Machine learning‐based behaviour classification using acceleration data is a powerful tool in bio‐logging research. Deep learning architectures such as convolutional neural networks (CNN), long short‐term memory (LSTM) and self‐attention mechanisms as well as related training techniques have been extensively studied in human activity recognition. However, they have rarely been used in wild animal studies. The main challenges of acceleration‐based wild animal behaviour classification include data shortages, class imbalance problems, various types of noise in data due to differences in individual behaviour and where the loggers were attached and complexity in data due to complex animal‐specific behaviours, which may have limited the application of deep learning techniques in this area. To overcome these challenges, we explored the effectiveness of techniques for efficient model training: data augmentation, manifold mixup and pre‐training of deep learning models with unlabelled data, using datasets from two species of wild seabirds and state‐of‐the‐art deep learning model architectures. Data augmentation improved the overall model performance when one of the various techniques (none, scaling, jittering, permutation, time‐warping and rotation) was randomly applied to each data during mini‐batch training. Manifold mixup also improved model performance, but not as much as random data augmentation. Pre‐training with unlabelled data did not improve model performance. The state‐of‐the‐art deep learning models, including a model consisting of four CNN layers, an LSTM layer and a multi‐head attention layer, as well as its modified version with shortcut connection, showed better performance among other comparative models. Using only raw acceleration data as inputs, these models outperformed classic machine learning approaches that used 119 handcrafted features. Our experiments showed that deep learning techniques are promising for acceleration‐based behaviour classification of wild animals and highlighted some challenges (e.g. effective use of unlabelled data). There is scope for greater exploration of deep learning techniques in wild animal studies (e.g. advanced data augmentation, multimodal sensor data use, transfer learning and self‐supervised learning). We hope that this study will stimulate the development of deep learning techniques for wild animal behaviour classification using time‐series sensor data.

This abstract is cited from the original article "Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers" in Methods in Ecology and Evolution (Otsuka et al., 2024).Please see README for the details of the datasets.

If you use this dataset, please cite this paper: Puertas, E.; De-Las-Heras, G.; Sánchez-Soriano, J.; Fernández-Andrés, J. Dataset: Variable Message Signal Annotated Images for Object Detection. Data 2022, 7, 41. https://doi.org/10.3390/data7040041

This dataset consists of Spanish road images taken from inside a vehicle, as well as annotations in XML files in PASCAL VOC format that indicate the location of Variable Message Signals within them. Also, a CSV file is attached with information regarding the geographic position, the folder where the image is located, and the text in Spanish. This can be used to train supervised learning computer vision algorithms, such as convolutional neural networks. Throughout this work, the process followed to obtain the dataset, image acquisition, and labeling, and its specifications are detailed. The dataset is constituted of 1216 instances, 888 positives, and 328 negatives, in 1152 jpg images with a resolution of 1280x720 pixels. These are divided into 576 real images and 576 images created from the data-augmentation technique. The purpose of this dataset is to help in road computer vision research since there is not one specifically for VMSs.

The folder structure of the dataset is as follows:

• vms_dataset/
  • data.csv
  • real_images/
    • imgs/
    • annotations/
  • data-augmentation/
    • imgs/
    • annotations/

In which:

• data.csv: Each row contains the following information separated by commas (,): image_name, x_min, y_min, x_max, y_max, class_name, lat, long, folder, text.
• real_images: Images extracted directly from the videos.
• data-augmentation: Images created using data-augmentation
• imgs: Image files in .jpg format.
• annotations: Annotation files in .xml format.

The goal of this work is to generate large statistically representative datasets to train machine learning models for disruption prediction provided by data from few existing discharges. Such a comprehensive training database is important to achieve satisfying and reliable prediction results in artificial neural network classifiers. Here, we aim for a robust augmentation of the training database for multivariate time series data using Student-t process regression. We apply Student-t process regression in a state space formulation via Bayesian filtering to tackle challenges imposed by outliers and noise in the training data set and to reduce the computational complexity. Thus, the method can also be used if the time resolution is high. We use an uncorrelated model for each dimension and impose correlations afterwards via coloring transformations. We demonstrate the efficacy of our approach on plasma diagnostics data of three different disruption classes from the DIII-D tokamak. To evaluate if the distribution of the generated data is similar to the training data, we additionally perform statistical analyses using methods from time series analysis, descriptive statistics, and classic machine learning clustering algorithms.

