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

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

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[list_of_values, object_detection_prediction, instance_segmentation_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"
}