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

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

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"
}