The photo transfer app market, valued at $328 million in 2025, is poised for substantial growth, driven by the increasing use of smartphones and cloud storage services. The market's Compound Annual Growth Rate (CAGR) of 4.6% from 2019-2033 reflects a steady demand for efficient and convenient methods of transferring photos across devices. Key drivers include the proliferation of high-resolution cameras in mobile devices, the need for seamless backups, and the growing popularity of cloud-based photo storage solutions. The market is segmented by application (personal and enterprise use) and device type (Android and iOS). While personal use currently dominates, enterprise applications are emerging, driven by the needs of businesses to manage and share large photo libraries efficiently. The rising popularity of cross-platform compatibility, enhanced security features, and automated backup solutions are shaping current market trends. Conversely, concerns about data privacy and security, along with the complexity of some solutions, pose challenges to market growth. Competition is fierce, with established players like iCloud and Dropbox alongside niche players offering specialized features. Regional growth is expected to be strongest in North America and Asia-Pacific, reflecting high smartphone penetration and increasing internet connectivity in these regions. The forecast period (2025-2033) anticipates continued market expansion fueled by technological advancements and the expanding user base. The increasing adoption of artificial intelligence (AI) for automated photo organization and management is anticipated to enhance user experience and drive market growth further. Furthermore, the integration of photo transfer apps with other productivity and collaboration tools will enhance their appeal, particularly in the enterprise sector. The continuous improvement of network infrastructure and the expansion of 5G connectivity will provide opportunities for faster and more reliable photo transfers, fostering market expansion. However, potential regulatory changes concerning data privacy could impact future growth and necessitate enhanced security measures from app developers. The market will likely see increased consolidation, with larger players acquiring smaller companies to expand their market share and service offerings.

Curated image privacy data sets

In the article, we trained and evaluated models on the Image Privacy Dataset (IPD) and the PrivacyAlert dataset. The datasets are originally provided by other sources and have been re-organised and curated for this work.

Our curation organises the datasets in a common structure. We updated the annotations and labelled the splits of the data in the annotation file. This avoids having separated folders of images for each data split (training, validation, testing) and allows a flexible handling of new splits, e.g. created with a stratified K-Fold cross-validation procedure. As for the original datasets (PicAlert and PrivacyAlert), we provide the link to the images in bash scripts to download the images. Another bash script re-organises the images in sub-folders with maximum 1000 images in each folder.

Both datasets refer to images publicly available on Flickr. These images have a large variety of content, including sensitive content, seminude people, vehicle plates, documents, private events. Images were annotated with a binary label denoting if the content was deemed to be public or private. As the images are publicly available, their label is mostly public. These datasets have therefore a high imbalance towards the public class. Note that IPD combines two other existing datasets, PicAlert and part of VISPR, to increase the number of private images already limited in PicAlert. Further details in our corresponding https://doi.org/10.48550/arXiv.2503.12464" target="_blank" rel="noopener">publication.

List of datasets and their original source:

• PicAlert [Images occupy 2.4 GB]
• VISPR [Images occupy 49.7 GB]
• PrivacyAlert [Images occupy 1 GB]

Notes:

• For PicAlert and PrivacyAlert, only urls to the original locations in Flickr are available in the Zenodo record
• Collector and authors of the PrivacyAlert dataset selected the images from Flickr under Public Domain license
• Owners of the photos on Flick could have removed the photos from the social media platform
• Running the bash scripts to download the images can incur in the "429 Too Many Requests" status code

Pre-computed visual entitities

Some of the models run their pipeline end-to-end with the images as input, whereas other models require different or additional inputs. These inputs include the pre-computed visual entities (scene types and object types) represented in a graph format, e.g. for a Graph Neural Network. Re-using these pre-computed visual entities allows other researcher to build new models based on these features while avoiding re-computing the same on their own or for each epoch during the training of a model (faster training).

For each image of each dataset, namely PrivacyAlert, PicAlert, and VISPR, we provide the predicted scene probabilities as a .csv file , the detected objects as a .json file in COCO data format, and the node features (visual entities already organised in graph format with their features) as a .json file. For consistency, all the files are already organised in batches following the structure of the images in the datasets folder. For each dataset, we also provide the pre-computed adjacency matrix for the graph data.

