | |||
marigold | 67 | primula | 93 | tiger lily | 45 | |||
mexican aster | 40 | prince of wales feathers | 40 | toad lily | 41 | |||
mexican petunia | 82 | purple coneflower | 85 | tree mallow | 58 | |||
monkshood | 46 | red ginger | 42 | tree poppy | 62 | |||
moon orchid | 40 | rose | 171 | trumpet creeper | 58 | |||
morning glory | 107 | ruby-lipped cattleya | 75 | wallflower | 196 | |||
orange dahlia | 67 | siam tulip | 41 | water lily | 194 | |||
osteospermum | 61 | silverbush | 52 | watercress | 184 | |||
oxeye daisy | 49 | snapdragon | 87 | wild pansy | 85 | |||
passion flower | 251 | spear thistle | 48 |
|
windflower | 54 | ||
pelargonium | 71 | spring crocus | 42 | yellow iris | 49 |
Credits : 102 Category Flower Dataset
Code/Model/Publication URL: Automated flower classification over a large number of classes
Source URL: Flower Dataset
Dataset Examples :
Credits: Automated flower classification over a large number of classes
29. NYU Depth Dataset V2
Dataset Characteristics :
Task Type: Dense Semantic Image Segmentation, Indoor segmentation
Image Attribute: 1449 densely labeled pairs of aligned RGB and depth images
464 different indoor scenes across 26 scene classes
5,064 distinct objects,spanning 894 different classes
Code/Model/Publication URL: Indoor Segmentation and Support Inference from RGBD Images
Source URL: NYU Depth Dataset V2
Dataset Examples :
Credits: NYU Depth Dataset V2
30. Fashionista
Dataset Characteristics :
Task Type: Clothing Estimation
Image Attribute : 158,235 fashion photos
Categories :
Garments |
||
background(null) | top | cardigan |
skin | skirt | blazer |
hair | jacket | t-shirt |
dress | coat | socks |
bag | shirt | necklace |
blouse | shoes | bracelet |
purse | accessories | boots |
sweater | jumper | cape |
leggings | romper | vest |
belt | tights | jeans |
heels | wedges | stockings |
hat | shorts |
Code/Model/Publication URL: Parsing Clothing in Fashion Photographs
Source URL: Clothing Parsing
Dataset Examples :
Credits : Parsing Clothing in Fashion Photographs
31. UNIMIB Food Database
Dataset Characteristics :
Task Type: food segmentation
Image Attribute: 1,027 canteen trays for a total of 3,616 food instances belonging to 73 food classes.
Segmented images of the 73 food categories
Code/Model/Publication URL: Food recognition: a new dataset, experiments, and results
Source URL: UNIMIB Food Database
Dataset Examples :
Credits: Food recognition: a new dataset, experiments, and results
32. SYNSCAPES
Dataset Characteristics :
Task Type: Semantic segmentation
Image Attribute: 25,000 RGB images in PNG format at 1440×720 resolution
Categories: 19
Classes |
|
Road | SideWalk |
Building | Wall |
Fence | Pole |
Tr.Sign | Tr. Light |
Vegetation | Terrain |
Sky | Person |
Rider | Car |
Truck | Bus |
Train | Motorcycle |
Bicycle |
Code/Model/Publication URL : Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing
Source URL: SYNSCAPES
Dataset Examples :
Credits : Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing
33. YouTube-VOS
Dataset Characteristics :
Task Type :
-
- Semi-supervised Video Object Segmentation
- Video Instance Segmentation
Video Attribute :
-
- 4000+ high-resolution YouTube videos
- 90+ semantic categories
- 7800+ unique objects
- 190k+ high-quality manual annotations
- 340+ minutes duration
Object categories in YouTube-VOS |
||||
person | cat | train | hedgehog | squirrel |
table | ape | snake | owl | eagle |
rope | camera | parrot | zebra | plant |
snail | chameleon | watch | giant_panda | giraffe |
airplane | toilet | box | stuffed_toy | tissue |
sedan | bear | bus | camel | guitar |
lizard | fox | shark | frisbee | kangaroo |
microphone | duck | leopard | tiger | whale |
cloth | cup | dog | elephant | surfboard |
knife | bottle | shovel | skateboard | horse |
earless_seal | tennis_racket | small_panda | flag | monkey |
others | frog | crocodile | spider | mirror |
sheep | deer | mouse | umbrella | ball |
ring | fish | motorbike | boat | paddle |
jellyfish | necklace | rabbit | turtle | snowboard |
raccoon | eyeglasses | ant | hat | bird |
penguin | parachute | backpack | cow | truck |
lion | bucket | butterfly | hand | dolphin |
sign | bike | handbag |
Code/Model/Publication URL :
Source URL : YouTube-VOS
Dataset Examples :
Credits : Video Instance Segmentation
34. The Lane Marker Dataset
Dataset Characteristics :
Task Type : binary marker segmentation, lane-dependent pixel-level segmentation
Image Attribute : 100,042 labeled lane marker images from about 350 km recorded drives
Code/Model/Publication URL : Unsupervised Labeled Lane Markers Using Maps
Source URL : The Lane Marker Dataset
Dataset Examples :
Credits : Unsupervised Labeled Lane Markers Using Maps
35. Total-Text
Dataset Characteristics :
Task Type : Scene Text Detection, Segmentation-based text detection
Image Attribute : It has 1,555 scene images, 4,265 curved text, 9,330 annotated words with 3 different text orientations including horizontal, multi-oriented, and curved text.