PurposeIn the context of lung cancer screening, the scarcity of well-labeled medical images poses a significant challenge to implement supervised learning-based deep learning methods. While data augmentation is an effective technique for countering the difficulties caused by insufficient data, it has not been fully explored in the context of lung cancer screening. In this research study, we analyzed the state-of-the-art (SOTA) data augmentation techniques for lung cancer binary prediction.MethodsTo comprehensively evaluate the efficiency of data augmentation approaches, we considered the nested case control National Lung Screening Trial (NLST) cohort comprising of 253 individuals who had the commonly used CT scans without contrast. The CT scans were pre-processed into three-dimensional volumes based on the lung nodule annotations. Subsequently, we evaluated five basic (online) and two generative model-based offline data augmentation methods with ten state-of-the-art (SOTA) 3D deep learning-based lung cancer prediction models.ResultsOur results demonstrated that the performance improvement by data augmentation was highly dependent on approach used. The Cutmix method resulted in the highest average performance improvement across all three metrics: 1.07%, 3.29%, 1.19% for accuracy, F1 score and AUC, respectively. MobileNetV2 with a simple data augmentation approach achieved the best AUC of 0.8719 among all lung cancer predictors, demonstrating a 7.62% improvement compared to baseline. Furthermore, the MED-DDPM data augmentation approach was able to improve prediction performance by rebalancing the training set and adding moderately synthetic data.ConclusionsThe effectiveness of online and offline data augmentation methods were highly sensitive to the prediction model, highlighting the importance of carefully selecting the optimal data augmentation method. Our findings suggest that certain traditional methods can provide more stable and higher performance compared to SOTA online data augmentation approaches. Overall, these results offer meaningful insights for the development and clinical integration of data augmented deep learning tools for lung cancer screening.

Data Augmentation Tools Market Outlook

According to our latest research, the global Data Augmentation Tools market size reached USD 1.62 billion in 2024, with a robust year-on-year growth trajectory. The market is poised for accelerated expansion, projected to achieve a CAGR of 26.4% from 2025 to 2033. By the end of 2033, the market is forecasted to reach approximately USD 12.34 billion. This dynamic growth is primarily driven by the rising demand for artificial intelligence (AI) and machine learning (ML) applications across diverse industry verticals, which necessitate vast quantities of high-quality training data. The proliferation of data-centric AI models and the increasing complexity of real-world datasets are compelling enterprises to invest in advanced data augmentation tools to enhance data diversity and model robustness, as per the latest research insights.

One of the principal growth factors fueling the Data Augmentation Tools market is the intensifying adoption of AI-driven solutions across industries such as healthcare, automotive, retail, and finance. Organizations are increasingly leveraging data augmentation to overcome the challenges posed by limited or imbalanced datasets, which are often a bottleneck in developing accurate and reliable AI models. By synthetically expanding training datasets through augmentation techniques, enterprises can significantly improve the generalization capabilities of their models, leading to enhanced performance and reduced risk of overfitting. Furthermore, the surge in computer vision, natural language processing, and speech recognition applications is creating a fertile environment for the adoption of specialized augmentation tools tailored to image, text, and audio data.

Another significant factor contributing to market growth is the rapid evolution of augmentation technologies themselves. Innovations such as Generative Adversarial Networks (GANs), automated data labeling, and domain-specific augmentation pipelines are making it easier for organizations to deploy and scale data augmentation strategies. These advancements are not only reducing the manual effort and expertise required but also enabling the generation of highly realistic synthetic data that closely mimics real-world scenarios. As a result, businesses across sectors are able to accelerate their AI/ML development cycles, reduce costs associated with data collection and labeling, and maintain compliance with stringent data privacy regulations by minimizing the need to use sensitive real-world data.