Note: IPD is based on PicAlert and VISPR and therefore IPD refers to the scene probabilities and object detections of the other two datasets. Both PicAlert and VISPR must be downloaded and prepared to use IPD for training and testing.

Further details on downloading and organising data can be found in our GitHub repository: https://github.com/graphnex/privacy-from-visual-entities (see ARTIFACT-EVALUATION.md#pre-computed-visual-entitities-)

Enquiries, questions and comments

If you have any enquiries, question, or comments, or you would like to file a bug report or a feature request, use the issue tracker of our GitHub repository.

Graph and download economic data for State Tax Collections: T51 Documentary and Stock Transfer Taxes for the United States (QTAXT51QTAXCAT3USNO) from Q1 1994 to Q1 2025 about transfers, collection, stocks, tax, and USA.

The figures and tables of paper "A deep map transfer learning method for face recognition in an unrestricted smart city environment"

Graph and download economic data for Government current transfer payments (A084RC1Q027SBEA) from Q1 1947 to Q2 2025 about transfers, payments, government, GDP, and USA.

Given the difficulty to handle planetary data we provide downloadable files in PNG format from the missions Chang'E-3 and Chang'E-4. In addition to a set of scripts to do the conversion given a different PDS4 Dataset.

Graph and download economic data for National income: Business current transfer payments (net): to government (net) (W061RC1Q027SBEA) from Q1 1947 to Q2 2025 about national income, transfers, payments, Net, business, government, income, GDP, and USA.

PSRGAN DatasetsPlease check this link for more information.https://hyongsong.work/

United States - Personal current transfer payments was 282.72800 Bil. of $ in April of 2025, according to the United States Federal Reserve. Historically, United States - Personal current transfer payments reached a record high of 282.72800 in April of 2025 and a record low of 0.71300 in April of 1951. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Personal current transfer payments - last updated from the United States Federal Reserve on August of 2025.

The images are in the ImageNet structure, with each class having its own folder containing the respective images. The images have a resolution of 256x256 pixels.

Dataset Details:

• Total number of classes: 14
• Total number of images: 3279
• Resolution: 256x256 pixels
• Image format: JPG

If you find this dataset useful or interesting, please don't forget to show your support by Upvoting! 🙌👍

Data Collection Methodology:

To create this dataset, - I searched for each PC part on Google Images and extracted the image links. - I then downloaded the full-size images from the original source and converted them to JPG format with a resolution of 256 pixels. - During the process, most images were downscaled, with only a very few being upscaled. - Finally, I manually went over all the images and deleted any that didn't fit well for image classification.

Potential Task Ideas:

1. Train an image classification model using popular architectures like ViT, ResNet, or EfficientNet.
2. Perform transfer learning on this dataset using pre-trained models.
3. Explore different data augmentation techniques to enhance model performance.
4. Fine-tune existing models to improve classification accuracy.
5. Compare the performance of different models on this dataset.
6. Use the dataset as a benchmark for evaluating new image classification techniques.

Class Naming Convention:

All files are named in ImageNet style. ```shell Kingdom ├── class_1 │ ├── 1.jpg │ └── 2.jpg ├── class_2 │ ├── 1.jpg │ └── 2.jpg └── class_3 ├── 1.jpg └── 2.jpg

**I have not divided the dataset into train,val,test so that you can decide on the split ratios.**

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Photo by <a href="https://unsplash.com/@zelebb?utm_content=creditCopyText&utm_medium=referral&utm_source=unsplash">Andrey Matveev</a> on <a href="https://unsplash.com/photos/a-close-up-of-two-computer-fans-on-a-yellow-background-8hkotoCEI5o?utm_content=creditCopyText&utm_medium=referral&utm_source=unsplash">Unsplash</a>

United States - Business current transfer payments (net) was 312.13800 Bil. of $ in January of 2025, according to the United States Federal Reserve. Historically, United States - Business current transfer payments (net) reached a record high of 312.13800 in January of 2025 and a record low of 0.52400 in January of 1947. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Business current transfer payments (net) - last updated from the United States Federal Reserve on July of 2025.

