Florence-2 Model

v2

Class: Florence2BlockV2 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.foundation.florence2.v2.Florence2BlockV2

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

Dedicated inference server required (GPU recommended) - you may want to use dedicated deployment

This Workflow block introduces Florence 2, a Visual Language Model (VLM) capable of performing a wide range of tasks, including:

• Object Detection

• Instance Segmentation

• Image Captioning

• Optical Character Recognition (OCR)

• and more...

Below is a comprehensive list of tasks supported by the model, along with descriptions on how to utilize their outputs within the Workflows ecosystem:

Task Descriptions:

• Custom Prompt (custom) - Use free-form prompt to generate a response. Useful with finetuned models.

• Text Recognition (OCR) (ocr) - Model recognizes text in the image

• Text Detection & Recognition (OCR) (ocr-with-text-detection) - Model detects text regions in the image, and then performs OCR on each detected region

• Captioning (short) (caption) - Model provides a short description of the image

• Captioning (detailed-caption) - Model provides a long description of the image

• Captioning (long) (more-detailed-caption) - Model provides a very long description of the image

• Unprompted Object Detection (object-detection) - Model detects and returns the bounding boxes for prominent objects in the image

• Object Detection (open-vocabulary-object-detection) - Model detects and returns the bounding boxes for the provided classes

• Detection & Captioning (object-detection-and-caption) - Model detects prominent objects and captions them

• Prompted Object Detection (phrase-grounded-object-detection) - Based on the textual prompt, model detects objects matching the descriptions

• Prompted Instance Segmentation (phrase-grounded-instance-segmentation) - Based on the textual prompt, model segments objects matching the descriptions

• Segment Bounding Box (detection-grounded-instance-segmentation) - Model segments the object in the provided bounding box into a polygon

• Classification of Bounding Box (detection-grounded-classification) - Model classifies the object inside the provided bounding box

• Captioning of Bounding Box (detection-grounded-caption) - Model captions the object in the provided bounding box

• Text Recognition (OCR) for Bounding Box (detection-grounded-ocr) - Model performs OCR on the text inside the provided bounding box

• Regions of Interest proposal (region-proposal) - Model proposes Regions of Interest (Bounding Boxes) in the image

Type identifier

Use the following identifier in step "type" field: roboflow_core/florence_2@v2to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
task_type	str	Task type to be performed by model. Value determines required parameters and output response..	❌
prompt	str	Text prompt to the Florence-2 model.	✅
classes	List[str]	List of classes to be used.	✅
grounding_detection	Optional[List[float], List[int]]	Detection to ground Florence-2 model. May be statically provided bounding box [left_top_x, left_top_y, right_bottom_x, right_bottom_y] or result of object-detection model. If the latter is true, one box will be selected based on grounding_selection_mode..	✅
grounding_selection_mode	str	.	❌
model_id	str	Model to be used.	✅

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to Florence-2 Model in version v2.