The growing integration of data augmentation tools within cloud-based AI development platforms is also acting as a major catalyst for market expansion. Cloud deployment offers unparalleled scalability, accessibility, and collaboration capabilities, allowing organizations of all sizes to harness the power of data augmentation without significant upfront infrastructure investments. This democratization of advanced data engineering tools is especially beneficial for small and medium enterprises (SMEs) and academic research institutes, which often face resource constraints. The proliferation of cloud-native augmentation solutions is further supported by strategic partnerships between technology vendors and cloud service providers, driving broader market penetration and innovation.

From a regional perspective, North America continues to dominate the Data Augmentation Tools market, driven by the presence of leading AI technology companies, a mature digital infrastructure, and substantial investments in research and development. However, the Asia Pacific region is emerging as the fastest-growing market, fueled by rapid digital transformation initiatives, a burgeoning startup ecosystem, and increasing government support for AI innovation. Europe also holds a significant share, underpinned by strong regulatory frameworks and a focus on ethical AI development. Meanwhile, Latin America and the Middle East & Africa are witnessing steady adoption, particularly in sectors such as BFSI and healthcare, where data-driven insights are becoming increasingly critical.

Component Analysis

The Data Augmentation Tools market by component is bifurcated into Software and Services. The software segment currently accounts for the largest share of the market, owing to the widespread deployment of standalone and integrated augmentation solutions across enterprises and research institutions. These software plat

Over the last ten years, social media has become a crucial data source for businesses and researchers, providing a space where people can express their opinions and emotions. To analyze this data and classify emotions and their polarity in texts, natural language processing (NLP) techniques such as emotion analysis (EA) and sentiment analysis (SA) are employed. However, the effectiveness of these tasks using machine learning (ML) and deep learning (DL) methods depends on large labeled datasets, which are scarce in languages like Spanish. To address this challenge, researchers use data augmentation (DA) techniques to artificially expand small datasets. This study aims to investigate whether DA techniques can improve classification results using ML and DL algorithms for sentiment and emotion analysis of Spanish texts. Various text manipulation techniques were applied, including transformations, paraphrasing (back-translation), and text generation using generative adversarial networks, to small datasets such as song lyrics, social media comments, headlines from national newspapers in Chile, and survey responses from higher education students. The findings show that the Convolutional Neural Network (CNN) classifier achieved the most significant improvement, with an 18% increase using the Generative Adversarial Networks for Sentiment Text (SentiGan) on the Aggressiveness (Seriousness) dataset. Additionally, the same classifier model showed an 11% improvement using the Easy Data Augmentation (EDA) on the Gender-Based Violence dataset. The performance of the Bidirectional Encoder Representations from Transformers (BETO) also improved by 10% on the back-translation augmented version of the October 18 dataset, and by 4% on the EDA augmented version of the Teaching survey dataset. These results suggest that data augmentation techniques enhance performance by transforming text and adapting it to the specific characteristics of the dataset. Through experimentation with various augmentation techniques, this research provides valuable insights into the analysis of subjectivity in Spanish texts and offers guidance for selecting algorithms and techniques based on dataset features.

Data Augmentation Tools Market Outlook

As per our latest research, the global Data Augmentation Tools market size reached USD 1.47 billion in 2024, reflecting the rapidly increasing adoption of artificial intelligence and machine learning across diverse sectors. The market is experiencing robust momentum, registering a CAGR of 25.3% from 2025 to 2033. By the end of 2033, the Data Augmentation Tools market is forecasted to reach a substantial value of USD 11.6 billion. This impressive growth is primarily driven by the escalating need for high-quality, diverse datasets to train advanced AI models, coupled with the proliferation of digital transformation initiatives across industries.

The primary growth factor fueling the Data Augmentation Tools market is the exponential rise in AI and machine learning applications, which require vast amounts of labeled data for effective training. As organizations strive to develop more accurate and robust models, the demand for data augmentation solutions that can synthetically expand and diversify datasets has surged. This trend is particularly pronounced in sectors such as healthcare, automotive, and retail, where the quality and quantity of data directly impact the performance and reliability of AI systems. The market is further propelled by the increasing complexity of data types, including images, text, audio, and video, necessitating sophisticated augmentation tools capable of handling multimodal data.

