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

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

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[object_detection_prediction, keypoint_detection_prediction, instance_segmentation_prediction, list_of_values]): 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"
}