In industry, UV filters are widely used in quality control processes. For example, in semiconductor manufacturing, UV filters are used to detect tiny flaws and contaminants on chip surfaces. In addition, these filters are used to check for unevenness and defects in coatings and plastic products to ensure product quality.

In the medical field, ultraviolet light is one of the most effective ways to kill viruses and bacteria. Meanwhile, in clinical diagnosis, UV filters help diagnostic equipment accurately observe the fluorescent response of microorganisms and other biomarkers in body fluid samples.

In summary, after understanding some basic information about UV bandpass filters, I believe you will have certain insights and judgments. Before making a choice for your application, you should be well prepared with a budget buying guide, which will help you make a decision faster.

Unlike narrowband filters, broadband UV filters allow a wider range of UV wavelengths to pass. These filters are used in applications that require a broader spectral response, such as solar UV research or environmental monitoring where multiple UV wavelengths are required to detect or measure various substances.

For specific applications, custom UV bandpass filters can be designed and manufactured to meet unique requirements. For example, Optolong Optics, their website not only provides a variety of optical filters, but also provides users with a variety of customized filters services to help users solve incompatibility issues.

A dual-band UV filter is a specialized filter capable of transmitting two different wavelength ranges, typically one in the UV and one in the visible or infrared spectrum. These filters can be used in multispectral imaging systems that need to capture images in ultraviolet and other wavelength bands simultaneously.

Florence-2 is a versatile Vision-Language Model (VLM), capable of handling multiple vision tasks within a single model. Its zero-shot capabilities are impressive across diverse tasks such as image captioning, object detection, segmentation and OCR. While Florence-2 performs well out-of-the-box, additional fine-tuning can further adapt the model to new tasks or improve its performance on unique, custom datasets.

Florence-2 can generate captions for specific regions of an image defined by bounding boxes. For this, it takes the bounding box location as input. You can extract the category with '' or a description with '' .

In this tutorial, we will use several auxiliary functions. The most important is the run_example core function, which generates a response from the Florence-2 model.

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Let’s read this article to explore the definition of UV bandpass filters and the unique advantages of its various types in order to choose the most suitable filter for a specific purpose.

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UV bandpass filters selectively transmit ultraviolet (UV) light while blocking other wavelengths of light. These filters enable precise control of UV radiation in a variety of applications. UV bandpass filters come in many types, each with its own unique materials and construction methods.

Florence-2 can generate image captions at various levels of detail, using the '' , '' or '' task prompts.

In recent years, the field of computer vision has witnessed the rise of foundation models that enable image annotation without the need for training custom models. We’ve seen models like CLIP [2] for classification, GroundingDINO [3] for object detection, and SAM [4] for segmentation — each excelling in its domain. But what if we had a single model capable of handling all these tasks together?

UV band filters are widely used in imaging technology and are particularly suitable for capturing the reflection or emission properties of UV light. UV filters in general photography are mainly used to block ultraviolet light below 400nm while allowing visible light between 400-700nm to pass through.

In forensic science, the use of UV filters can reveal traces of blood, body fluids, and steganography that are invisible under normal lighting.

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Despite its relatively small size, with versions of 0.23B & 0.77B parameters, Florence-2 achieves state-of-the-art (SOTA) performance. Its compact size enables efficient deployment on devices with limited computing resources, while ensuring fast inference speeds.

Florence-2 can also perform text-grounded object detection. By providing specific object names or descriptions as input, Florence-2 detects bounding boxes around the specified objects.

Astronomers use specific types of UV filters to study objects that primarily emit UV wavelengths, such as Optolong’s Venus-U filter, which can be used to observe Venus.

The model was pre-trained on an enormous, high quality dataset called FLD-5B, consisting of 5.4B annotations on 126 million images. This allows Florence-2 to excel in zero-shot performance on many tasks without requiring additional training.

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UV bandpass filters typically cover the UV spectrum from about 200 nm to 400 nm. Here are some common ranges in this UV spectrum:

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The model accurately describes the image and its surrounding. It even identifies the camera’s brand and model, demonstrating its OCR ability. However, in the '' task there are minor inconsistencies, which is expected from a zero-shot model.