Code/Model/Publication URL : Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition
Source URL : The Lane Marker Dataset
Dataset Examples :
Credits : Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition
36. MVTec D2S: Densely Segmented Supermarket Dataset
Dataset Characteristics :
Task Type : instance-aware semantic segmentation in an industrial domain
Image Attribute : 21,000 images in 700 different scenes with various backgrounds, clutter objects, and occlusion levels.
Categories : 60
adelholzener_alpenquelle_classic_075 | carrot |
adelholzener_classic_naturell_02 | grapes_green_sugraone_seedless |
apple_roter_boskoop | adelholzener_classic_bio_apfelschorle_02 |
banana_single | salad_iceberg |
caona_kakaohaltiges_getraenkepulver | banana_bundle |
clementine | lettuce |
clementine_single | avocado |
coca_cola_light_05 | apple_golden_delicious |
corny_nussvoll_single | augustiner_lagerbraeu_hell_05 |
douwe_egberts_professional_kaffee_gemahalen | corny_schoko_banana_single |
ethiquable_gruener_tee_ceylon | cocoba_fruehstueckskakao_mit_honing |
franken_tafelreinigar | adelholzener_gourmet_mineralwasser_02 |
gepa_bio_und_fair_fencheltee | grapes_sweet_celebration_seedless |
gepa_bio_und_fair_pfefferminztee | coca_cola_05 |
koelln_muesli_schoko | tegernseer_hell_03 |
pasta_reggia_fusilli | koelln_muesli_fruechte |
pasta_reggia_spaghetti | feldsalat |
rispentomaten | gepa_bio_und_fair_kamillentee |
roma_rispentomaten | dr_oetker_vitalis_knuspermuesli_klassisch |
suntory_gokuri_limonade | cucumber |
pear | orange_single |
kilimanjaro_tea_earl_grey | kiwi |
zucchini | apple_granny_smith |
pelikan_tintenpatrone_canon | gepa_bio_caffe_crema |
cafe_wunderbar_espresso | gepa_italienischer_bio_espresso |
gepa_bio_und_fair_rooibostee | corny_nussvoll |
augustiner_weissbier_05 | pasta_reggia_elicoidali |
corny_schoko_banana | rucola |
adelholzener_alpenquelle_naturell_075 | apple_braeburn_bundle |
gepa_bio_und_fair_kraeuterteemischung | oranges |
Code/Model/Publication URL : MVTec D2S: Densely Segmented Supermarket Dataset
Source URL : MVTec D2S Dataset
Dataset Examples :
Credits : MVTec D2S: Densely Segmented Supermarket Dataset
37. CORe50
Dataset Characteristics :
Task Type : image segmentation
Video Attribute : 50 domestic objects videos collected in 11 distinct sessions (8 indoor and 3 outdoor) characterized by different backgrounds and lighting.
Categories : 10
plug adapters |
mobile phones |
scissors |
light bulbs |
cans |
glasses |
balls |
markers |
cups |
remote controls |
Code/Model/Publication URL : CORe50: a New Dataset and Benchmark for Continuous Object Recognition
Source URL : CORe50
Dataset Examples :
Credits : CORe50: a New Dataset and Benchmark for Continuous Object Recognition
38. Fashionpedia
Dataset Characteristics :
Task Type : Apparel instance segmentation
Image Attribute : 48,825 clothing images in daily-life, street-style,celebrity events, runway, and online shopping with 46 apparel categories and 294 attributes
Code/Model/Publication URL : Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset
Source URL : Fashionpedia
Dataset Examples :
Credits : Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset
39. The Oxford-IIIT Pet Dataset
Dataset Characteristics :
Task Type : image segmentation
Image Attribute : 7349 images of cats and dogs
Categories :
Code/Model/Publication URL : Cats and Dogs
Source URL : The Oxford-IIIT Pet Dataset
Dataset Examples :
Credits : The Oxford-IIIT Pet Dataset
40. BDD100K: A Large-scale Diverse Driving Video Database
Dataset Characteristics :
Task Type : drivable area segmentation, semantic segmentation, instance segmentation, multi-object segmentation tracking
Video Attribute : 100K videos and 10 tasks
Code/Model/Publication URL : BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Source URL : BDD100K
Dataset Examples :
Credits : BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
41. MOTS Dataset
Dataset Characteristics :
Task Type : Multi-Object Tracking and Segmentation
Video Attribute : 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras
Code/Model/Publication URL : MOTS: Multi-Object Tracking and Segmentation
Source URL : MOTS
Dataset Examples :
Credits : MOTS: Multi-Object Tracking and Segmentation
42. KITTI MOTS Dataset
Dataset Characteristics :
Task Type : Multi-Object Tracking and Segmentation
Video Attribute : 21 training sequences and 29 test sequences
Code/Model/Publication URL : MOTS: Multi-Object Tracking and Segmentation
Source URL : KITTI-MOTS
Dataset Examples :
Credits : MOTS: Multi-Object Tracking and Segmentation
43. ApolloScape Dataset
Dataset Characteristics :
Task Type : Lanemark segmentation
Image Attribute : 100K image frames, 80k lidar point cloud and 1000km trajectories for urban traffic
Code/Model/Publication URL : DVI: Depth Guided Video Inpainting for Autonomous Driving
Source URL : ApolloScape
Dataset Examples :
Credits : Toolkit for ApolloScape Dataset
44. Camouflaged Object (CAMO) dataset
Dataset Characteristics :
Task Type : camouflaged object segmentation
Image Attribute : 1250 images under two categories, i.e., naturally camouflaged objects and artificially camouflaged objects.
