Building Blocks for Custom Vision Pipelines

Find and count the contours on an image.

Apply a threshold to an image.

Convert an RGB image to grayscale.

Apply a blur to an image.

Looks for instances of template in specific image

Apply SIFT to an image.

Compare SIFT descriptors from multiple images.

Count the number of pixels that match a specific color within a given tolerance.

Get the dominant color of an image in RGB format.

Merges detections made against multiple pieces of input image into single detection.

Add custom metadata to Roboflow Model Monitoring dashboard

Draws triangle markers on an image at specific coordinates based on provided detections.

Draws a polygon around detected objects in an image.

Pixelates detected objects in an image.

Paints a mask over detected objects in an image.

Draws labels on an image at specific coordinates based on provided detections.

Paints a halo around detected objects in an image.

Draws ellipses that highlight detected objects in an image.

Draws dots on an image at specific coordinates based on provided detections.

Draws scaled up crops of detections on the scene.

Draws the corners of detected objects in an image.

Paints a solid color on detected objects in an image.

Draws a circle around detected objects in an image.

Draws a box around detected objects in an image.

Blurs detected objects in an image.

Paints a mask over all areas outside of detected regions in an image.

Takes first non-empty data element or default

Collapses dimensionality level by aggregation of nested data into list

Define a variable from model predictions, such as the class names, confidences, or number of detections.

Replaces classes of detections with classes predicted by a chained classification model

Simplify polygons so they are geometrically convex and simplify them to contain only requested amount of vertices

Correct coordinates of detections from plane defined by given polygon to straight rectangular plane of given width and height

Save images and predictions in your Roboflow Dataset

Apply transformations on detected bounding boxes.

Conditionally filter out model predictions.

Crop an image using bounding boxes from a detection model.

Detect and read QR codes in an image.

Detect and read barcodes in an image.

Predict the location of objects with bounding boxes.

Apply multiple tags to an image.

Apply a single tag to an image.

Predict skeletons on objects.

Predict the shape, size, and location of objects.

Run a zero-shot object detection model.

Run OpenAI's GPT-4 with Vision

Convert bounding boxes to polygons, or run SAM2 on an entire image to generate a mask.

Continue a branch of logic if a condition is met.

Evaluate a condition and return a response.

Use SAM 2 to segment objects in an image.

Use SAHI to run sliced inference with a computer vision model.

Combine predictions from multiple detections models to make a decision about object presence.

Convert object detection predictions into serialized format, facilitating data transfer or storage within workflow pipelines.

This block sends email alerts with images and predictions.

Send images and prediction files to AWS S3.

Send SMS notifications through Twilio based on configurable conditions, utilizing dynamic message content and sender/receiver details.

Publish serialized data payloads to an MQTT broker, handling connection management and error reporting.

Securely transmit data to specified webhooks using HMAC-SHA512 for payload authentication, ensuring data integrity and confidentiality.

Enhance images with visual annotations based on detection predictions, combining bounding boxes and labels for clarity.

Tag batches of images with boolean flags indicating the presence or absence of predictions for various detection types.

Monitor and trigger an alarm if the count of specified prediction classes within a defined timeframe exceeds a set threshold.

Conditionally filter and output data fields based on boolean conditions from a preceding workflow step.

Send notifications to a specified Slack channel based on conditions, using customizable message content and Slack API tokens.

Export prediction data to a CSV file for various prediction kinds, including object detection and instance segmentation.

Publish data to a Kafka topic, with server connection and topic configuration, ensuring data propagation within distributed systems.

Trigger alerts on PagerDuty based on configurable conditions, sending notifications with customized details about system events.

Retrieve and manage secrets from AWS Secrets Manager for use in workflow automation.

Randomly sample images based on a specified percentage, triggering actions for selected images within workflow processes.

Track and update object positions across video frames using ByteTrack.

GPT-4 with Vision is a type of LMM.

CogVLM is a type of LMM.

YOLOv8 Classification is a type of Roboflow Classification Model.

YOLOv8 Keypoint Detection is a type of Roboflow Keypoint Detection Model.

YOLOv7 Instance Segmentation is a type of Roboflow Instance Segmentation Model.

YOLOv8 Instance Segmentation is a type of Roboflow Instance Segmentation Model.

YOLOv5 Instance Segmentation is a type of Roboflow Instance Segmentation Model.

YOLOv5 is a type of Roboflow Object Detection Model.

YOLO-NAS is a type of Roboflow Object Detection Model.

YOLOv9 is a type of Roboflow Object Detection Model.

YOLOv8 is a type of Roboflow Object Detection Model.

Use relative coordinates for cropping.

Apply a fixed offset on the width and height of detections.

Filter predictions from detection models based on defined conditions.

Create dynamic crops from a detections model.

Use absolute coordinates for cropping.

Collect data and predictions that flow through workflows for use in active learning.

Detect the location of QR codes in an image.

Run Optical Character Recognition on a model.

Detect objects using an object detection model.

Run a multi-label classification model.

Run a classification model.

Run inference on a keypoint detection model.

Run an instance segmentation model.

Run a zero-shot object detection model.

Run Optical Character Recognition on a model.

Run a large language model for classification.

Run a large language model.

Compare CLIP image and text embeddings.

Combine predictions from multiple detections models to make a decision about object presence.

Control the flow of a workflow based on the result of a step.