Yes, UV bandpass filters can be customized to meet the specific requirements of an application. This includes adjustments to diameter, thickness, substrate material and spectral properties.

Inspired by Large Language Models (LLMs), Florence-2 was designed as a sequence-to-sequence model. It takes an image and text instructions as inputs, and outputs text results. The input or output text may represent plain text or a region in the image. The region format varies depending on the task:

UV bandpass filters are typically made from materials such as fused silica or borosilicate glass, which are chosen for their ability to withstand high-energy UV radiation and environmental durability.

These filters are characterized by their ability to provide a clear cutoff between the UV wavelengths transmitted and the visible light blocked. This steep transition is often applied in forensic analysis where all visible light must be eliminated to see only UV-induced fluorescence, or in systems where high optical performance and low light loss are required.

Florence-2 was released by Microsoft in June 2024. It was designed to perform multiple vision tasks within a single model. It is an open-source model, available on Hugging Face under the permissive MIT licence.

In this tutorial we introduce Florence-2 [1]— a novel, open-source Vision-Language Model (VLM) designed to handle a diverse range of vision and multimodal tasks, including captioning, object detection, segmentation and OCR.

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Florence-2 can perform a variety of visual tasks. Let’s explore some of its capabilities, starting with image captioning.

Florence-2 can identify densely packed regions in the image, and to provide their bounding box coordinates and their related labels or captions. To extract bounding boxes with labels, use the ’’task prompt:

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The run_example function combines the task prompt with any additional text input (if provided) into a single prompt. Using the processor, it generates text and image embeddings that serve as inputs to the model. The magic happens during the model.generate step, where the model’s response is generated. Here’s a breakdown of some key parameters:

Florence-2 can also generate segmentation polygons grounded by text ('') or by bounding boxes (''):

Narrowband UV filters transmit primarily a very narrow range of UV wavelengths while blocking other visible and infrared light. These filters are highly selective and ideal for scientific and medical applications where precise wavelength isolation is critical, such as fluorescence microscopy or analytical instruments that measure specific UV-absorbing compounds.

Florence-2 demonstrates strong OCR capabilities. It can extract text from an image with the '' task prompt, and extract both text and its location with '' :

Lastly, the model’s output is decoded and post-processed with processor.batch_decode and processor.post_process_generation to produce the final text response, which is returned by the run_example function.

There are many types of UV bandpass filters, each designed to meet the specific requirements of different applications. Here is a detailed description of some common types of UV bandpass filters:

But the core function of a UV bandpass filter is the ability to isolate specific UV wavelengths, which is useful in a variety of applications, from forensic analysis to sterilization processes.

For your convenience, I added a widget to the Colab notebook that enables you to draw a bounding box on the image, and code to convert it to Florence-2 format.

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In scientific research, UV filters are essential for observing substances that exhibit specific reactions under UV light. For example, in biological research, by using UV filters to observe the fluorescence response of biological samples, scientists can identify and track specific molecules and proteins within cells.

Additionally, we utilize auxiliary functions to visualize the results (draw_bbox ,draw_ocr_bboxes and draw_polygon) and handle the conversion between bounding boxes formats (convert_bbox_to_florence-2 and convert_florence-2_to_bbox). These can be explored in the attached Colab notebook.

If you don’t know how to choose and judge, you can contact our optolong professionals, who will give you detailed answers based on your needs.

Each type of UV bandpass filter serves a unique purpose and is optimized for different performance characteristics (such as bandwidth, transmission efficiency, and blocking capabilities) to effectively fit a variety of UV applications.

This type of bandpass filter not only helps observe celestial objects such as Venus, but also allows in-depth study of star formation and galaxy evolution in the universe by analyzing the ultraviolet radiation of these objects.

After installing and importing the necessary libraries (as demonstrated in the accompanying Colab notebook), we begin by loading the Florence-2 model, processor and the input image of a camera:

In this case, the '' identified the lens, while the '' was less specific. However, this performance may vary with different images.

A UV bandpass filter is a filter that transmits UV rays and blocks other wavelengths. These optical filters are widely used in scientific, industrial and medical applications. The transmittance of UV bandpass filters is generally 15-30%. Due to material reasons, most UV bandpass filters cannot be highly transparent.