• inputs: Mask Visualization, Classification Label Visualization, Instance Segmentation Model, Detections Consensus, Detections Merge, Webhook Sink, Multi-Label Classification Model, Email Notification, QR Code Generator, VLM As Detector, LMM, SAM 3, Detection Offset, Corner Visualization, Image Convert Grayscale, Stability AI Outpainting, Segment Anything 2 Model, Halo Visualization, Object Detection Model, Trace Visualization, Google Vision OCR, Clip Comparison, Instance Segmentation Model, CSV Formatter, Text Display, Stitch Images, Google Gemini, Local File Sink, Slack Notification, VLM As Classifier, Roboflow Dataset Upload, PTZ Tracking (ONVIF).md), Color Visualization, Dot Visualization, Polygon Visualization, Object Detection Model, Anthropic Claude, Buffer, Byte Tracker, Contrast Equalization, Detections Classes Replacement, Dimension Collapse, Velocity, Moondream2, Halo Visualization, Florence-2 Model, Blur Visualization, Label Visualization, Twilio SMS/MMS Notification, Ellipse Visualization, OpenAI, SIFT, Model Monitoring Inference Aggregator, Single-Label Classification Model, Detections List Roll-Up, OpenAI, Image Threshold, Background Color Visualization, Model Comparison Visualization, Size Measurement, OpenAI, Keypoint Detection Model, Gaze Detection, Polygon Visualization, Twilio SMS Notification, SAM 3, Bounding Box Visualization, OCR Model, Overlap Filter, Icon Visualization, Time in Zone, Google Gemini, Florence-2 Model, Roboflow Dataset Upload, Anthropic Claude, Dynamic Zone, Dynamic Crop, VLM As Detector, Google Gemini, Path Deviation, Image Blur, Line Counter, Byte Tracker, Stability AI Inpainting, Template Matching, Image Contours, Path Deviation, Morphological Transformation, Triangle Visualization, Bounding Rectangle, Detections Stitch, Relative Static Crop, Detections Filter, Camera Calibration, Grid Visualization, Detections Stabilizer, Camera Focus, Image Slicer, Detections Combine, LMM For Classification, Line Counter Visualization, Llama 3.2 Vision, Keypoint Detection Model, SIFT Comparison, Camera Focus, Time in Zone, Background Subtraction, Image Slicer, Circle Visualization, Seg Preview, Clip Comparison, Email Notification, Byte Tracker, Image Preprocessing, SAM 3, Depth Estimation, Time in Zone, CogVLM, Absolute Static Crop, Roboflow Custom Metadata, EasyOCR, Stitch OCR Detections, Perspective Correction, Anthropic Claude, Pixelate Visualization, Stability AI Image Generation, Reference Path Visualization, Keypoint Visualization, Detection Event Log, Polygon Zone Visualization, YOLO-World Model, Stitch OCR Detections, Crop Visualization, Motion Detection, OpenAI, Detections Transformation
• outputs: Mask Visualization, Classification Label Visualization, Instance Segmentation Model, Detections Consensus, Webhook Sink, Email Notification, QR Code Generator, VLM As Detector, LMM, SAM 3, Corner Visualization, Stability AI Outpainting, Segment Anything 2 Model, Halo Visualization, JSON Parser, Object Detection Model, Trace Visualization, Google Vision OCR, Instance Segmentation Model, Clip Comparison, Text Display, Google Gemini, Slack Notification, Local File Sink, VLM As Classifier, Roboflow Dataset Upload, PTZ Tracking (ONVIF).md), Color Visualization, Dot Visualization, Polygon Visualization, Object Detection Model, Anthropic Claude, Buffer, Contrast Equalization, Detections Classes Replacement, Perception Encoder Embedding Model, Moondream2, Halo Visualization, Florence-2 Model, Twilio SMS/MMS Notification, Label Visualization, Ellipse Visualization, OpenAI, Model Monitoring Inference Aggregator, Detections List Roll-Up, OpenAI, Image Threshold, Model Comparison Visualization, Background Color Visualization, Size Measurement, OpenAI, Keypoint Detection Model, Twilio SMS Notification, Polygon Visualization, SAM 3, Bounding Box Visualization, Icon Visualization, Time in Zone, Google Gemini, Florence-2 Model, Roboflow Dataset Upload, Anthropic Claude, Dynamic Crop, CLIP Embedding Model, VLM As Detector, Google Gemini, Path Deviation, Image Blur, Line Counter, Cache Set, Stability AI Inpainting, Path Deviation, Morphological Transformation, Triangle Visualization, Detections Stitch, Grid Visualization, Llama 3.2 Vision, Line Counter Visualization, LMM For Classification, Keypoint Detection Model, Distance Measurement, SIFT Comparison, Time in Zone, Circle Visualization, Seg Preview, Clip Comparison, Email Notification, Image Preprocessing, SAM 3, Depth Estimation, Cache Get, Line Counter, Time in Zone, CogVLM, Roboflow Custom Metadata, Stitch OCR Detections, Perspective Correction, Anthropic Claude, Stability AI Image Generation, Reference Path Visualization, Keypoint Visualization, VLM As Classifier, Polygon Zone Visualization, YOLO-World Model, Stitch OCR Detections, Crop Visualization, Pixel Color Count, Motion Detection, OpenAI

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Florence-2 Model in version v2 has.

Bindings

• input

  • images (image): The image to infer on..
  • prompt (string): Text prompt to the Florence-2 model.
  • classes (list_of_values): List of classes to be used.
  • grounding_detection (Union[instance_segmentation_prediction, list_of_values, object_detection_prediction, keypoint_detection_prediction]): Detection to ground Florence-2 model. May be statically provided bounding box [left_top_x, left_top_y, right_bottom_x, right_bottom_y] or result of object-detection model. If the latter is true, one box will be selected based on grounding_selection_mode..
  • model_id (roboflow_model_id): Model to be used.

• output

  • raw_output (Union[string, language_model_output]): String value if string or LLM / VLM output if language_model_output.
  • parsed_output (dictionary): Dictionary.
  • classes (list_of_values): List of values of any type.

