BasicAI Inc

When selecting an outsourcing annotation service, several key factors are essential to consider: ?High-Quality and Low-Cost Annotations: Ensuring the service delivers accurate and reliable annotations at a reasonable cost is crucial to maximizing value. ?Exceptional Quality and Accuracy: Ensuring that the annotations meet stringent quality standards and are highly accurate is vital for reliable AI model training. ?Annotator's Turnaround Time: Quick and efficient annotation turnaround time is essential for meeting project deadlines and maintaining productivity. ?Data Privacy and Security: Protecting sensitive data and ensuring strict adherence to data privacy regulations is paramount. ?Efficiency and Scalability: An annotation service should be efficient and scalable to accommodate projects of varying sizes and complexities. BasicAI data labeling service, with 7+ years of experience and over 30,000 datasets for Model Training, addresses all these critical factors and more.???? Enjoy our full data service, expertise in HITL labeling pros, and a commitment to providing top-notch quality, accuracy, and efficiency to our valued customers worldwide.???? Choose BasicAI for success in AI-powered applications! ?? Discover more: https://lnkd.in/g7ktdFsX #AI #DataAnnotation #DataLabeling #OutsourcingService #AIExperts #DataPrivacy #OutsourcingAnnotation #DataQuality #AnnotationExpertise #AnnotationTools #RealTimeQA #Efficiency #BasicAI

?????? ???? ???? ???????????????? ?????? ???????????? ???????????????? ????????????? #AI researchers increasingly test intelligence by having models play classic games humans enjoy. An Anthropic internal team created an interface enabling #Claude to play Pokémon Red. Their 3.7 Sonnet version collected three gym badges??–significant progress compared to previous versions that stalled in Viridian Forest. This improvement comes from the new model's ability to "think through" and try different strategies when facing obstacles. Meanwhile, ?????? ???? ???????? in California has developed "GamingAgent," testing AI capabilities through real-time games like Super Mario Bros. Their system provides game screenshots to #LLMs , which then generate Python code to control Mario. Interestingly, non-reasoning models performed better in this game, where split-second timing makes all the difference between success and failure. Some experts question whether these game-based evaluations truly measure model capability. Nevertheless, Anthropic's inclusion of the Pokémon benchmark in its announcement signals a shift away from traditional benchmarks toward more relatable tests. They believe this demonstrates the model's ability to formulate plans and adapt strategies–crucial for complex business applications like research and financial analysis. While not comprehensive assessments, gaming environments force models to develop complex operational strategies. Their adaptability and problem-solving skills provide valuable insight into AI's ongoing evolution. #innovation #LargeLanguageModel #LargeLanguageModels #LLM #ChatGPT #NaturalLanguageProcessing #ComputerVision #trainingdata #datasets #ArtificialIntelligence #AI #AIModels #DeepLearning #MachineLearning #ML #DL #AIModels #Technology #mlalgorithm #DataAnnotation #DataLabeling #BasicAI

??????????12, ?????????????????? ???? ????????????????????? The YOLO family has always stood out for their real-time performance, with inference speed being a critical requirement. While #Transformer attention mechanisms have shown impressive results in visual tasks, its global attention approach with heavy computational costs simply didn't align with YOLO's speed-first philosophy. As the first attention-centric real-time YOLO detector, #YOLOv12 addresses the efficiency problem through three innovations: ?? It introduces the ???????? ?????????????????? (??2) module that maintains large receptive fields while reducing computational complexity, enabling faster processing. ?? It brings in a Residual Efficient Layer Aggregation Network (??-????????) with scaling factors to optimize large-scale models. ?? It redesigns traditional attention mechanisms to better integrate with YOLO's architecture, boosting computational efficiency. Experiments prove YOLOv12 outperforms previous models in latency, accuracy, and computational demands. For instance, YOLOv12-S achieves 1.5% better mAP than RT-DETR while running 42% faster and using just 36% of the computational resources and 45% of the parameters. ?? Demo: https://lnkd.in/e4nVwj2a Without doubt, this version marks a shift from CNN dominance in YOLO series, showing promise for applications across #autonomousdriving , aerospace, medicalimaging , and other #objectdetection -intensive domains. ???????? ???? ?????? ?????????? – ???? ???????? ???? ???????????????????? ???? ?????????????????? ?????? ???????????? ??????????????????? ??????'?? ?????????????? ?????????? ?? #innovation #ComputerVision #trainingdata #datasets #ArtificialIntelligence #AI #AIModels #DeepLearning #MachineLearning #ML #DL #AIModels #Technology #BoundingBox #ObjectTracking #mlalgorithm #DataAnnotation #DataLabeling #BasicAI