Categories : Camouflaged animals consist of amphibians, birds, insects, mammals, reptiles, and underwater animals in various environments, i.e., ground, underwater, desert, forest, mountain, and snow.
Camouflaged human falls into soldiers on the battlefields and human body painting arts.
Code/Model/Publication URL : Anabranch Network for Camouflaged Object Segmentation
Source URL : Camouflaged Object Segmentation
Dataset Examples :
Credits : Anabranch Network for Camouflaged Object Segmentation
45. Berkeley Segmentation Data Set and Benchmarks 500 (BSDS500)
Dataset Characteristics :
Task Type : image segmentation
Image Attribute : 300 images are used for training / validation and 200 fresh images, together with human annotations, are added for testing
Code/Model/Publication URL : Contour Detection and Hierarchical Image Segmentation
Source URL : Berkeley Segmentation Data Set and Benchmarks 500 (BSDS500)
Dataset Examples :
Credits : Contour Detection and Hierarchical Image Segmentation
46. Microsoft Research Cambridge Object Recognition Image Database (MSRC v1 & v2)
Dataset Characteristics :
Task Type : image segmentation, semantic segmentation
Image Attribute : v1 : 240 images and 9 object classes with coarse pixel-wise labeled images.
v2 : 591 images, 23 object classes with accurate pixel-wise labeled images.
Code/Model/Publication URL : Microsoft Research Cambridge Object Recognition Image Database
Source URL : Microsoft Research Cambridge Object Recognition Image Database
Credits : Microsoft Research Cambridge Object Recognition Image Database
47. Transmission Electron Microscopic (TEM) Cell Recordings Segmentation Dataset
Dataset Characteristics :
Task Type : automatic segmentations for mitochondria
Image Attribute : A minimum of 10000 cells were recorded under five classes (background, cytoplasm,nucleus, mitochondria and vesicles)
Code/Model/Publication URL : Semi-automatic procedure for the determination of the cell surface area used in systems immunology
Source URL : Transmission Electron Microscopic (TEM) Cell Recordings Segmentation Dataset
Dataset Examples :
Credits : Semi-automatic procedure for the determination of the cell surface area used in systems immunology
48. Freiburg-Berkeley Motion Segmentation Dataset (FBMS-59)
Dataset Characteristics :
Task Type : Motion Segmentation
Video Attribute : 59 video sequence with 720 frames annotated
Code/Model/Publication URL : Segmentation of moving objects by long term video analysis
Source URL : Freiburg-Berkeley Motion Segmentation Dataset (FBMS-59)
Dataset Examples :
Credits : Freiburg-Berkeley Motion Segmentation Dataset (FBMS-59)
49. Georgia Tech Segmentation and Tracking Dataset
Dataset Characteristics :
Task Type : target segmentation and tracking in video
Video Attribute : 6 videos, 243 frames
parachute | girl |
monkeydog | penguin |
birdfall | cheetah |
Code/Model/Publication URL : Motion Coherent Tracking Using Multi-label MRF Optimization
Source URL : GaTech SegTrack
Dataset Examples :
Credits : GaTech SegTrack
50. Video Segmentation Benchmark (VSB100) Dataset
Dataset Characteristics :
Task Type : Video Segmentation
Video Attribute : 100 HD quality videos with ground truth annotations
Code/Model/Publication URL : A Unified Video Segmentation Benchmark: Annotation, Metrics and Analysis
Source URL : Video Segmentation Benchmark (VSB100) Dataset
Dataset Examples :
Credits : A Unified Video Segmentation Benchmark: Annotation, Metrics and Analysis
51. Scene Flow Datasets
Dataset Characteristics :
Task Type : Object-level and material-level segmentation
Image Attribute : more than 39000 stereo frames in 960×540 pixel resolution
Code/Model/Publication URL : A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
Source URL : Scene Flow Datasets
Dataset Examples :
Credits : A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
52. FreiHAND Dataset
Dataset Characteristics :
Task Type : hand pose and shape estimation from single color image
Image Attribute : 130240 training and 3960 evaluation samples with Hand segmentation mask (224×224 pixels)
Code/Model/Publication URL : FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape from Single RGB Images
Source URL : FreiHAND Dataset
Dataset Examples :
Credits : FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape from Single RGB Images
53. YouTube-Objects Dataset
Dataset Characteristics :
Task Type : video segmentation, foreground object segmentation
Video Attribute : 26 web videos with 10 object classes and more than 20,000 frames
aeroplane | bird |
boat | car |
cat | cow |
dog | horse |
motorbike | train |
Code/Model/Publication URL : Supervoxel-Consistent Foreground Propagation in Video
Source URL : YouTube-Objects Segmentation Labels
Dataset Examples :
Credits : Supervoxel-Consistent Foreground Propagation in Video
54. SegTrack v2 Dataset
Dataset Characteristics :
Task Type : video segmentation
Video Attribute : 1,000 frames with pixel-level annotations for 14 categories
Girl | Birdfall |
Parachute | Cheetah |
Monkeydog | Penguin |
Drifting Car | Hummingbird |
Frog | Worm |
Soldier | Monkey |
Bird of Paradise | BMX |
Code/Model/Publication URL : Video Segmentation by Tracking Many Figure-Ground Segments
Source URL : SegTrack v2 Dataset
Dataset Examples :
Credits : SegTrack v2 Dataset
55. Human semantic part segmentation Dataset
Dataset Characteristics :
Task Type : ground and aerial robot segmentation
Image Attribute : two hundred and one images of six different people in multiple viewpoints
Categories : 14 body parts
Head | Torso |
Left Upper arm | Left Lower arm |
Left hand | Right Upper hand |
Right Lower arm | Right hand |
Right Upper leg | Right Lower leg |
Right foot | Left Upper leg |
Left Lower leg | Left foot |
Code/Model/Publication URL : Deep Learning for Human Part Discovery in Images
Source URL : Human semantic part segmentation Dataset
Dataset Examples :
Credits : Deep Learning for Human Part Discovery in Images
56. Cambridge-driving Labeled Video Database (CamVid)
Dataset Characteristics :
Task Type : semantic segmentation
Image Attribute : per-pixel semantic segmentation of over 700 images
Categories : 32 semantic classes
List of the 32 object class names and their corresponding colors used for labeling.
