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Image classification, object detection, segmentation, face recognition, OCR, image generation, video understanding
14,079 datasets
Chemical and mineralogical data for four ferromanganese samples from the Dampier Ridge and Lord Howe Rise off eastern Australia. The dataset includes two nodules and two crusts collected by the West German vessel Sonne from water depths between 1549 and 2700 meters. It is provided by the Australian Ocean Data Network and was last updated in June 2026.
Vaskor Mostafa's 2026 study presents results from an ablation study of the LeafDet object detection model. The model, based on YOLOv8, was designed to detect eight distinct tomato leaf diseases using a balanced dataset of 7,836 images named PlantTom. The study reports a 91.6% [email protected] performance and includes validation via Eigen-CAM visualization.
LeafDet model performance data for detecting eight distinct tomato leaf diseases. The dataset is associated with the PlantTom image collection, which contains 7,836 images, and was authored by Vaskor Mostafa. It was last updated on May 22, 2026.
7,836 images of tomato leaf diseases across 8 distinct classes, compiled from various public sources to address class imbalance. The dataset was created by Vaskor Mostafa and last updated on 2026-05-22. It was developed to support the LeafDet object detection model for smart agriculture applications.
Uganda's financial service points exported from OpenStreetMap. The data includes banks, ATMs, post offices, money transfer agents, and microfinance branches tagged via the `amenity` key. It was last updated on 2026-05-15 by the Humanitarian OpenStreetMap Team.
A Spanish study collected survey data from 37 healthcare professionals managing spinal muscular atrophy (SMA). The dataset likely contains responses to validated instruments measuring procrastination, burnout, regret intensity, and evidence-based practice attitudes. The data was uploaded by Jorge Maurino and last updated on June 2, 2026.
Annual portraits of the workforce and consultant usage within Québec public bodies' information resources departments. Public bodies report their numbers annually on the first Monday in November, with compiled data published in the second quarter of the following year. The dataset is produced by the Government of Québec under the Act on the Governance and Management of Information Resources.
5.5 KB of parameters for training a neural network to classify power quality disturbances. The dataset was created by Junqing Zhang and uploaded to figshare on June 3, 2026. It likely contains parameters used in a simulation analysis comparing a proposed feature-image fusion method against traditional recognition systems.
An 18-year Intensively Monitored Watershed experiment tested low-tech, process-based wood additions in three tributaries to Asotin Creek, Washington. The dataset, authored by Stephen N. Bennett and last updated in 2026, includes results from installing 654 large wood structures and monitoring their effects on wood frequency, jam formation, and channel morphology. Adaptive management actions, maintenance, and responses to fire and high flows are documented.
Ummer Shakeel published a dataset on figshare in 2026 containing performance metrics for deep learning models detecting rice leaf diseases. The dataset likely contains comparative evaluation results for five transfer learning architectures, including InceptionV3, DenseNet201, and MobileNetV2, trained on a balanced set of 1914 image samples. The work focuses on Pakistan, a major rice producer, and uses post-hoc visualization techniques like GrabCut segmentation for interpretability.
1914 image samples of rice leaves were processed to evaluate five deep transfer learning architectures. The dataset, created by Ummer Shakeel and last updated in May 2026, contains model training hyperparameters and configuration details, with results including test accuracies up to 98.43% for InceptionV3. It is shared under a CC-BY-4.0 license on figshare.
Ummer Shakeel's dataset provides image counts per class for a rice leaf disease dataset. The underlying dataset contains 1914 balanced image samples and was used to evaluate five deep transfer learning architectures for disease classification. The dataset was last updated on May 7, 2026.
Pakistan is a major global rice producer. This dataset contains a comparative evaluation of five deep transfer learning architectures (InceptionV3, DenseNet201, ResNet152V2, EfficientNetV2L, MobileNetV2) trained on a balanced set of 1914 rice leaf images for disease detection. The dataset was created by Ummer Shakeel and last updated in May 2026.
Pakistan is the fourth-largest rice producer and exporter globally. This dataset contains a comparative evaluation of five deep transfer learning architectures (InceptionV3, DenseNet201, ResNet152V2, EfficientNetV2L, MobileNetV2) trained on a balanced set of 1914 rice leaf image samples. The work, authored by Ummer Shakeel and last updated in May 2026, applies post-hoc visualization techniques like GrabCut segmentation to enhance interpretability.
Pakistan is the fourth-largest rice producer and the fifth-largest exporter worldwide. This 5.5 KB dataset by Ummer Shakeel, last updated in May 2026, contains performance statistics from a comparative evaluation of five deep transfer learning architectures for classifying rice leaf diseases. The models, including InceptionV3 and MobileNetV2, were trained on a balanced dataset of 1914 image samples.
Rocco Salvatore Calabrò published a secondary analysis of a public multimodal gait dataset on May 25, 2026. The study used data from 138 able-bodied adults and 50 adults with stroke to develop side-aware gait-state representations. The analysis focused on 11 waveform domains, including sagittal kinematics and surface electromyography, to identify three distinct gait states in a cohort of 43 stroke patients.
A 2026 secondary analysis of a public multimodal gait dataset comprising 138 able-bodied adults and 50 adults with stroke. The study derived side-aware gait-state representations from 11 waveform domains, including sagittal kinematics and surface electromyography, focusing on a strict complete-case cohort of 43 stroke patients. The dataset was authored by Rocco Salvatore Calabrò and shared under a CC-BY-4.0 license.
138 able-bodied adults and 50 adults with stroke contributed to this public multimodal gait dataset. The analysis retained a strict complete-case stroke cohort of 43 individuals, organized into three gait states based on 11 waveform domains including sagittal kinematics and surface electromyography. Rocco Salvatore Calabrò published this secondary analysis on figshare in May 2026.
Rocco Salvatore Calabrò conducted a secondary analysis of a public multimodal gait dataset comprising 138 able-bodied adults and 50 adults with stroke. The study derived side-aware gait-state representations from 11 waveform domains, including kinematics and surface electromyography, for 43 stroke patients. The analysis was published on figshare in May 2026.
A secondary analysis of a public multimodal gait dataset comprising 138 able-bodied adults and 50 adults with stroke. The dataset includes 11 waveform domains, such as sagittal kinematics and surface electromyography, each represented by 1,001 time-normalized points. The retained fused side-aware solution organizes a strict complete-case stroke cohort of 43 individuals into three distinct gait states.