Remember your first encounter with 3D point cloud? Those countless points precisely reconstructing our world. Yet in practice, #LiDAR applications are both more fascinating and challenging than they appear. “????'?? ????????'?? ?????????????? ?????? ?????????? ???? ?????????????????? ?????????????????????????? ???????? ?????????? ?????????? ???? ???????????? ?? ??????????????.” We've dug into real projects and talked with teams on the ground. Here are some lesser-known insights: ?? In highway scenarios, LiDAR sensors require regular maintenance – monthly lens cleaning is essential. While everyone talks about real-time processing, keeping raw data for 3-7 days has saved countless debugging sessions. ?? For #autonomousdriving , distant objects at highway speeds might only return a few dozen points. Leading teams are fusing multiple #pointcloud frames together to build a clearer picture. ?? In 3D #objecttracking annotation, cuboid size, pitch angles, ID consistency, and occlusion all impact accuracy. When a truck temporarily disappears behind a building, skilled annotators need to predict where it should be based on context. ?? Warehouse automation faces unique challenges with varying package types. Cardboard boxes deform under pressure, plastic packaging creates reflections, and flexible materials have unpredictable shapes. ?? ???????? ??????????????????, ???????????? ??????????, ??????????????... In #industrialinspection , combining traditional grayscale processing with 3D point cloud analysis significantly improves defect detection. ... ???????? ?????? ???????? ??????????? We've packed 10 real-world case studies across different sectors, covering hardware setups, technical challenges, and annotation requirements. Whether in active LiDAR projects or just starting out, there's valuable insight in these field notes. ? Read our full blog: https://lnkd.in/g_qjYhFZ #innovation #trainingdata #datasets #ArtificialIntelligence #AI #AIModels #DeepLearning #MachineLearning #ML #DL #AIModels #Technology #mlalgorithm #3DPerception #DataAnnotation #DataLabeling #BasicAI

We've curated 6 recent comprehensive reviews to help you track rapid growth in #computervision . Autonomous driving, edge computing, medical imaging... These papers map out where we're headed: 1?? ?? ???????????? ???? ?????????? ???????????? ?????? ???????????????????? ?????????????? https://lnkd.in/gkJgAnuW This deep dive explores how #autonomousvehicles understand and predict their environment. The paper breaks down current approaches to scene representation (image-based, BEV, occupancy grids, #pointcloud ) and shows how #selfsupervisedlearning is pushing the boundaries. 2?? ????????????-???????????????? ???????????? ?????? ???????? ????????????????: ?? ?????????????????????????? ???????????? https://lnkd.in/gqcvTpdN As #VisionLanguageModels get bigger, how do we make them work on smaller devices? This review tackles the challenge of deploying #VLMs to edge devices, covering compression techniques, fine-tuning strategies, and real-world applications. 3?? ???? ?????? ??????????????: ?????????????? ?????????????? ????????????????, ??????????????????, ?????? ?????????? ???????????? ???????????? https://lnkd.in/gsxsJGgf It proposes a "living review framework" tracking #medicalimaging datasets and research outputs. It's built around an SQL database that connects #dataset documentation, research artifacts (like annotations and error reports), and dataset relationships. 4?? ??????????8 ???? ????????11: ?? ?????????????????????????? ???????????????????????? ????-?????????? ?????????????????????? ???????????? https://lnkd.in/g4-i_aeQ An in-depth comparison of the latest four YOLO architectures. The authors dig into papers, docs, and source code to map out how these models evolved and what makes each version tick. 5?? ?????????????? ?????????????????????? ?????? ????????????????????: ?? ?????????????????????????? ???????????? ???? ????????, ????????????, ?????? ???????? ???????????????????? https://lnkd.in/giy4_ZyX A comprehensive review covering facial, speech, and text modalities. Details preprocessing techniques, datasets, state-of-the-art methods, evaluation metrics, and cross-modal emotion control techniques. 6?? ??????????-????????????: ???????????? ???? ???????????? ???????????? ?????????????????????? ???????????????????? ???? ???????????? https://lnkd.in/gsmh6TgF A fresh take on Human Action Recognition (HAR). Introduces the SMART-Vision framework to show how different #deeplearning approaches fit together. First comprehensive Open-HAR analysis included, plus solid insights on where the field is heading. What's your bet on the most commercially promising direction here? Share your thoughts below ?? #Research #innovation #trainingdata #datasets #ArtificialIntelligence #AI #AIModels #MachineLearning #ML #DL #AIModels #Technology #mlalgorithm #AutonomousDriving #DataAnnotation #DataLabeling #BasicAI