Code/Model/Publication URL : Semantic Object Classes in Video: A High-Definition Ground Truth Database
Source URL : Human semantic part segmentation Dataset
Dataset Examples :
Credits : Semantic Object Classes in Video: A High-Definition Ground Truth Database
57. Cityscapes Dataset
Dataset Characteristics :
Task Type : instance segmentation, semantic segmentation
Image Attribute : 25k Images
Group |
Classes |
flat | road, sidewalk, parking, rail track |
human | person, rider |
vehicle | car, truck, bus, on rails, motorcycle, bicycle, caravan, trailer |
construction | building, wall, fence, guard rail, bridge, tunnel |
object | pole, pole group, traffic sign, traffic light |
nature | vegetation, terrain |
sky | sky |
void | ground, dynamic, static |
*require institutional login
Code/Model/Publication URL : Semantic Understanding of Urban Street Scenes
Source URL : Cityscapes Dataset
Dataset Examples :
Credits : Cityscapes Dataset
58. Multi-Human Parsing (MHP) v1.0 Dataset
Dataset Characteristics :
Task Type : multi-human parsing
Image Attribute : 4,980 images consist of 14,969 person instances with fine-grained annotations at pixel-level
Categories : 18 different semantic labels
Face | Hair |
Upper Clothes | Right Arm |
Left Arm | Pants |
Left Shoe | Right Shoe |
Right Leg | Left Leg |
Torso Skin | Dress |
Hat | Skirt |
Bag | Sun Glasses |
Belt | Scarf |
Code/Model/Publication URL : Multi-Human Parsing in the Wild
Source URL : Multi-Human Parsing (MHP) v1.0 Dataset
Dataset Examples :
Credits : Multi-Human Parsing in the Wild
59. Mut1ny head/face segmentation dataset
Dataset Characteristics :
Task Type : head/face segmentation
Image Attribute : 16.5k (16557) fully pixel-level labeled segmentation images
Categories : 11 facial labels
Lips | Eyes |
Nose | Hair |
Ears | Eyebrows |
Teeth | General face |
Facial hair | Specs/sunglasses |
Background/undefined |
Source URL : Mut1ny head/face segmentation dataset
Dataset Examples :
Credits : Mut1ny head/face segmentation dataset
60. Semantic Drone Dataset
Dataset Characteristics :
Task Type : semantic understanding of urban scenes for increasing the safety of autonomous drone flight and landing procedures
Image Attribute : The training set contains 400 publicly available images and the test set is made up of 200 private images.
Semantic classes of the Drone Dataset
|
|
|
|
Source URL : Semantic Drone Dataset
Dataset Examples :
Credits : Semantic Drone Dataset
61. The SYNTHIA dataset
Dataset Characteristics :
Task Type : semantic segmentation
Image Attribute : +200,000 HD images from video streams and +20,000 HD images from independent snapshots
Categories : pixel-level semantic annotations for 13 classes
sky | building |
road | sidewalk |
fence | vegetation |
lane-marking | pole |
car | traffic signs |
pedestrians | cyclists |
miscellaneous |
Code/Model/Publication URL : The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes
Source URL : The SYNTHIA dataset
Dataset Examples :
Credits : The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes
62. RailSem19
Dataset Characteristics :
Task Type : A Dataset for Semantic Rail Scene Understanding
Image Attribute : 8500 unique images having 110000 annotation taken from a the ego-perspective of a rail vehicle. Over 1000 examples with railway crossings and 1200 tram scenes.
