The DAVIS Challenge 2017 benchmark is a dataset for video object segmentation.

The DAVIS 2017 video object segmentation dataset.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('davis', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

Contains the semantic masks for all the publicly available frames in the Semi-supervised sets, a JSON file with the category for each object and another JSON file with the id and the super category for each category. Contains the three human annotated scribbles for each of the objects in the TrainVal Semi-supervised set.

The 2017 Davis Challenge on Video Object Segmentation is a benchmark for evaluating video object segmentation models.

Our task is to localize and provide a pixel-level mask of an object on all video frames given a language referring expression obtained either by looking at the first frame only or the full video. To validate our approach we employ two popular video object segmentation datasets, DAVIS16 [38] and DAVIS17 [42]. These two datasets introduce various challenges, containing videos with single or multiple salient objects, crowded scenes, similar looking instances, occlusions, camera view changes, fast motion, etc. DAVIS16 [38] consists of 30 training and 20 test videos of diverse object categories with all frames annotated with pixel-level accuracy. Note that in this dataset only a single object is annotated per video. For the multiple object video segmentation task we consider DAVIS17. Compared to DAVIS16, this is a more challenging dataset, with multiple objects annotated per video and more complex scenes with more distractors, occlusions, smaller objects, and fine structures. Overall, DAVIS17 consists of a training set with 60 videos, and a validation/test-dev/test-challenge set with 30 sequences each. As our goal is to segment objects in videos using language specifications, we augment all objects annotated with mask labels in DAVIS16 and DAVIS17 with non-ambiguous referring expressions. We follow the work of [34] and ask the annotator to provide a language description of the object, which has a mask annotation, by looking only at the first frame of the video. Then another annotator is given the first frame and the corresponding description, and asked to identify the referred object. If the annotator is unable to correctly identify the object, the description is corrected to remove ambiguity and to specify the object uniquely. We have collected two referring expressions per target object annotated by non-computer vision experts (Annotator 1, 2). However, by looking only at the 1st frame, the obtained referring expressions may potentially be invalid for an entire video. (We actually quantified that only∼ 15% of the collected descriptions become invalid over time and it does not affect strongly segmentation results as temporal consistency step helps to disambiguate some of such cases, see the supp. material for details.) Besides, in many applications, such as video editing or video-based advertisement, the user has access to a full video. Providing a language query which is valid for all frames might decrease the editing time and result in more coherent predictions. Thus, on DAVIS17 we asked the workers to provide a description of the object by looking at the full video. We have collected one expression of the full video type per target object. Future work may choose to use either setting. The average length for the first frame/full video expressions is 5.5/6.3 words. For DAVIS17 first frame annotations we notice that descriptions given by Annotator 1 are longer than the ones by Annotator 2 (6.4 vs. 4.6 words). We evaluate the effect of description length on the grounding performance in §5. Besides, the expressions relevant to a full video mention verbs more often than the first frame descriptions (44% vs. 25%). This is intuitive, as referring to an object which changes its appearance and position over time may require mentioning its actions. Adjectives are present in over 50% for all annotations. Most of them refer to colors (over 70%), shapes and sizes (7%) and spatial/ordering words (6% first frame vs. 13% full video expressions). The full video expressions also have a higher number of adverbs and prepositions, and overall are more complex than the ones provided for the first frame. Overall augmented DAVIS16/17 contains ∼ 1.2k referring expressions for more than 400 objects on 150 videos with ∼ 10k frames. We believe the collected data will be of interest to segmentation as well as vision and language communities, providing an opportunity to explore language as alternative input for video object segmentation.

This paper proposes the Davis 2017 validation set for video object segmentation.

This dataset tracks annual overall school rank from 2010 to 2017 for T.r. Davis Elementary School

The DAVIS-2017 dataset is a benchmark for video object segmentation

About this dataset

Context

The dataset tabulates the Davis population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Davis. The dataset can be utilized to understand the population distribution of Davis by age. For example, using this dataset, we can identify the largest age group in Davis.

