The core design logic behind DINO-X's custom template solution lies in decomposing the need for "custom model capabilities" into two modules: "basic model capabilities + exclusive visual features". While fully preserving the performance of the DINO-X vision model, this solution significantly lowers the threshold for customization — users do not need to build a full-scale model; instead, they only need to provide a small number of annotated samples to quickly generate a visual template for specific targets. At the same time, it directly reuses DINO-X's powerful visual reasoning capabilities, effectively avoiding the complex training processes and high development costs associated with traditional model customization.
Furthermore, DINO-X custom templates have further enhanced the product's usability. Even if users have no background in AI expertise, they can smoothly complete the entire personalized customization process — from dataset upload to template generation — using the automated tools provided by the platform. For more tutorials on creating and customizing custom templates, please refer to Custom Templates 101: How to Create Your "Exclusive Small Model" on the DINO-X Platform.
Users can also directly filter and call their preferred public visual templates in the DINO-X Template Marketplace. Currently, DINO-X Platform has connected with CountAnything, an AI counting app. You can apply to the official team for free to load your custom template into CountAnything, enabling a customized counting solution.
In the previous article, we showcased a collection of high-quality community templates. This article will continue to focus on the DINO-X Template Marketplace, providing an in-depth introduction to 7 custom templates that excel in vertical fields and demonstrating their actual test results.
Overview of Custom Templates
(1) Beverage Category Recognition
Template Introduction: This template is specifically designed for intelligent retail scenarios. It supports the rapid recognition of mainstream beverage types and brands on the market (such as Coca-Cola, Sprite, Fanta, etc.), and can be widely applied in links such as automatic commodity settlement, real-time out-of-stock alerts for inventory, and statistical analysis of sales data. It helps retail scenarios achieve operational automation and refined management.
Test Results: As shown in the image, in the test scenario where multiple types of beverages are mixed and some are placed at an angle, this template can not only accurately distinguish between different beverage brands such as Coca-Cola and Sprite, but also fully mark the specific placement position and posture of each bottle. Even when there is partial occlusion of beverages, it can still achieve stable recognition.
(2) Cotton Pest Recognition
Template Introduction: This template can be used for the detection and recognition of major pests in cotton fields, supporting the automatic identification of multiple pest categories such as ladybugs, stink bugs, lacewings, and cotton bollworms. It is suitable for agricultural production monitoring, pest situation investigation, field management, and intelligent plant protection systems. It helps agricultural practitioners achieve early detection of pests and precise prevention and control, improving crop yields and pesticide use efficiency.
Test Results: As shown in the image, even when lacewings are perched on leaves, this template can quickly locate and accurately mark their category. At the same time, it can effectively eliminate interfering factors such as leaf textures and soil particles, providing a precise basis for pest situation analysis.
(3) Mask Wearing Recognition
Template Introduction: This template can be used for the detection of mask wearing among people in public areas such as parks, subway stations, hospitals, and office buildings. It supports real-time recognition of situations such as not wearing a mask and incorrect mask wearing, effectively assisting in public health prevention and control as well as management standardization.
Test Results: As shown in the image, this template can accurately identify the mask-wearing status of different individuals in the picture.
(4) Safety Helmet Wearing Recognition
Template Introduction: Addressing the safety management needs of industrial work scenarios, this template can be applied in places such as construction sites, factory workshops, and mine operation areas. It automatically detects whether workers in the picture are wearing safety helmets in a standardized manner and links with on-site monitoring systems and safety early warning devices.
Test Results: As shown in the image, even in environments with low pixel quality and relatively dim light, this template still maintains stable performance. It can not only accurately locate the position of each safety helmet but also clearly mark the specific standing position of workers wearing safety helmets. Even when the color of the safety helmet is similar to the background color (e.g., distant safety helmets are confused with the wall), it can effectively distinguish them, avoiding missed detections and false detections.
(5) Flame Recognition
Template Introduction: This template can be adapted to fire source monitoring in various scenarios such as family living rooms, kitchen stoves, laboratory workbenches, and outdoor camping sites. It supports real-time recognition of flame shapes in the picture and connects with intelligent cameras, home safety control platforms, and industrial fire early warning systems. When a flame is detected, it can quickly link with alarm devices (such as sound and light alarms, mobile phone push notifications), shortening the time to detect a fire and improving early fire warning and emergency response capabilities.
Test Results: As shown in the image, this template can accurately identify and locate the positions of multiple different fire sources and frame the complete size of the flames.
(6) Shelf Out-of-Stock Recognition
Template Introduction: This template is suitable for scenarios such as chain retail stores and convenience stores. It automatically identifies out-of-stock areas and empty product positions on shelves, assisting stores in achieving intelligent replenishment and optimized product display, thereby improving operational efficiency.
Test Results: As shown in the image, in the test scenario with multi-layer shelves and a wide variety of products, this template can accurately locate areas on the shelves where no products are placed (only empty pallets or empty slots remain). Even when products in adjacent slots have slight overflow, it can effectively distinguish between out-of-stock areas and normally displayed areas.
(7) Nut and Bolt Recognition
Template Introduction: This template is mainly used for the automatic recognition of nuts and bolts commonly found in mechanical equipment and structural parts. It is suitable for industrial testing scenarios such as production lines and equipment inspections, improving the operational safety of equipment and maintenance efficiency.
Test Results: As shown in the image, this template can accurately distinguish between nuts and bolts and locate their specific positions.
The above 7 custom templates cover multiple vertical fields including retail, agriculture, public health, construction sites, and fire safety. Each template has undergone simulated testing in real scenarios, demonstrating the core advantages of DINO-X custom templates in scenario adaptability and recognition accuracy. The stable performance and efficient recognition capabilities of the DINO-X model provide convenient and reliable visual capability support for refined recognition in vertical fields.
Reference Resources
1.CountAnything, an AI Intelligent Counting App : https://deepdataspace.com/products/countanything
2.DINO-X Template Market: https://cloud.deepdataspace.com/custom/market
3.Customize Exclusive Visual Templates on DINO-X Platform: https://cloud.deepdataspace.com/custom/template