Categories : pixel-level semantic annotations for 13 classes
buffer-stop | crossing |
guardrail | train-car |
platform | rail |
switch-ind. | switch-left |
switch-right | switch-unknown |
switch-static | track-sign-front |
track-signal-back | track-signal-front |
person-group | truck |
car | fence |
person | pole |
rail-occluder |
Code/Model/Publication URL : RailSem19: A Dataset for Semantic Rail Scene Understanding
Source URL : RailSem19
Dataset Examples :
Credits : RailSem19: A Dataset for Semantic Rail Scene Understanding
63. DeepFashion: In-shop Clothes Retrieval Dataset
Dataset Characteristics :
Task Type : instance semantic segmentation
Image Attribute : 7,982 number of clothing items, 52,712 number of in-shop clothes images, and ~200,000 cross-pose/scale pairs;
Code/Model/Publication URL : DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations
Source URL : DeepFashion: In-shop Clothes Retrieval
Dataset Examples :
Credits : DeepFashion: In-shop Clothes Retrieval
64. Indian Driving Dataset
Dataset Characteristics :
Task Type : road scene understanding in unstructured environments
Image Attribute : 10,004 images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads
road | parking |
drivable fallback | ground |
sidewalk | rail track |
non-drivable fallback | train |
person | animal |
rider | motorcycle |
bicycle | autorickshaw |
car | truck |
bus | caravan |
trailer | train |
vehicle fallback | curb |
wall | fence |
guard rail | billboard |
traffic sign | traffic light |
pole | polegroup |
obs-str-bar-fallback | building |
bridge | tunnel |
vegetation | sky |
fallback background |
Code/Model/Publication URL : IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments
Source URL : Indian Driving Dataset
Dataset Examples :
Credits : IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments
65. A2D2 Dataset
Dataset Characteristics :
Task Type : Instance Segmentation, Semantic Segmentation
Image Attribute : 41,277 camera images are semantically labelled.
Natural Object | Sky |
RD normal street | Building |
Car | SideWalk |
Ego car | Truck |
Grid Structure | Road Blocks |
Drivable cobblestone | Curbstone |
Solid line | Non-drivable street |
Poles | Dashed lines |
Irrelevant Signs | Traffic sign |
Parking area | Obstacles/trash |
RD restricted area | Traffic guide obj |
Slow Drive area | Signal corpus |
Pedestrian | Bicycle |
Painted driv. instr | utility vehicle |
traffic signal | Sidebars |
Small vehicles | zebra crossing |
Electronic traffic | Tractor |
Blurred Area | Animals |
Speed bumper | Rain dirt |
Code/Model/Publication URL : A2D2: Audi Autonomous Driving Dataset
Source URL : A2D2 Dataset
Dataset Examples :
Credits : A2D2: Audi Autonomous Driving Dataset
66. Mapillary Vistas Dataset
Dataset Characteristics :
Task Type : semantic image segmentation and instance-specific image segmentation
Image Attribute : 25 000 high-resolution images annotated into 66 object categories with additional, instance-specific labels for 37 classes.
Bird | Ground Animal |
Crosswalk-Plain | Person |
Bicyclist | Motorcyclist |
other rider | Lane Marking - Crosswalk |
Banner | Bench |
Bike Rack | Billboard |
Catch Basin | CCTV Camera |
Fire Hydrant | Junction Box |
Mailbox | Manhole |
Phone Booth | Street Light |
Pole | Traffic Sign Frame |
Utility Pole | Traffic Light |
Traffic Sign (Back) | Traffic Sign (Front) |
Trash Can | Bicycle |
Boat | Bus |
Car | Caravan |
Motorcycle | Other Vehicle |
Trailer | Truck |
Wheeled Slow |
*require institutional login
Code/Model/Publication URL : The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes
Source URL : Mapillary Vistas Dataset
Dataset Examples :
Credits : The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes
67. eTRIMS Image Database
Dataset Characteristics :
Task Type : object segmentation and class segmentation of real world street scene.
Image Attribute : The database is comprised of two datasets,
The 4-Class eTRIMS Dataset with 4 annotated object classes with 60 images and 534 annotated objects.
- sky
- building
- vegetation
- pavement/road
The 8-Class eTRIMS Dataset with 8 annotated object classes with 60 images and 1702 annotated objects.
- sky
- building
- vegetation
- pavement
- window
- door
- car
- road
Code/Model/Publication URL : eTRIMS Image Database for Interpreting Images of Man-Made Scenes
Source URL : eTRIMS Image Database
Dataset Examples :
Credits : eTRIMS Image Database
68. Daimler Urban Segmentation Dataset
Dataset Characteristics :
Task Type : image segmentation of highly cluttered urban traffic scenes
Image Attribute : 5000 rectified stereo image pairs with a resolution of 1024×440 with pixel-level semantic class annotations into 5 classes: ground, building, vehicle, pedestrian, sky
Code/Model/Publication URL : Efficient Multi-Cue Scene Segmentation
Source URL : Daimler Urban Segmentation Dataset
Dataset Examples :
Credits : Stixmantics: A Medium-Level Model for Real-Time Semantic Scene Understanding
69. KITTI Motion Segmentation Dataset
Dataset Characteristics :
Task Type : Motion Segmentation
Image Attribute : six sequences from KITTI raw data to generate a total of 1750 frames. In addition to these frames, 200 frames from KITTI scene flow are used to provide us with 1950 frames in total.