Example JSON definition of step Florence-2 Model in version v2

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/florence_2@v2",
    "images": "$inputs.image",
    "task_type": "<block_does_not_provide_example>",
    "prompt": "my prompt",
    "classes": [
        "class-a",
        "class-b"
    ],
    "grounding_detection": "$steps.detection.predictions",
    "grounding_selection_mode": "first",
    "model_id": "florence-2-base"
}

v1

Class: Florence2BlockV1 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.foundation.florence2.v1.Florence2BlockV1

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

Dedicated inference server required (GPU recommended) - you may want to use dedicated deployment

This Workflow block introduces Florence 2, a Visual Language Model (VLM) capable of performing a wide range of tasks, including:

• Object Detection

• Instance Segmentation

• Image Captioning

• Optical Character Recognition (OCR)

• and more...

Below is a comprehensive list of tasks supported by the model, along with descriptions on how to utilize their outputs within the Workflows ecosystem:

Task Descriptions:

• Custom Prompt (custom) - Use free-form prompt to generate a response. Useful with finetuned models.

• Text Recognition (OCR) (ocr) - Model recognizes text in the image

• Text Detection & Recognition (OCR) (ocr-with-text-detection) - Model detects text regions in the image, and then performs OCR on each detected region

• Captioning (short) (caption) - Model provides a short description of the image

• Captioning (detailed-caption) - Model provides a long description of the image

• Captioning (long) (more-detailed-caption) - Model provides a very long description of the image

• Unprompted Object Detection (object-detection) - Model detects and returns the bounding boxes for prominent objects in the image

• Object Detection (open-vocabulary-object-detection) - Model detects and returns the bounding boxes for the provided classes

• Detection & Captioning (object-detection-and-caption) - Model detects prominent objects and captions them

• Prompted Object Detection (phrase-grounded-object-detection) - Based on the textual prompt, model detects objects matching the descriptions

• Prompted Instance Segmentation (phrase-grounded-instance-segmentation) - Based on the textual prompt, model segments objects matching the descriptions

• Segment Bounding Box (detection-grounded-instance-segmentation) - Model segments the object in the provided bounding box into a polygon

• Classification of Bounding Box (detection-grounded-classification) - Model classifies the object inside the provided bounding box

• Captioning of Bounding Box (detection-grounded-caption) - Model captions the object in the provided bounding box

• Text Recognition (OCR) for Bounding Box (detection-grounded-ocr) - Model performs OCR on the text inside the provided bounding box

• Regions of Interest proposal (region-proposal) - Model proposes Regions of Interest (Bounding Boxes) in the image

Type identifier

Use the following identifier in step "type" field: roboflow_core/florence_2@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
task_type	str	Task type to be performed by model. Value determines required parameters and output response..	❌
prompt	str	Text prompt to the Florence-2 model.	✅
classes	List[str]	List of classes to be used.	✅
grounding_detection	Optional[List[float], List[int]]	Detection to ground Florence-2 model. May be statically provided bounding box [left_top_x, left_top_y, right_bottom_x, right_bottom_y] or result of object-detection model. If the latter is true, one box will be selected based on grounding_selection_mode..	✅
grounding_selection_mode	str	.	❌
model_version	str	Model to be used.	✅

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to Florence-2 Model in version v1.