Key observations

The largest age group in Davis, CA was for the group of age 20-24 years with a population of 17,266 (25.69%), according to the 2021 American Community Survey. At the same time, the smallest age group in Davis, CA was the 80-84 years with a population of 1,044 (1.55%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Davis is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Davis total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Davis Population by Age. You can refer the same here

This dataset tracks annual free lunch eligibility from 2017 to 2023 for Davis Elementary School vs. Texas and Austin Independent School District

Explore detailed 2017 Nissan import data of Christal Davis in the USA—product details, price, quantity, origin countries, and US ports.

Geoscience Australia conducted a hydrographic and seafloor characterisation survey nearshore from Davis Station in the Australian Antarctic Territory. The multibeam bathymetry data was acquired …Show full descriptionGeoscience Australia conducted a hydrographic and seafloor characterisation survey nearshore from Davis Station in the Australian Antarctic Territory. The multibeam bathymetry data was acquired during January-February 2017 from the AAD workboat Howard Burton in the Vestfold Hills region. This survey is a component of the Australian Antarctic Program (AAP) and Project 5093 Hydrographic Surveying and Seafloor Characterisation Program (Chief Investigator: Ursula Harris, AAD). The objective of the survey was to map the seabed environment in shallow (<300 m) coastal waters for the compilation of nautical charts and acquire baseline data for environmental management, science, infrastructure and logistical operations. Data collected during the survey includes high-resolution multibeam bathymetry, backscatter, sediment samples, seafloor imagery and sub-bottom profiles. This dataset is a 32bit geotiff at 11 m resolution using EGM2008 vertical datum and EPSG:4326 coordinate system and produced from the processed EM3002D/ EM2040C Dual system bathymetry data. This dataset is published with the permission of the CEO, Geoscience Australia. Not to be used for navigational purposes.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Address Range / Feature Name Relationship File (ADDRFN.dbf) contains a record for each address range / linear feature name relationship. The purpose of this relationship file is to identify all street names associated with each address range. An edge can have several feature names; an address range located on an edge can be associated with one or any combination of the available feature names (an address range can be linked to multiple feature names). The address range is identified by the address range identifier (ARID) attribute that can be used to link to the Address Ranges Relationship File (ADDR.dbf). The linear feature name is identified by the linear feature identifier (LINEARID) attribute that can be used to link to the Feature Names Relationship File (FEATNAMES.dbf).

Graph and download economic data for Market Hotness: Nielsen Household Rank in Davis County, UT (NIHHRACOUNTY49011) from Aug 2017 to Jul 2024 about Davis County, UT; nielsen; Ogden; rank; UT; households; and USA.

Over time there have been a number of tide gauges deployed at Davis Station, Antarctica. The data download files contain further information about the gauges, but some of the information has been summarised here. Note that this metadata record only describes tide gauge data from 1993 to 2017. More recent data are described elsewhere.

Tide Gauge 3 (TG003)
This folder contains the following folders:-
early_tg_files

monthly_tg_files
monthly download files from the submerged tide gauge at Davis deployed in March 1993.
These files are ASCII hexadecimal files. They need to be converted to decimal.
The resultant values are absolute seawater pressures in mbar.

Remaining files are downloaded in normal format obtained directly from tide gauge.

raw
memory images from submerged tide gauge. file extension is memory bank number.
These files are processed by a utility called tgxtract.exe which creates files in same format as those in old_tidedata folder.
These file have extension .srt. They are then converted to decimal pressure values.

output
output file (.srt) which have been sent to BoM.

Tide Gauge 6 (TG006)
This folder contains the following folders:-

raw
memory images from submerged tide gauge. file extension is memory bank number.
These files are processed by a utility called tgxtract.exe which creates files in same format as original download format.
These file have extension .srt.
These files are ASCII hexadecimal files. They need to be converted to decimal.
The resultant values are absolute seawater pressures in mbar.


output
output file (.srt) which have been sent to BoM.

