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After developing computer vision models for different startups and companies, I've come to realize that there's no fixed way to do it. Just the topic of PyTorch vs TensorFlow is polarizing in itself. That's just the tip of the iceberg - atop of the tons of different annotation formats, models and infrastructure setups.
At Datature, we seek to streamline the pipeline and tasks required to build computer vision models effectively. Starting from annotations, dataset management, workflow management, all the way to training and deployment. The interface has been carefully curated to ensure that teams get it right on the first try!
There will be more how-to contents in the upcoming weeks to demonstrate what we believe data teams and researchers should be doing instead. Meanwhile, here's a five minutes introduction video on how you can use our platform to build your models at neck-breaking speed.
An Evolving Product
We believe that the product should constantly evolve to fit more use cases and we are open to suggestions on the next key feature to build. Additionally, we are looking to build a community around the product, so come chat with us on our Slack Channel!
Train Your Own Object Detection Model
Many Ways, One Outcome
After developing computer vision models for different startups and companies, I've come to realize that there's no fixed way to do it. Just the topic of PyTorch vs TensorFlow is polarizing in itself. That's just the tip of the iceberg - atop of the tons of different annotation formats, models and infrastructure setups.
At Datature, we seek to streamline the pipeline and tasks required to build computer vision models effectively. Starting from annotations, dataset management, workflow management, all the way to training and deployment. The interface has been carefully curated to ensure that teams get it right on the first try!
There will be more how-to contents in the upcoming weeks to demonstrate what we believe data teams and researchers should be doing instead. Meanwhile, here's a five minutes introduction video on how you can use our platform to build your models at neck-breaking speed.
An Evolving Product
We believe that the product should constantly evolve to fit more use cases and we are open to suggestions on the next key feature to build. Additionally, we are looking to build a community around the product, so come chat with us on our Slack Channel!
Train Your Own Object Detection Model
Many Ways, One Outcome
After developing computer vision models for different startups and companies, I've come to realize that there's no fixed way to do it. Just the topic of PyTorch vs TensorFlow is polarizing in itself. That's just the tip of the iceberg - atop of the tons of different annotation formats, models and infrastructure setups.
At Datature, we seek to streamline the pipeline and tasks required to build computer vision models effectively. Starting from annotations, dataset management, workflow management, all the way to training and deployment. The interface has been carefully curated to ensure that teams get it right on the first try!
There will be more how-to contents in the upcoming weeks to demonstrate what we believe data teams and researchers should be doing instead. Meanwhile, here's a five minutes introduction video on how you can use our platform to build your models at neck-breaking speed.
An Evolving Product
We believe that the product should constantly evolve to fit more use cases and we are open to suggestions on the next key feature to build. Additionally, we are looking to build a community around the product, so come chat with us on our Slack Channel!
This article introduces D-FINE, an advanced object detection model addressing the limitations of traditional methods. It uses Fine-grained Distribution Refinement (FDR) for precise bounding box adjustments and Global Optimal Localization Self-Distillation (GO-LSD) for efficient learning. The article also demonstrates fine-tuning D-FINE on custom datasets with Datature Nexus for real-world applications.
How to Use LiteRT for Real-Time Inferencing on Android
8
MIN READ
November 6, 2024
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This article introduces LiteRT, Google’s rebranded tool for on-device AI, with a step-by-step guide to deploying models on Android. It covers model export, integration, and optimization, showcasing how developers can leverage LiteRT for efficient real-time performance in mobile applications.
YOLO11: Step-by-Step Training on Custom Data and Comparison with YOLOv8
5
MIN READ
October 22, 2024
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Ultralytics YOLO11 represents the latest breakthrough in real-time object detection, building on YOLOv8 to address the need for quicker and more accurate predictions in fields such as self-driving cars and surveillance. This article presents a step-by-step guide to training an object detection model using YOLO11 on a crop dataset, comparing its performance with YOLOv8 to showcase its capabilities and emphasize its effectiveness in high-demand situations.
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