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

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

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, instance_segmentation_prediction, list_of_values, 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"
}