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

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

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, list_of_values, keypoint_detection_prediction, instance_segmentation_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"
}