Tide Gauge 12 (TG012) and Tide Gauge 12i (TG012i)

Documentation notes from the older metadata records:
Documentation dated 2001-03-07
Davis Submerged Tide Gauge

The gauge used at Davis was designed in 1991/2 by Platypus Engineering, Hobart, Tasmania . It was intended to be submerged in about 7 metres of water in a purpose made concrete mooring in the shape of a truncated pyramid. The gauge measures pressure using a Paroscientific Digiquartz Pressure Transducer with a full scale pressure of 30 psi absolute. The accuracy of the transducer is 1 in 10,000 of full scale over the calibrated temperature. The overall accuracy of the system is better than +/- 3 mm for a known water density. Data is retrieved from the gauges by lowering a coil assembly on the end of a cable over a projecting knob on the top of the gauge and by use of an interface unit, a serial connection can be established to the gauge. Time setting and data retrieval can be then achieved. One of these of these gauges was deployed at Davis in early 1993 in a mooring in ???? bay. Data has been retrieved from these gauges irregularly since then. The records are complete since deployment except for a few days in late 1995. The loss was caused by a fault in the software which allows directory entries to overwrites data when the directory memory has been filled. Conversion of raw data to tidal records is done as detailed in document DATAFORMAT1.DOC . As the current gauge is expected to require a new battery soon, a new mooring has been placed close to the original. A new gauge is at Davis ready to be deployed as time permits.
Levelling

Levelling of the gauge at Davis was done by installing a temporary pressure type gauge in shallow water and recording sealevel for 10 days. The temporary gauge was precisely levelled to a permanent benchmark. The temporary gauge was then calibrated using a known height of seawater from the bay at the same temperature as the water in the bay. The density of the seawater was accurately measured. This work, in conjunction with the tidal records from the submerged gauge have enabled a MSL for Davis to be established. Permanent Tide Gauge. No suitable sites for an Aquatrak type gauge at Davis have been identified.

Documentation dated 2008-10-17
There are two submerged tide gauges at Davis. One is soon to be removed to have its battery replaced.
These gauges record pressure and temperature values. The download software only formats these records to produce 10 minute average pressure values (hPa) and unscaled temperature values.

Visible light orthomosaics of the study reaches in Red Canyon Creek were generated from UAV images and Structure from Motion photogrammetry. The orthomosaics show the study areas one year prior to beaver dam analogue (BDA) installation (2017), immediately after BDA installation (2018), and one year after the BDAs had been constructed (2019). These data are used in Davis et al. (2021), Evaluating the geomorphic channels response to beaver dam analog installation using unoccupied aerial vehicles (https://doi.org/10.1002/esp.5180). The rest of the data used in Davis et al. (2021) is available on CUAHSI HydroShare (http://www.hydroshare.org/resource/7c4ae80863174acfb571ccf8bdac8968).

This dataset tracks annual reduced-price lunch eligibility from 2017 to 2023 for Davis Elementary School vs. Texas and Austin Independent School District

Graph and download economic data for Housing Inventory: Active Listing Count Month-Over-Month in Davis County, UT (ACTLISCOUMM49011) from Jul 2017 to Aug 2025 about Davis County, UT; Ogden; UT; active listing; listing; and USA.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Edge refers to the linear topological primitives that make up MTDB. The All Lines Shapefile contains linear features such as roads, railroads, and hydrography. Additional attribute data associated with the linear features found in the All Lines Shapefile are available in relationship (.dbf) files that users must download separately. The All Lines Shapefile contains the geometry and attributes of each topological primitive edge. Each edge has a unique TIGER/Line identifier (TLID) value.

Graph and download economic data for Housing Inventory: Median Days on Market Year-Over-Year in Davis County, UT (MEDDAYONMARYY49011) from Jul 2017 to Aug 2025 about Davis County, UT; Ogden; UT; median; and USA.