Led by ???????????? ????, LandingAI has just introduced ?????????????? ???????????? ??????????????????, a breakthrough that could transform how we approach #objectdetection tasks. Traditional object detection relies heavily on manual #boundingbox annotations and #modeltraining – limiting detection to pre-labeled objects. Agentic-OD flips this paradigm: by leveraging pure reasoning capabilities, it can identify and analyze objects in images with nothing more than a text prompt, dramatically streamlining the development process. ??????????????-???? ?????????? ?? ????????????????????, ??????????-?????????? ????????????????. Rather than instant inference, it spends 20-30 seconds on deep reasoning – much like OpenAI's o1 and #DeepSeekR1 do with text. By combining reflection, tool utilization, planning, and multi-agent collaboration, Agentic-OD has shown remarkable results, outpacing current leading systems in internal tests. While Agentic-OD might remind some of ????????-???????????? ???????????? ?????????????????? in handling long-tail and open-world objects, it represents a fundamental shift – from "train-and-detect" to "think-and-reason." This showcases the potential of integrating #LLM -style reasoning into #computervision . This doesn't mean we can throw out our existing toolbox. Manual annotation remains crucial for high-stakes applications like #autonomousdriving , industrial inspection, and medical imaging. We'll still need #groundtruth data for benchmarking and evaluation. For real-time applications, traditional pre-trained models might be more practical than agent-based approaches that require longer inference times. ??????????????-???? ?????????????????????? ???????????? ???????? ???????????????? ???????????????? ????????????????????????. Andrew Ng notes that while this is just an initial attempt, it opens a new chapter in computer vision. As LandingAI's latest innovation, #AgenticOD opens up exciting possibilities for both research and industry applications. The future focus lies in improving reasoning performance for edge deployment. #LandingAI #AgenticObjectDetection #innovation #trainingdata #datasets #ArtificialIntelligence #AI #AIModels #DeepLearning #MachineLearning #ML #DL #AIModels #Technology #mlalgorithm #DataAnnotation #DataLabeling #BasicAI

????????????1 ?? ?????????????? ????????????????????: ?????????? ???????? ???????????????????? ???????????????? ???? ?????????? ?????? ???????? ????????? Choosing the right #dataannotation tool is essential for #AI teams looking to build high-quality training datasets efficiently. BasicAI's data annotation platform has been trusted by leading global AI teams, contributing to the creation of more than 100,000 training datasets. The platform is available in two versions: the open-source Xtreme1 and the enterprise solution. But which one best fits your needs? Let's break it down: ????????????1: ?????????????????????? ?????? ???????? ???????????????????? & ?????????? ?????????? · Completely #opensource , free to use, and customize · Supports basic image, point cloud, and #LLM annotation · Ideal for developers with limited budgets and smaller datasets ?????????????? ???????? ???????????????????? ????????????????: ?????????????????????? ?????? ?????????? ???? ?????????? · Advanced tools for #computervision (image, video, point cloud, 4D-BEV fusion) and #NLP (text, audio, LLM) · Seamless team collaboration with granular access control and real-time progress tracking · Multi-stage quality control with customizable rules for auto QA · Optimized for #autonomousdriving , #robotics , and other complex use cases · Enterprise-grade performance for massive datasets ???????? ?????????????????? ??????????: ? Intuitive UI for efficient #datalabeling ? AI-assisted annotation to speed up workflows ? Support for diverse data formats ? Flexible Ontology management ?????? ?????????? ?????????? ???????????????? ?? ???????????????? ???????????????????? ???? ?????? ?????? ???????????????? ?????????????? ???? ????????????1 ?????? ?????????????? ????????????????????. While Xtreme1 provides flexibility and control for smaller projects, our enterprise platform is designed to handle the most demanding datasets and labeling workflows. Its advanced functionalities, enhanced data security measures, and scalability make it the top choice for enterprise-level users. Dive deeper into the differences between these two powerful platforms in our latest blog post ?? https://lnkd.in/gQ7jFddM to find the perfect fit for your team's needs. #Technology #ArtificialIntelligence #DeepLearning #MachineLearning #ML #DL #AIModels #AItraining #machinevision #trainingdata #datasets #groundtruth #mlalgorithm #BasicAI