Sim-to-Real transfer has been invented and widely used. However

The growing availability of spatial transcriptomics data offers key resources for annotating query datasets using reference datasets. However, batch effects, unbalanced reference annotations, and tissue heterogeneity pose significant challenges to alignment analysis. Here, we present stGuide, an attention-based supervised graph learning model designed for cross-slice alignment and efficient label transfer from reference to query datasets. stGuide leverages supervised representations guided by reference annotations to map query slices into a shared embedding space using an attention-based mechanism. It then assigns spot-level labels by incorporating information from the nearest neighbors in the learned representation. Using human dorsolateral prefrontal cortex and breast cancer datasets, stGuide demonstrates its capabilities by (i) producing category-guided, low-dimensional features with well-mixed slices; (ii) transferring labels effectively across heterogeneous tissues; and (iii) uncovering relationships between clusters. Comparisons with state-of-the-art methods demonstrate that stGuide consistently outperforms existing approaches, positioning it as a robust and versatile tool for spatial transcriptomics analysis.

This dataset contains ether as well as popular ERC20 token transfer transactions extracted from the Ethereum Mainnet blockchain.

Only send ether, contract function call, contract deployment transactions are present in the dataset. Miner reward (static block reward) and "uncle block inclusion reward" are added as transactions to the dataset. Transaction fee reward and "uncles reward" are not currently included in the dataset.

Details of the datasets are given below:

FILENAME FORMAT:

The filenames have the following format:

eth-tx--.txt.bz2

where is the starting block number and final block number.

For example file eth-tx-1000000-1099999.txt.bz2 contains transactions from

block 1000000 to block 1099999 inclusive.

The files are compressed with bzip2. They can be uncompressed using command bunzip2.

TRANSACTION FORMAT:

Each line in a file corresponds to a transaction. The transaction has the following format:

is the abreviation for the asset. For example ETH means ether transfer in Wei 


          units. ERC20 tokens transfers (transfer and transferFrom function calls in ERC20 


          contract) are indicated by token symbol. For example GUSD is Gemini USD stable


          coin. The JSON file erc20tokens.json given below contains the details of ERC20 tokens.


          Failed transactions are prefixed with "F-".


  Number of the block which contains the transaction. 


    Position of the transaction in the block (i.e. transaction number in the block). Static block reward and uncle block inclusion reward has 0 as tx no. The position of transactions in the block is shifted by 1. The first transaction has 1 as tx no.

Source ethereum address of the transfer. For static block rewards, the from addr is ETHMAINBLOCK. For uncle block inclusion rewards, the from addr is ETHMAINUNCLE.

  Destination ethereum address of the transfer. The to addr is 0x0 for contract deployment transactions instead of null.


   Amount of transfer. The amount is given as hexadecimal a number. 

BLOCK TIME FORMAT:

The block time file has the following format:

  Number of the block. 


 Unix timestamp at which the block is mined as a hexadecimal number.

erc20tokens.json FILE:

This file contains the list of popular ERC20 token contracts whose transfer/transferFrom transactions appear in the data files.

ERC20 token list: USDT TRYb XAUt BNB LEO LINK HT HEDG MKR CRO VEN INO PAX INB SNX REP MOF ZRX SXP OKB XIN OMG SAI HOT DAI EURS HPT BUSD USDC SUSD HDG QCAD PLUS BTCB WBTC cWBTC renBTC sBTC imBTC pBTC

IMPORTANT NOTE:

Public Ethereum Mainnet blockchain data is open and can be obtained by connecting as a node on the blockchain or by using the block explorer web sites such as http://etherscan.io . The downloaders and users of this dataset accept the full responsibility of using the data in GDPR compliant manner or any other regulations. We provide the data as is and we cannot be held responsible for anything.

NOTE:

If you use this dataset, please do not forget to add the DOI number to the citation.

If you use our dataset in your research, please also cite our paper: https://link.springer.com/article/10.1007/s10586-021-03511-0

@article{kilic2022parallel, title={Parallel Analysis of Ethereum Blockchain Transaction Data using Cluster Computing}, journal={Cluster Computing}, author={K{\i}l{\i}{\c{c}}, Baran and {"O}zturan, Can and Sen, Alper}, year={2022}, month={Jan} }

Transfer Learning Data Set: The total data set contains 1.44m synthetic images and 10k real-world images of size 299x224.

Due to file size limitation, the data set is split into two sets. This data set (part 0: DOI 10.5281/zenodo.2581311) contains the real-world images and the synthetic images of perspective 00. The second part (part 1: DOI 10.5281/zenodo.2581469) contains perspective 01 and 10 of the synthetic images.

