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

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

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[instance_segmentation_prediction, object_detection_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"
}