Omkar2118/datasets huggingface dataset:
https://github.com/ntu-aris/MMAUD
Full name: MMAUD Dataset

In response to the evolving challenges posed by small unmanned aerial vehicles (UAVs), which possess the potential to transport harmful payloads or independently cause damage, we introduce MMAUD: a comprehensive Multi-Modal Anti-UAV Dataset. MMAUD addresses a critical gap in contemporary threat detection methodologies by focusing on drone detection, UAV-type classification, and trajectory estimation. MMAUD stands out by combining diverse sensory inputs, including stereo vision, various Lidars, Radars, and audio arrays. It offers a unique overhead aerial detection vital for addressing real-world scenarios with higher fidelity than datasets captured on specific vantage points using thermal and RGB. Additionally, MMAUD provides accurate Leica-generated ground truth data, enhancing credibility and enabling confident refinement of algorithms and models, which has never been seen in other datasets. Most existing works do not disclose their datasets, making MMAUD an invaluable resource for developing accurate and efficient solutions. Our proposed modalities are cost-effective and highly adaptable, allowing users to experiment and implement new UAV threat detection tools. Our dataset closely simulates real-world scenarios by incorporating ambient heavy machinery sounds. This approach enhances the dataset's applicability, capturing the exact challenges faced during proximate vehicular operations. It is expected that MMAUD can play a pivotal role in advancing UAV threat detection, classification, trajectory estimation capabilities, and beyond.
Jacquard v2
Full name: Jacquard V2: Refining Datasets using the Human In the Loop Data Correction Method

In the context of rapid advancements in industrial automation, vision-based robotic grasping plays an increasingly crucial role. In order to enhance visual recognition accuracy, the utilization of large-scale datasets is imperative for training models to acquire implicit knowledge related to the handling of various objects. Creating datasets from scratch is a time and labor-intensive process. Moreover, existing datasets often contain errors due to automated annotations aimed at expediency, making the improvement of these datasets a substantial research challenge. Consequently, several issues have been identified in the annotation of grasp bounding boxes within the popular Jacquard Grasp. We propose utilizing a Human-In-The-Loop(HIL) method to enhance dataset quality. This approach relies on backbone deep learning networks to predict object positions and orientations for robotic grasping. Predictions with Intersection over Union (IOU) values below 0.2 undergo an assessment by human operators. After their evaluation, the data is categorized into False Negatives(FN) and True Negatives(TN). FN are then subcategorized into either missing annotations or catastrophic labeling errors. Images lacking labels are augmented with valid grasp bounding box information, whereas images afflicted by catastrophic labeling errors are completely removed. The open-source tool Labelbee was employed for 53,026 iterations of HIL dataset enhancement, leading to the removal of 2,884 images and the incorporation of ground truth information for 30,292 images. The enhanced dataset, named the Jacquard V2 Grasping Dataset, served as the training data for a range of neural networks.