Another significant driver is the growing focus on reducing model bias and improving generalization capabilities. Data augmentation tools enable organizations to generate synthetic samples that account for various real-world scenarios, thereby minimizing overfitting and enhancing the robustness of AI models. This capability is critical in regulated industries like BFSI and healthcare, where the consequences of biased or inaccurate models can be severe. Furthermore, the rise of edge computing and IoT devices has expanded the scope of data augmentation, as organizations seek to deploy AI solutions in resource-constrained environments that require optimized and diverse training datasets.

The proliferation of cloud-based solutions has also played a pivotal role in shaping the trajectory of the Data Augmentation Tools market. Cloud deployment offers scalability, flexibility, and cost-effectiveness, allowing organizations of all sizes to access advanced augmentation capabilities without significant infrastructure investments. Additionally, the integration of data augmentation tools with popular machine learning frameworks and platforms has streamlined adoption, enabling seamless workflow integration and accelerating time-to-market for AI-driven products and services. These factors collectively contribute to the sustained growth and dynamism of the global Data Augmentation Tools market.

From a regional perspective, North America currently dominates the Data Augmentation Tools market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading technology companies, robust investment in AI research, and early adoption of digital transformation initiatives have established North America as a key hub for data augmentation innovation. Meanwhile, Asia Pacific is poised for the fastest growth over the forecast period, driven by the rapid expansion of the IT and telecommunications sector, burgeoning e-commerce industry, and increasing government initiatives to promote AI adoption. Europe also maintains a significant market presence, supported by stringent data privacy regulations and a strong focus on ethical AI development.

Component Analysis

The Component segment of the Data Augmentation Tools market is bifurcated into Software and Services, each playing a critical role in enabling organizations to leverage data augmentation for AI and machine learning initiatives. The software sub-segment comprises

Medical image analysis is critical to biological studies, health research, computer- aided diagnoses, and clinical applications. Recently, deep learning (DL) techniques have achieved remarkable successes in medical image analysis applications. However, these techniques typically require large amounts of annotations to achieve satisfactory performance. Therefore, in this dissertation, we seek to address this critical problem: How can we develop efficient and effective DL algorithms for medical image analysis while reducing annotation efforts? To address this problem, we have outlined two specific aims: (A1) Utilize existing annotations effectively from advanced models; (A2) extract generic knowledge directly from unannotated images.

To achieve the aim (A1): First, we introduce a new data representation called TopoImages, which encodes the local topology of all the image pixels. TopoImages can be complemented with the original images to improve medical image analysis tasks. Second, we propose a new augmentation method, SAMAug-C, that lever- ages the Segment Anything Model (SAM) to augment raw image input and enhance medical image classification. Third, we propose two advanced DL architectures, kCBAC-Net and ConvFormer, to enhance the performance of 2D and 3D medical image segmentation. We also present a gate-regularized network training (GrNT) approach to improve multi-scale fusion in medical image segmentation. To achieve the aim (A2), we propose a novel extension of known Masked Autoencoders (MAEs) for self pre-training, i.e., models pre-trained on the same target dataset, specifically for 3D medical image segmentation.

Scientific visualization is a powerful approach for understanding and analyzing various physical or natural phenomena, such as climate change or chemical reactions. However, the cost of scientific simulations is high when factors like time, ensemble, and multivariate analyses are involved. Additionally, scientists can only afford to sparsely store the simulation outputs (e.g., scalar field data) or visual representations (e.g., streamlines) or visualization images due to limited I/O bandwidths and storage space. Therefore, in this dissertation, we seek to address this critical problem: How can we develop efficient and effective DL algorithms for scientific data generation and compression while reducing simulation and storage costs?