While Segment Anything Model 2 (SAM 2) has made significant strides in object segmentation, it struggles with visual object tracking (VOT) in complex, real-world scenarios. Crowded scenes and fast-moving or occluded objects often lead to tracking errors, as #SAM2 prioritizes appearance over motion cues and temporal consistency. Researchers from the University of Washington have recently proposed SAMURAI, an enhanced adaptation of SAM 2 designed for zero-shot visual #objecttracking with a focus on motion-aware memory. SAMURAI introduces two key technical advancements: ???????????? ????????????????: By leveraging the tried-and-true #KalmanFilter , SAMURAI effectively predicts object motion and refines #boundingbox estimates, ensuring accurate tracking even in the most dynamic scenes. ????????????-?????????? ???????????? ??????????????????: SAM 2's fixed-window memory can introduce low-quality features, causing errors to snowball over time. SAMURAI's intelligent memory selection mechanism nips this problem in the bud, resulting in more reliable tracking. Without requiring retraining or fine-tuning, SAMURAI outperforms SAM 2 on all benchmarks and demonstrates performance comparable to supervised methods like LoRAT and ODTrack. It excels in handling fast-moving or occluded targets, making it suitable for object tracking in sports, dance performances, or real-time tracking of specific targets in crowded or dynamic environments to enhance the intelligence of surveillance systems. ?? Project page: https://lnkd.in/dBwzFu6a #innovation #Technology #artificialintelligence #AI #computervision #trainingdata #datasets #DeepLearning #MachineLearning #ML #DL #AIModels #objectdetection #videoanalysis #groundtruth #mlalgorithm #dataannotation #datalabeling #BasicAI

A centuries-old art mystery has been cracked using #computervision . ?????????????? is one of the most celebrated artists of the Renaissance, renowned for his breathtaking paintings and frescoes. The ?????????????? ?????????? ????????, a vibrant and intricately detailed work, showcases Raphael's mature artistic style. However, since the mid-1800s, art historians have debated the authenticity of this masterpiece. A recent #AI analysis has shed new light on this mystery, revealing that the face of ????. ???????????? was likely painted by another artist. To unravel this mystery, researchers turned to #artificialintelligence techniques. They trained an #AImodel using expert-verified #Raphael paintings, enabling it to recognize his unique artistic style down to the brushstrokes, color combinations, and chiaroscuro. The model, built on Microsoft's ResNet50 framework and enhanced with Support Vector Machine (SVM), can detect subtle differences in artworks at a granular level. This approach has previously achieved a 98% accuracy rate in identifying Raphael's genuine works. By analyzing the faces of the figures in the painting separately, researchers discovered that while the other characters clearly aligned with Raphael's style, St. Joseph's face stood out, suggesting it was likely painted by another hand, possibly Raphael's talented pupil, ???????????? ????????????. This research introduces new tools to the field of art history and showcases the immense potential of AI in art authentication. With its ability to analyze details far beyond the human eye, AI can assist museums and art institutions in conducting more thorough examinations of their collections, aiding in art preservation, forgery detection, and historical research. #innovation #art #trainingdata #datasets #DeepLearning #MachineLearning #ML #DL #AIModels #Technology #groundtruth #mlalgorithm #dataannotation #datalabeling #BasicAI

???????? ???????????????? has unveiled DINO-X, a unified object-centric vision model that delivers the best open-world #objectdetection performance to date. What sets #DINOX apart is its remarkable ability to recognize objects and environments it has never seen before. By leveraging a #Transformer -based architecture and massive datasets, DINO-X achieves prompt-free open-world object detection and delivers unparalleled performance on long-tailed object recognition tasks. DINO-X outshines traditional models with its superior speed, accuracy, and versatility in handling various prompting methods, including text and images. The research team has further extended DINO-X by integrating multiple perception heads, allowing it to handle a wide range of open-world perception and object understanding tasks such as object detection, #instancesegmentation , human #poseestimation , and multimodal Q&A. This effectively achieves a unification of visual tasks. DINO-X owes its exceptional generalization ability and adaptability to Grounding-100M, a colossal #dataset containing over 100 million high-quality grounding samples. This diverse #trainingdata has equipped DINO-X to excel across multiple detection benchmarks, demonstrating its prowess in recognizing an astounding variety of objects. With DINO-X, AI now possesses a keen visual understanding of the open world, enabling it to navigate real-world uncertainties with unprecedented finesse. The potential applications of DINO-X are vast, spanning domains such as #embodiedintelligence , automatic annotation of large-scale #multimodal data, and assistive technologies for the visually impaired. The DINO-X Edge model further amplifies its practical utility by bringing real-time object detection to a broader spectrum of edge devices. Exciting news for developers: the DINO-X API is now publicly available at: https://lnkd.in/dE2Tn478 #innovation #trainingdata #datasets #ArtificialIntelligence #AI #DeepLearning #MachineLearning #ML #DL #AIModels #Technology #groundtruth #mlalgorithm #BasicAI