Code/Model/Publication URL : MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving
Source URL : KITTI Motion Segmentation Dataset
Dataset Examples :
Credits : MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving
70. CUHK DeepFashion2 Dataset
Dataset Characteristics :
Task Type : Cloth Segmentation
Image Attribute : 491K diverse images of 13 popular clothing categories, 801K clothing clothing items, 873K Commercial-Consumer clothes pairs
short sleeve top | long sleeve top |
short sleeve outwear | long sleeve outwear |
vest | sling |
shorts | trousers |
skirt | short sleeve dress |
long sleeve dress | vest dress |
sling dress |
Code/Model/Publication URL : DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation,Segmentation and Re-Identification of Clothing Images
Source URL : DeepFashion2
Dataset Examples :
Credits : DeepFashion2
71. WildDash 2 Dataset
Dataset Characteristics :
Task Type : semantic and instance segmentation for the automotive domain
Image Attribute : 4256 public frames
Categories : pixel-level semantic annotations for 20 classes
Code/Model/Publication URL : WildDash – Creating Hazard-Aware Benchmarks
Source URL : WildDash 2 Dataset
Dataset Examples :
Credits : WildDash – Creating Hazard-Aware Benchmarks
72. CAD 120 affordance dataset
Dataset Characteristics :
Task Type: semantic image segmentation
Image Attribute: contain 3090 images containing 9916 object instances
Affordances in the dataset:
1.openable
2.cuttable
3.pourable
4.containable
5.supportable
6.holdable
Code/Model/Publication URL: Weakly Supervised Affordance Detection
Source URL : CAD 120 Affordance Segmentation Dataset
Dataset Examples :
Credits : Weakly Supervised Affordance Detection
73. The Aberystwyth Leaf Evaluation Dataset
Dataset Characteristics :
Task Type : Leaf Segmentation
Code/Model/Publication URL : The Aberystwyth Leaf Evaluation Dataset: A plant growth visible light image dataset of Arabidopsis thalian
Source URL : The Aberystwyth Leaf Evaluation Dataset
Dataset Examples :
Credits : The Aberystwyth Leaf Evaluation Dataset: A plant growth visible light image dataset of Arabidopsis thalian
74. Shadow Detection/Texture Segmentation Computer Vision Dataset
Dataset Characteristics :
Task Type : Texture Segmentation
Image Attribute : 53 outdoor images with objects influenced by shadows
Code/Model/Publication URL : Shadow free segmentation in still images using local density measure
Source URL : Shadow Detection/Texture Segmentation Computer Vision Dataset
Dataset Examples :
Credits : Shadow free segmentation in still images using local density measure
75. CITY-OSM – ETH Zurich Dataset
Dataset Characteristics :
Task Type : Aerial Image Segmentation
Image Attribute : Four large datasets were downloaded from Google Maps and OSM, for the cities of Chicago, Paris, Zurich, and Berlin.Additionally and also downloaded a somewhat smaller dataset for the city of Potsdam.
Code/Model/Publication URL : Learning Aerial Image Segmentation from Online Maps
Source URL : CITY-OSM – ETH Zurich Dataset
Dataset Examples :
Credits : Learning Aerial Image Segmentation from Online Maps
76. The TUD Crossing dataset
Dataset Characteristics :
Task Type : pedestrian instance segmentation
Image Attribute : 201 images with 1008 highly overlapping pedestrians and consisting of 1216 pedestrian instances with densely segmented overlapping pedestrians.
Code/Model/Publication URL : Hough Regions for Joining Instance Localization and Segmentation
Source URL : The TUD Crossing dataset
Dataset Examples :
Credits : Hough Regions for Joining Instance Localization and Segmentation
77. Inria Aerial Image Labeling Dataset
Dataset Characteristics :
Task Type : semantic segmentation on aerial images
Image Attribute : Coverage of 810 km² (405 km² for training and 405 km² for testing) having Ground truth data for two semantic classes: building and not building
Code/Model/Publication URL : CAN SEMANTIC LABELING METHODS GENERALIZE TO ANY CITY?THE INRIA AERIAL IMAGE LABELING BENCHMARK
Source URL : Inria Aerial Image Labeling Dataset
Dataset Examples :
Credits : CAN SEMANTIC LABELING METHODS GENERALIZE TO ANY CITY?THE INRIA AERIAL IMAGE LABELING BENCHMARK
78. Zurich Summer Dataset
Dataset Characteristics :
Task Type : Semantic segmentation of urban scene
Image Attribute : 20 multi-spectral VHR images acquired over the city of Zurich(Switzerland) by the QuickBird satellite in 2002. The average image size is 1000×1150 pixels (approximately 23Mpixels in total)
Categories : 8 different urban and periurban classes : Roads, Buildings, Trees, Grass, Bare Soil, Water, Railways and Swimming pools
Code/Model/Publication URL : Semantic segmentation of urban scenes by learning local class interactions
Source URL : Zurich Summer Dataset
Dataset Examples :
Credits : Semantic segmentation of urban scenes by learning local class interactions
79. Pedestrian Color Naming Dataset
Dataset Characteristics :
Task Type : image segmentation
Image Attribute : 14,213 images, each of which hand-labeled with color label for each pixel
Code/Model/Publication URL : Pedestrian Color Naming via Convolutional Neural Network
Source URL : Pedestrian Color Naming Dataset
Dataset Examples :
Credits : Pedestrian Color Naming via Convolutional Neural Network
80. Human Skin Segmentation Dataset
Dataset Characteristics :
Task Type : human skin segmentation
Image Attribute : 32 face photo, 46 family photo with ground truth labels
Code/Model/Publication URL : A Fusion Approach for Efficient Human Skin Detection
Source URL : Human Skin Segmentation Dataset
Dataset Examples :