To tackle this problem: First, we propose a DL framework that generates un- steady vector fields data from a set of streamlines. Based on this method, domain scientists only need to store representative streamlines at simulation time and recon- struct vector fields during post-processing. Second, we design a novel DL method that translates scalar fields to vector fields. Using this approach, domain scientists only need to store scalar field data at simulation time and generate vector fields from their scalar field counterparts afterward. Third, we present a new DL approach that compresses a large collection of visualization images generated from time-varying data for communicating volume visualization results.

🩺 Dataset Description

This dataset is an augmented version of an ECG image dataset created to balance and enrich the original classes for deep learning–based cardiovascular disease classification.

The original dataset consisted of unbalanced image counts per class in the training set: - ABH: 233 images - MI: 239 images - HMI: 172 images - NORM: 284 images

To improve class balance and model generalization, each class in the training set was expanded to 500 images using a combination of morphological, noise-based, and geometric data augmentation techniques. Additionally, the test set includes 112 images per class.

⚖️ Final Dataset Composition

• Training set: 4 classes × 500 images each → 2,000 images total
• Test set: 4 classes × 112 images each → 448 images total

🔬 Data Augmentation Techniques

1. Morphological Alterations - Erosion - Dilation - None (original preserved)

2. Noise Introduction - augment_noise_black_rain — simulates black streaks - augment_noise_pixel_dropout_black — random black pixel dropout - augment_noise_white_rain — simulates white streaks - augment_noise_pixel_dropout_white — random white pixel dropout

3. Geometric Transformations - Shift — small translations in all directions - Scale — random zoom-in/zoom-out between 0.9× and 1.1× - Rotate — small random rotation between -5° and +5°

These transformations were applied with balanced proportions to ensure diversity and realism while preserving diagnostic features of ECG signals.

💡 Intended Use

This dataset is designed for: - Training and evaluating deep learning models (CNNs, ViTs) for ECG image classification - Research in medical image augmentation, imbalanced data learning, and cardiovascular disease prediction

📘 License

This dataset is released under the CC0 1.0 License, allowing free use and distribution for research and educational purposes.

The Synthetic Data Platform market is experiencing robust growth, driven by the increasing need for data privacy, escalating data security concerns, and the rising demand for high-quality training data for AI and machine learning models. The market's expansion is fueled by several key factors: the growing adoption of AI across various industries, the limitations of real-world data availability due to privacy regulations like GDPR and CCPA, and the cost-effectiveness and efficiency of synthetic data generation. We project a market size of approximately $2 billion in 2025, with a Compound Annual Growth Rate (CAGR) of 25% over the forecast period (2025-2033). This rapid expansion is expected to continue, reaching an estimated market value of over $10 billion by 2033. The market is segmented based on deployment models (cloud, on-premise), data types (image, text, tabular), and industry verticals (healthcare, finance, automotive). Major players are actively investing in research and development, fostering innovation in synthetic data generation techniques and expanding their product offerings to cater to diverse industry needs. Competition is intense, with companies like AI.Reverie, Deep Vision Data, and Synthesis AI leading the charge with innovative solutions. However, several challenges remain, including ensuring the quality and fidelity of synthetic data, addressing the ethical concerns surrounding its use, and the need for standardization across platforms. Despite these challenges, the market is poised for significant growth, driven by the ever-increasing need for large, high-quality datasets to fuel advancements in artificial intelligence and machine learning. The strategic partnerships and acquisitions in the market further accelerate the innovation and adoption of synthetic data platforms. The ability to generate synthetic data tailored to specific business problems, combined with the increasing awareness of data privacy issues, is firmly establishing synthetic data as a key component of the future of data management and AI development.

Data augmentation methods have played an important role in the recent advance of deep learning models, and have become an indispensable component of state-of-the-art models in semi-supervised, self-supervised, and supervised training for vision.

This dataset contains synthetic video samples generated from a 10-class subset of Tiny ImageNet using Stable Video Diffusion (SVD). It is designed to evaluate the impact of generative temporal augmentation on image classification performance.

Each training and validation video corresponds to a single image augmented into a sequence of frames.

Videos are stored in .mp4 format and labeled via train.csv and val.csv.