1. 10k real world images the filename labels the image: -first 6 digits represent the id. -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp -10. digit is empty (reserved for 2nd perspective) -12. digit labels the graspbox: 0 -> green box, 1 -> yellow box -14. digit labels the distractors : 0 -> no distractors, 1 -> distractors -c in the end stands for acolor image, d is reserved for depth (not in use).

2. 1.44m synthetic images the folder labels the enabled technique: -first 2 digits label the perspective: 00 -> standard, 01 -> shake, 10 -> random -3. digit labels the graspbox: 0 -> defaul green box, 1 -> random box -4. digit labels the distractors: 0 -> no distractors, 1 -> distractors -5. digit labels the lighting: 0 -> default lighting, 1 -> random lighting -6. digit labels the mesh randomization: 0 -> default mesh color, 1 -> random mesh color

   the filename consists of 3 parts: -first 6 digits represent the id. -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp -10.-15. digit equals the folder name and represents the enabled technique -c in the end stands for a color image, d is reserved for depth (not in use).

Interline Transfer CCD Image Sensors Market Outlook

The global market size for Interline Transfer CCD (Charge-Coupled Device) Image Sensors was valued at approximately USD 1.5 billion in 2023, with expectations to reach USD 2.8 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 7.1% during the forecast period. The substantial growth of this market is attributed to the increasing demand for high-quality imaging in various applications such as consumer electronics, automotive, industrial, medical, and security and surveillance sectors.

One of the primary growth factors driving the Interline Transfer CCD Image Sensors market is the increasing adoption of consumer electronics, particularly smartphones and digital cameras, which require high-resolution image sensors. The demand for high-quality imaging and advanced photographic features in these devices has led to significant advancements in CCD sensor technology. Moreover, the proliferation of social media and the increasing trend of sharing high-quality images and videos are further propelling the demand for advanced imaging sensors.

Another crucial factor contributing to market growth is the expansion of the automotive industry, particularly with the development of advanced driver-assistance systems (ADAS) and autonomous vehicles. These systems rely heavily on high-resolution image sensors to provide accurate and reliable data for navigation and safety. Interline Transfer CCD Image Sensors are known for their high sensitivity and low noise, making them ideal for automotive applications. The growing emphasis on vehicle safety and the integration of sophisticated imaging systems in modern vehicles are expected to drive the market further.

The industrial and medical sectors are also significant contributors to the market's growth. In industrial applications, CCD image sensors are used in machine vision systems for quality control and automated inspection processes. The medical sector utilizes these sensors in various imaging devices, such as endoscopes and diagnostic equipment, where high-resolution and precise imaging are crucial. The increasing demand for automation in manufacturing and the rising need for advanced medical imaging technologies are expected to fuel the growth of the Interline Transfer CCD Image Sensors market.

Regionally, Asia Pacific holds a dominant position in the global market, driven by the presence of major consumer electronics manufacturers and the rapid adoption of advanced automotive technologies in countries like China, Japan, and South Korea. North America and Europe are also significant markets due to the strong presence of automotive and industrial sectors, coupled with high healthcare expenditure. The Middle East & Africa and Latin America are expected to witness moderate growth, driven by increasing investments in industrial automation and healthcare infrastructure.

Sensor Type Analysis

The Interline Transfer CCD Image Sensors market is segmented by sensor type into Full-Frame, Frame-Transfer, and Interline-Transfer sensors. Full-Frame sensors offer the highest image quality and sensitivity by capturing the entire image frame at once, but they are generally more expensive and complex. These sensors are primarily used in high-end imaging applications such as professional photography and scientific research where image quality is paramount. The market for Full-Frame CCD sensors is expected to grow steadily, driven by the increasing demand for high-resolution imaging in professional and scientific applications.

Frame-Transfer sensors, on the other hand, offer a good balance between performance and cost. They transfer the captured image frame to a storage array before the next exposure, which reduces image lag and enhances image quality. These sensors are widely used in applications that require a moderate level of performance at a reasonable cost, such as consumer electronics and some industrial applications. The market segment for Frame-Transfer sensors is anticipated to grow at a moderate pace, supported by the expanding consumer electronics market and increasing industrial automation.