• inputs: Mask Visualization, Classification Label Visualization, Instance Segmentation Model, Detections Consensus, Detections Merge, Webhook Sink, Multi-Label Classification Model, Email Notification, QR Code Generator, VLM As Detector, LMM, SAM 3, Detection Offset, Corner Visualization, Image Convert Grayscale, Stability AI Outpainting, Segment Anything 2 Model, Halo Visualization, Object Detection Model, Trace Visualization, Google Vision OCR, Clip Comparison, Instance Segmentation Model, CSV Formatter, Text Display, Stitch Images, Google Gemini, Local File Sink, Slack Notification, VLM As Classifier, Roboflow Dataset Upload, PTZ Tracking (ONVIF).md), Color Visualization, Dot Visualization, Polygon Visualization, Object Detection Model, Anthropic Claude, Buffer, Byte Tracker, Contrast Equalization, Detections Classes Replacement, Dimension Collapse, Velocity, Moondream2, Halo Visualization, Florence-2 Model, Blur Visualization, Label Visualization, Twilio SMS/MMS Notification, Ellipse Visualization, OpenAI, SIFT, Model Monitoring Inference Aggregator, Single-Label Classification Model, Detections List Roll-Up, OpenAI, Image Threshold, Background Color Visualization, Model Comparison Visualization, Size Measurement, OpenAI, Keypoint Detection Model, Gaze Detection, Polygon Visualization, Twilio SMS Notification, SAM 3, Bounding Box Visualization, OCR Model, Overlap Filter, Icon Visualization, Time in Zone, Google Gemini, Florence-2 Model, Roboflow Dataset Upload, Anthropic Claude, Dynamic Zone, Dynamic Crop, VLM As Detector, Google Gemini, Path Deviation, Image Blur, Line Counter, Byte Tracker, Stability AI Inpainting, Template Matching, Image Contours, Path Deviation, Morphological Transformation, Triangle Visualization, Bounding Rectangle, Detections Stitch, Relative Static Crop, Detections Filter, Camera Calibration, Grid Visualization, Detections Stabilizer, Camera Focus, Image Slicer, Detections Combine, LMM For Classification, Line Counter Visualization, Llama 3.2 Vision, Keypoint Detection Model, SIFT Comparison, Camera Focus, Time in Zone, Background Subtraction, Image Slicer, Circle Visualization, Seg Preview, Clip Comparison, Email Notification, Byte Tracker, Image Preprocessing, SAM 3, Depth Estimation, Time in Zone, CogVLM, Absolute Static Crop, Roboflow Custom Metadata, EasyOCR, Stitch OCR Detections, Perspective Correction, Anthropic Claude, Pixelate Visualization, Stability AI Image Generation, Reference Path Visualization, Keypoint Visualization, Detection Event Log, Polygon Zone Visualization, YOLO-World Model, Stitch OCR Detections, Crop Visualization, Motion Detection, OpenAI, Detections Transformation
• outputs: Mask Visualization, Classification Label Visualization, Instance Segmentation Model, Detections Consensus, Webhook Sink, Email Notification, QR Code Generator, VLM As Detector, LMM, SAM 3, Corner Visualization, Stability AI Outpainting, Segment Anything 2 Model, Halo Visualization, JSON Parser, Object Detection Model, Trace Visualization, Google Vision OCR, Instance Segmentation Model, Clip Comparison, Text Display, Google Gemini, Slack Notification, Local File Sink, VLM As Classifier, Roboflow Dataset Upload, PTZ Tracking (ONVIF).md), Color Visualization, Dot Visualization, Polygon Visualization, Object Detection Model, Anthropic Claude, Buffer, Contrast Equalization, Detections Classes Replacement, Perception Encoder Embedding Model, Moondream2, Halo Visualization, Florence-2 Model, Twilio SMS/MMS Notification, Label Visualization, Ellipse Visualization, OpenAI, Model Monitoring Inference Aggregator, Detections List Roll-Up, OpenAI, Image Threshold, Model Comparison Visualization, Background Color Visualization, Size Measurement, OpenAI, Keypoint Detection Model, Twilio SMS Notification, Polygon Visualization, SAM 3, Bounding Box Visualization, Icon Visualization, Time in Zone, Google Gemini, Florence-2 Model, Roboflow Dataset Upload, Anthropic Claude, Dynamic Crop, CLIP Embedding Model, VLM As Detector, Google Gemini, Path Deviation, Image Blur, Line Counter, Cache Set, Stability AI Inpainting, Path Deviation, Morphological Transformation, Triangle Visualization, Detections Stitch, Grid Visualization, Llama 3.2 Vision, Line Counter Visualization, LMM For Classification, Keypoint Detection Model, Distance Measurement, SIFT Comparison, Time in Zone, Circle Visualization, Seg Preview, Clip Comparison, Email Notification, Image Preprocessing, SAM 3, Depth Estimation, Cache Get, Line Counter, Time in Zone, CogVLM, Roboflow Custom Metadata, Stitch OCR Detections, Perspective Correction, Anthropic Claude, Stability AI Image Generation, Reference Path Visualization, Keypoint Visualization, VLM As Classifier, Polygon Zone Visualization, YOLO-World Model, Stitch OCR Detections, Crop Visualization, Pixel Color Count, Motion Detection, OpenAI

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Florence-2 Model in version v1 has.

Bindings

• input

  • images (image): The image to infer on..
  • prompt (string): Text prompt to the Florence-2 model.
  • classes (list_of_values): List of classes to be used.
  • grounding_detection (Union[instance_segmentation_prediction, list_of_values, object_detection_prediction, keypoint_detection_prediction]): Detection to ground Florence-2 model. May be statically provided bounding box [left_top_x, left_top_y, right_bottom_x, right_bottom_y] or result of object-detection model. If the latter is true, one box will be selected based on grounding_selection_mode..
  • model_version (string): Model to be used.

• output

  • raw_output (Union[string, language_model_output]): String value if string or LLM / VLM output if language_model_output.
  • parsed_output (dictionary): Dictionary.
  • classes (list_of_values): List of values of any type.

Example JSON definition of step Florence-2 Model in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/florence_2@v1",
    "images": "$inputs.image",
    "task_type": "<block_does_not_provide_example>",
    "prompt": "my prompt",
    "classes": [
        "class-a",
        "class-b"
    ],
    "grounding_detection": "$steps.detection.predictions",
    "grounding_selection_mode": "first",
    "model_version": "florence-2-base"
}