Credits : A Fusion Approach for Efficient Human Skin Detection
81. 38-Cloud: A Cloud Segmentation Dataset
Dataset Characteristics :
Task Type : Cloud Segmentation
Image Attribute : 8400 patches for training and 9201 patches for testing extracted from 38 Landsat 8 remote sensing images
Code/Model/Publication URL : CLOUD-NET: AN END-TO-END CLOUD DETECTION ALGORITHM FOR LANDSAT 8 IMAGERY
Source URL : 38-Cloud-A-Cloud-Segmentation-Dataset
Dataset Examples :
Credits : 38-Cloud-A-Cloud-Segmentation-Dataset
82. Multi-Human Parsing (MHP) v2.0 Dataset
Dataset Characteristics :
Task Type : multi-human parsing
Image Attribute : 25,403 human images with pixel-wise annotations
Categories : 58 semantic categories
cap/hat | helmet |
face | hair |
left-arm | right-arm |
left-hand | right-hand |
protector | bikini/bra |
jacket/windbreaker/hoodie | t-shirt |
polo-shirt | sweater |
singlet | torso-skin |
pants | shorts/swim-shorts |
skirt | stockings |
socks | left-boot |
right-boot | left-shoe |
right-shoe | left-high heel |
right-high heel | left-sandal |
right-sandal | left-leg |
right-leg | left-foot |
right-foot | coat |
dress | robe |
jumpsuit | other-full-body-clothes |
headwear | backpack |
ball | bats |
belt | bottle |
carrybag | cases |
sunglasses | eyewear |
glove | scarf |
umbrella | wallet/purse |
watch | wristband |
tie | other-accessory |
other-upper-body-clothes | other-lower-body-clothes |
Code/Model/Publication URL : Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing
Source URL : Multi-Human Parsing (MHP) v2.0 Dataset
Dataset Examples :
Credits : Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing
83. MSRA10K Salient Object Database
Dataset Characteristics :
Task Type : salient object detection and segmentation
Image Attribute : 10,000 images with pixel-level saliency labeling + Pixel accurate salient object labeling for 5000 images from MSRA-B dataset
Code/Model/Publication URL : Global Contrast based Salient Region Detection
Source URL : MSRA10K Salient Object Database
Dataset Examples :
Credits : MSRA10K Salient Object Database
84. Stanford background dataset
Dataset Characteristics :
Task Type : geometric and semantic scene understanding.
Image Attribute : 715 images have approximately 320-by-240 pixels.
Categories :
- sky
- tree
- road
- grass
- water
- bldg
- mntn
- fg obj.
- horz.
- vert.
Code/Model/Publication URL : Decomposing a Scene into Geometric and Semantically Consistent Regions
Source URL : Stanford background dataset
Dataset Examples :
Credits : Decomposing a Scene into Geometric and Semantically Consistent Regions
85. Oakland 3-D Point Cloud Dataset
Dataset Characteristics :
Task Type : semantic segmentation
Image Attribute : 17 files, 1.6 millions 3-D pts, 44 labels
Code/Model/Publication URL : Onboard Contextual Classification of 3-D Point Clouds with Learned High-order Markov Random Fields
Source URL : Oakland 3-D Point Cloud Dataset
Dataset Examples :
Credits : Onboard Contextual Classification of 3-D Point Clouds with Learned High-order Markov Random Fields
86. Penn-Fudan Database for Pedestrian Detection and Segmentation
Dataset Characteristics :
Task Type : pedestrian segmentation
Image Attribute : 170 images with 345 labeled pedestrians, among which 96 images are taken from around University of Pennsylvania, and other 74 are taken from around Fudan University
Code/Model/Publication URL : Object Detection Combining Recognition and Segmentation
Source URL : Penn-Fudan Database for Pedestrian Detection and Segmentation
Dataset Examples :
Credits : Penn-Fudan Database for Pedestrian Detection and Segmentation
87. HOPKINS 155 DATASET
Dataset Characteristics :
Task Type : motion segmentation
Image Attribute : 155 motion sequences of checkerboard, traffic, and articulated scenes
Code/Model/Publication URL : A Benchmark for the Comparison of 3-D Motion Segmentation Algorithms
Source URL : HOPKINS 155 DATASET
Dataset Examples :
Credits : HOPKINS 155 DATASET
88. Segmentation evaluation database
Dataset Characteristics :
Task Type : image segmentation
Image Attribute : 200 gray level images along with ground truth segmentations
Code/Model/Publication URL : Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration
Source URL : Segmentation evaluation database
Dataset Examples :
Credits : Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration
89. CO-SKEL dataset
Dataset Characteristics :
Task Type : co-skeletonization, Co-segmentation
Image Attribute : It consists of 26 categories with total 353 images of animals,birds, flowers and humans
bear | iris |
camel | cat |
cheetah | cormorant |
cow | cranesbill |
deer | desertrose |
dog | egret |
firepink | frog |
geranium | horse |
man | ostrich |
panda | pigeon |
seagull | seastar |
sheep | snowowl |
statue | woman |
Code/Model/Publication URL : Object Co-skeletonization with Co-segmentation
Source URL : CO-SKEL dataset
Dataset Examples :
Credits : Object Co-skeletonization with Co-segmentation
90. EV-IMO dataset
Dataset Characteristics :
Task Type : indoor motion segmentation
Video Attribute :
- Total recording time ~30 minutes
- Up to 3 independently moving objects objects
- Multiple types of scenes (varying backgrounds and motion speeds)
Code/Model/Publication URL : EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras
Source URL : EVIMO dataset
Dataset Examples :
Credits : EVIMO dataset
91. Materials in Context Database (MINC) dataset
Dataset Characteristics :
Task Type : material segmentation of images in the wild.
Image Attribute : It consists of 3M labeled point samples and 7061 labeled material segmentations in 23 material categories.