Interline-Transfer sensors are the most commonly used type of CCD sensors due to their ability to rapidly transfer image data from the sensor to the storage array, allowing for faster image capture and reduced motion blur. These sensors are particularly popular in high-speed imaging applications such as video recording, automotive, and security and surveillance systems. The segment for Interline-Transfer sensors is expected to witness sign

United States - Federal government current transfer payments was 1166988.00000 Mil. of $ in April of 2025, according to the United States Federal Reserve. Historically, United States - Federal government current transfer payments reached a record high of 1537438.00000 in April of 2020 and a record low of 2579.00000 in January of 1947. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Federal government current transfer payments - last updated from the United States Federal Reserve on August of 2025.

This dataset contains bitcoin transfer transactions extracted from the Bitcoin Mainnet blockchain. Part2 is available at https://zenodo.org/deposit/7157854 Part3 is available at https://zenodo.org/deposit/7158133 Part4 is available at https://zenodo.org/deposit/7158328 Details of the datasets are given below: FILENAME FORMAT: The filenames have the following format: btc-tx-

The global camera memory card reader market is experiencing robust growth, driven by the increasing adoption of high-resolution cameras and the expanding demand for efficient data transfer solutions. The market size in 2025 is estimated at $500 million, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors, including the proliferation of professional and consumer-grade DSLR and mirrorless cameras, the rising popularity of action cameras and drones, and the increasing need for fast and reliable data transfer for post-processing and storage of high-quality images and videos. The market is segmented by various reader types (USB, SD, CF, etc.), connectivity (USB 3.0, USB-C, Thunderbolt), and application (professional photography, consumer photography, video production). Key trends shaping the market include the increasing demand for high-speed readers compatible with newer memory card formats like CFexpress and SDXC, the growing adoption of cloud storage solutions integrated with readers, and the miniaturization of reader devices for portability. However, restraints on market growth include the fluctuating prices of memory cards and readers, technological limitations in data transfer speed, and the potential for counterfeit products. Major players like SanDisk, Sony, Lexar, Kingston, and others are driving innovation in this space through product diversification and strategic partnerships. The forecast period (2025-2033) promises substantial growth opportunities, particularly in developing economies with rising disposable incomes and expanding digital photography adoption. The professional photography segment is expected to witness significant growth, driven by the demand for high-speed and reliable data transfer solutions for efficient workflow.

The Thunderbolt 4 cable market, while a niche segment within the broader data cable industry, is experiencing robust growth fueled by the increasing demand for high-bandwidth data transfer capabilities. The market's expansion is driven by the proliferation of devices supporting Thunderbolt 4 technology, including high-performance laptops, external storage solutions (SSDs and NVMe drives), and docking stations. The adoption of Thunderbolt 4 is particularly strong in professional settings, such as video editing, graphic design, and data-intensive scientific research, where rapid data transfer speeds are critical. The market is segmented by application (PC, Phone – with PC currently dominating), cable type (primarily focusing on 6-foot lengths, although longer lengths are emerging), and geography. North America and Europe currently hold the largest market shares due to higher technology adoption rates and a strong presence of key players like Apple, Belkin, and HP. However, growth in Asia Pacific is expected to accelerate, driven by increasing disposable incomes and rising demand for high-performance computing devices in regions like China and India. While pricing remains a potential restraint for wider adoption, the long-term value proposition of Thunderbolt 4's superior speed and capabilities outweighs this factor for many professionals and high-end consumers. Competition is intensifying, with both established players and new entrants vying for market share through product innovation, improved pricing strategies, and enhanced marketing efforts. Continued growth in the Thunderbolt 4 cable market is anticipated through 2033. This projection considers the ongoing development of devices compatible with Thunderbolt 4, the increasing demand for faster data transfer rates across various industries, and the expanding ecosystem of peripherals that leverage this technology. The market will likely see further segmentation based on cable length, material (e.g., improved durability and shielding), and specific features tailored to particular applications. Furthermore, ongoing technological advancements may lead to the introduction of Thunderbolt 5 and beyond, potentially impacting the long-term trajectory of the Thunderbolt 4 cable market. However, for the foreseeable future, the demand for reliable and high-performance Thunderbolt 4 cables is expected to remain strong, driven by its consistent advantages in data transfer speeds and capabilities compared to other connection types.