Sources:

Tiny ImageNet: Stanford CS231n

SVD model: Stable Video Diffusion

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

The Gima Kolmi Leaf Dataset offers high-resolution, 700+ images of Ipomoea aquatica (Gima Kolmi) leaves, captured under natural sunlight in open field conditions in Rangpur, Bangladesh. Total Dataset Size: 7,292 images across all categories, including raw, processed, and augmented data. As a widely cultivated leafy vegetable in the country, early disease detection in Gima Kolmi is vital for ensuring yield and food security. This dataset supports research in machine learning and computer vision for plant disease classification, reflecting real-world agricultural conditions in rural Bangladesh. It is well-suited for developing AI-driven tools for precision agriculture and mobile-based plant health diagnostics.

Key Features of the Gima Kolmi Leaf Dataset :

1. Total Raw Images: 730
2. Processed Images: 730
3. Augmented Dataset 1: 3,645 images
4. Augmented Dataset 2: 2,187 images
5. Two Balanced Classes: • Disease • Disease Free
6. Metadata Included: • CSV file with filename, class label, source folder, and image dimensions
7. Visual Summary & Sample Image Provided

Data Collection : Device Used: Realme GT Master Edition (64 MP camera) Environment: Natural daylight, sunny conditions Location: OFRD, Rangpur, Bangladesh Coordinates: 25.72046007746578, 89.26284024499962 All images were taken manually by the author under consistent lighting and background conditions.

Preprocessing Details : • Resizing: All processed images resized to 224x224 pixels • Normalization: Pixel values scaled to [-1, 1] range • White background removed (optional) • Format: JPG, JPEG, PNG

Data Augmentation Techniques :

Augmented_1: • Horizontal_Flip • Vertical Flip • Rotation (±15°) • Brightness/Contrast Augmented_2 : • Random Zoom • Shearing • Cropping and Padding • Color Jitter • Gaussian Noise

Applications : The Gima Kolmi Leaf Dataset serves as a valuable resource for a wide range of research and development efforts, particularly in: ➨ Leaf Disease Classification using machine learning and deep learning techniques. ➨ Precision Agriculture, enabling the development of decision support tools for Bangladeshi farmers. ➨ Mobile-Based Disease Detection apps for in-field diagnosis and real-time crop monitoring. ➨ AI-Powered Agricultural Robotics for automated crop health assessment. ➨ Transfer Learning & Model Benchmarking in plant pathology-related computer vision tasks.

By providing labeled leaf images from real Bangladeshi farms, this dataset enables AI-based disease detection, supports early diagnosis, and promotes sustainable agriculture through practical and academic applications.

This is a companion dataset for the paper titled "Class-specific data augmentation for plant stress classification" by Nasla Saleem, Aditya Balu, Talukder Zaki Jubery, Arti Singh, Asheesh K. Singh, Soumik Sarkar, and Baskar Ganapathysubramanian published in The Plant Phenome Journal, https://doi.org/10.1002/ppj2.20112


Abstract:

Data augmentation is a powerful tool for improving deep learning-based image classifiers for plant stress identification and classification. However, selecting an effective set of augmentations from a large pool of candidates remains a key challenge, particularly in imbalanced and confounding datasets. We propose an approach for automated class-specific data augmentation using a genetic algorithm. We demonstrate the utility of our approach on soybean [Glycine max (L.) Merr] stress classification where symptoms are observed on leaves; a particularly challenging problem due to confounding classes in the dataset. Our approach yields substantial performance, achieving a mean-per-class accuracy of 97.61% and an overall accuracy of 98% on the soybean leaf stress dataset. Our method significantly improves the accuracy of the most challenging classes, with notable enhancements from 83.01% to 88.89% and from 85.71% to 94.05%, respectively. A key observation we make in this study is that high-performing augmentation strategies can be identified in a computationally efficient manner. We fine-tune only the linear layer of the baseline model with different augmentations, thereby reducing the computational burden associated with training classifiers from scratch for each augmentation policy while achieving exceptional performance. This research represents an advancement in automated data augmentation strategies for plant stress classification, particularly in the context of confounding datasets. Our findings contribute to the growing body of research in tailored augmentation techniques and their potential impact on disease management strategies, crop yields, and global food security. The proposed approach holds the potential to enhance the accuracy and efficiency of deep learning-based tools for managing plant stresses in agriculture.

