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

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

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[keypoint_detection_prediction, list_of_values, object_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"
}