Brick | Carpet |
Ceramic | Fabric |
Foliage | Food |
Glass | Hair |
Leather | Mirror |
Metal | Other |
Painted | Paper |
Plastic | Skin |
Pol. stone | Sky |
Stone | Tile |
Wallpaper | Wood |
Water | |
Code/Model/Publication URL : Material Recognition in the Wild with the Materials in Context Database
Source URL : MINC dataset
Dataset Examples :
Credits : Material Recognition in the Wild with the Materials in Context Database
92. Liver Tumor Segmentation (LiTS) dataset
Dataset Characteristics :
Task Type : Liver Tumor Segmentation
Image Attribute : the training data set contains 130 CT scans and the test data set 70 CT scans
Code/Model/Publication URL : The Liver Tumor Segmentation Benchmark (LiTS)
Source URL : (LiTS) dataset
Dataset Examples :
Credits : The Liver Tumor Segmentation Benchmark (LiTS)
93. Egocentric Dataset of the University of Barcelona – Segmentation (EDUB-Seg) dataset
Dataset Characteristics :
Task Type : egocentric event segmentation
Image Attribute : a total of 18,735 images captured by 7 different users during overall 20 days.
Code/Model/Publication URL : R-Clustering for Egocentric Video Segmentation
Source URL : EDUB-Seg dataset
Dataset Examples :
Credits : R-Clustering for Egocentric Video Segmentation
94. Audio Visual Cues Dataset
Dataset Characteristics :
Task Type : semantic segmentation from audio-visual clues
Image Attribute : 9 long reconstruction sequences, containing on average 1600 individual 640×480 RGB-D frames. 600 sounds from 50 different objects and 9 material categories were collected.
Code/Model/Publication URL : Joint Object-Material Category Segmentation from Audio-Visual Cues
Source URL : Audio Visual Cues Dataset
Dataset Examples :
Credits : Joint Object-Material Category Segmentation from Audio-Visual Cues
95. OpenSurfaces Dataset
Dataset Characteristics :
Task Type : surfaces segmentation from consumer photographs of indoor scene
Image Attribute : 25K images filtered then 58,928 surfaces annotated with a material name, and 33,378 annotated with object names.
Code/Model/Publication URL : OPENSURFACES: A Richly Annotated Catalog of Surface Appearance
Source URL : OpenSurfaces Dataset
Dataset Examples :
Credits : OPENSURFACES: A Richly Annotated Catalog of Surface Appearance
96. Multi-species fruit flower detection
Dataset Characteristics :
Task Type : semantic segmentation
Image Attribute : 190 images shared among three different species: apple, peach, and pear
Code/Model/Publication URL : Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network
Source URL : Data from: Multi-species fruit flower detection using a refined semantic segmentation network
Dataset Examples :
Credits : Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network
97. SAIL-VOS
Dataset Characteristics :
Task Type : video object segmentation
Image Attribute : 201 video sequences and 111,654 frames consisting of 1,388,389 objects
Code/Model/Publication URL : SAIL-VOS: Semantic Amodal Instance Level Video Object Segmentation –A Synthetic Dataset and Baselines
Source URL : SAIL-VOS
Dataset Examples :
Credits : SAIL-VOS: Semantic Amodal Instance Level Video Object Segmentation –A Synthetic Dataset and Baselines
98. TB-roses-v1 & v2 dataset
Dataset Characteristics :
Task Type : rose stem segmentation
Image Attribute : 354 images of rose bushes
Code/Model/Publication URL : Brain-inspired robust delineation operator
Source URL : TB-roses-v1 & v2 dataset
Dataset Examples :
Credits : TB-roses-v1 & v2 dataset
99. IOSTAR Retinal Vessel Segmentation Dataset
Dataset Characteristics :
Task Type : Vessel Segmentation
Image Attribute : 30 images with a resolution of 1024×1024 pixels
Code/Model/Publication URL : Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores
Source URL : IOSTAR Retinal Vessel Segmentation Dataset
*require institutional login
Dataset Examples :
Credits : Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores
100. PartNet Dataset
Dataset Characteristics :
Task Type : fine-grained semantic segmentation, hierarchical semantic segmentation, and instance segmentation
Image Attribute : 573,585 part instances over 26,671 3D models covering 24 object categories.
24 object categories in PartNet
Code/Model/Publication URL : PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding
Source URL : PartNet Dataset
Dataset Examples :
Credits : PartNet Dataset
101. Synthinel-1 dataset
Dataset Characteristics :
Task Type : Building footprint segmentation
Image Attribute : 2,108 synthetic images with Each synthetic image is 572×572 pixels in size, with a resolution 0.3m/pixel
The nine different virtual city styles used are.
(a) Red roof style
(b) Paris’ buildings style
(c) ancient building style
(d) sci-fi city style
(e) Chinese palace style
(f) Damaged city style
(g) Austin city style
(h) Venice style
(i) modern city style
Code/Model/Publication URL : The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation
Source URL : Synthinel-1 dataset
Dataset Examples :
Credits: Synthinel-1 dataset
Do you have a custom dataset with several object classes for which you desire pixel-perfect annotations?
Well, you have reached the right place, Request a demo in order to know how NeuralMarker can help you in creating your next pixel-perfect quality training data.
Consequently, hop on to here to know which AI Tools NeuralMarker An-End-to-End AI Annotation Platform uses to segment pixels over the images.