The dataset comprises a total of 510 logo images categorized into five classes, representing different brands: Gojek, Grab, Uniqlo, Miniso, and CircleCI. Specifically, the dataset is divided into 83 training images and 19 testing images. Each image is standardized to a resolution of 300x300 pixels in PNG format. Additionally, the dataset includes a CSV file that labels each image as either positive (original) or negative (forged), which is crucial for tasks involving image verification or detection of counterfeits.

This dataset is particularly suited for applications in machine learning models that employ similarity metrics or triplet loss functions, which are common in tasks such as image comparison and identity verification. The data structure and labeling facilitate training models to discern subtle differences between genuine and forged logos, which is vital in the field of trademark protection.

Researchers interested in utilizing this dataset for academic purposes, particularly in studies involving data augmentation techniques or deep learning for logo recognition and verification, should cite the following source:

Suyahman, S., Sunardi, S., Murinto, M., & Khusna, A. N. (2024). Data Augmentation Using Test-Time Augmentation on Convolutional Neural Network-Based Brand Logo Trademark Detection. Indonesian Journal of Artificial Intelligence and Data Mining, 7(2).

Khusna, A. N. (2024). Siamese Neural Networks with Chi-square Distance for Trademark Image Similarity Detection. Scientific Journal of Informatics, 11(2), 429-438.

This citation provides acknowledgment to the original creators and their contribution to the development of methodologies in image processing and artificial intelligence.

This extensive dataset presents a meticulously curated collection of low-resolution images showcasing 20 well-established rice varieties native to diverse regions of Bangladesh. The rice samples were carefully gathered from both rural areas and local marketplaces, ensuring a comprehensive and varied representation. Serving as a visual compendium, the dataset provides a thorough exploration of the distinct characteristics of these rice varieties, facilitating precise classification.

Dataset Composition

The dataset encompasses 18 distinct classes, encompassing Subol Lota, Bashmoti (Deshi), Ganjiya, Shampakatari, Sugandhi Katarivog, BR-28, BR-29, Paijam, Bashful, Lal Aush, BR-Jirashail, Gutisharna, Birui, Najirshail, Pahari Birui, Polao (Katari), Polao (Chinigura), Amon, Shorna-5, and Lal Binni. In total, the dataset comprises 4,730 original JPG images and 23,650 augmented images.

Image Capture and Dataset Organization

These images were captured using an iPhone 11 camera with a 5x zoom feature. Each image capturing these rice varieties was diligently taken between October 18 and November 29, 2023. To facilitate efficient data management and organization, the dataset is structured into two variants: Original images and Augmented images. Each variant is systematically categorized into 20 distinct sub-directories, each corresponding to a specific rice variety.

Original Image Dataset

The primary image set comprises 4,730 JPG images, uniformly sized at 853 × 853 pixels. Due to the initial low resolution, the file size was notably 268 MB. Employing compression through a zip program significantly optimized the dataset, resulting in a final size of 254 MB.

Augmented Image Dataset

To address the substantial image volume requirements of deep learning models for machine vision, data augmentation techniques were implemented. Transformations such as rotation (90° left, 90° right, 180° left) and flip were applied, generating an additional set of augmented images in every class, totaling 23,650 augmented images. These augmented images, also in JPG format and uniformly sized at 512 × 512 pixels, initially amounted to 781 MB. However, post-compression, the dataset was further streamlined to 699 MB.

Dataset Storage and Access

The raw and augmented datasets are stored in two distinct zip files, namely 'Original.zip' and 'Augmented.zip'. Both zip files contain 20 sub-folders representing a unique rice variety, namely 1_Subol_Lota, 2_Bashmoti, 3_Ganjiya, 4_Shampakatari, 5_Katarivog, 6_BR28, 7_BR29, 8_Paijam, 9_Bashful, 10_Lal_Aush, 11_Jirashail, 12_Gutisharna, 13_Red_Cargo,14_Najirshail, 15_Katari_Polao, 16_Lal_Biroi, 17_Chinigura_Polao, 18_Amon, 19_Shorna5, 20_Lal_Binni.

This dataset is the result of innovative research addressing the challenges of applying deep learning to medical image classification, specifically in the domain of diabetic retinopathy detection. The dataset combines real medical images with synthetic images generated using Denoising Diffusion Probabilistic Models (DDPM). Key features:

• Augmented dataset for diabetic retinopathy detection
• Includes both real and DDPM-generated synthetic images
• Image size: 192x192 pixels
• Demonstrates improved detection accuracy by an average of 9%
• Addresses class imbalance issues common in medical datasets

The dataset is valuable for researchers and practitioners working on:

• Medical image classification
• Generative models in healthcare
• Data augmentation techniques
• Addressing class imbalance in machine learning
• Diabetic retinopathy detection and other ophthalmological applications

By making this dataset publicly available, we aim to contribute to the advancement of deep learning applications in medical imaging and encourage further research in this critical area.

Acknowledgement: This dataset builds upon and extends the APTOS dataset collected by Aravind Eye Hospital. The original dataset can be found at www.kaggle.com/competitions/aptos2019-blindness-detection/overview/$citation

Early detection of Diabetic Retinopathy is a key challenge to prevent a patient from potential vision loss. The task of DR detection often requires special expertise from ophthalmologists. In remote places of the world such facilities may not be available, so In an attempt to automate the detection of DR, machine learning and deep learning techniques can be adopted. Some of the recent papers have proven such success on various publicly available dataset.

Another challenge of deep learning techniques is the availability of rightly processed standardized data. Cleaning and preprocessing the data often takes much longer time than the model training. As a part of my research work, I had to preprocess the images taken from APTOS and Messidor before training the model. I applied circle-crop and Graham Ben's preprocessing technique and scaled all the images to 512X512 format. Also, I applied the data augmentation technique and increased the number of samples from 3662 data of APTOS to 18310, and 400 messidor samples to 3600 samples. I divided the images into two classes class 0 (NO DR) and class 1 (DR). The large number of data is essential for transfer learning. This process is very cumbersome and time-consuming. So I thought to upload the newly generated dataset in Kaggle so that some people might find it useful for their work. I hope this will help many people. Feel free to use the data.

Context

Machine Learning Subreddit ... is a central place to know all the news of machine learning, deep learning and AI.

Content

Reddit posts from subreddit machinelearning, downloaded from https://www.reddit.com/r/machinelearnign/ using praw (The Python Reddit API Wrapper).

Inspiration

You can use the data to:

• Perform sentiment analysis;
• Identify discussion topics;
• Follow the trends (DeepLearning, Data Augmentation, AutoML, Reinforcement Learning).

The greatest challenge of machine learning problems is to select suitable techniques and resources such as tools and datasets. Despite the existence of millions of speakers around the globe and the rich literary history of more than a thousand years, it is expensive to find the computational linguistic work related to Punjabi Shahmukhi script, a member of the Perso-Arabic context-specific script low-resource language family. The selection of the best algorithm for a machine learning problem heavily depends on the availability of a dataset for that specific task. We present a novel, custom-built, and first-of-its-kind dataset for Punjabi in Shahmukhi script, its design, development, and validation process using Artificial Neural Networks. The dataset uses up to 40 classes, in multiple fonts, including Nasta’leeq, Naskh, and Arabic Type, etc, many font sizes and has been presented in many sub sizes. The dataset has been designed with a special dataset construction process by which researchers can make changes in the dataset as per their requirements.* The dataset construction program can also perform data augmentation to generate millions of images for a machine learning algorithm with different parameters including font type, size orientation, and translation. Using this process, a dataset of any language can be constructed. The CNNs in different architectures have been implemented and validation accuracy of up to 99% has been achieved.