Zero-Shot Prediction Plugin for FiftyOne

• twilio-automation-plugin

  3 2 6.2 Python

  Automate data ingestion into FiftyOne with Twilio

Week 1: 🎨 AI Art Gallery & Twilio Automation

• ten-weeks-of-plugins

  4 14 7.5

  My journey during 10 weeks of building FiftyOne plugins

def _execution_mode(ctx, inputs): delegate = ctx.params.get("delegate", False) if delegate: description = "Uncheck this box to execute the operation immediately" else: description = "Check this box to delegate execution of this task" inputs.bool( "delegate", default=False, required=True, label="Delegate execution?", description=description, view=types.CheckboxView(), ) if delegate: inputs.view( "notice", types.Notice( label=( "You've chosen delegated execution. Note that you must " "have a delegated operation service running in order for " "this task to be processed. See " "https://docs.voxel51.com/plugins/index.html#operators " "for more information" ) ), ) def resolve_delegation(self, ctx): return ctx.params.get("delegate", False)

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• zero-shot-prediction-plugin

  3 24 7.6 Python

  Run zero-shot prediction models on your data

fiftyone plugins download https://github.com/jacobmarks/zero-shot-prediction-plugin

• super-gradients

  8 4,322 9.6 Jupyter Notebook

  Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.

  

Most computer vision models are trained to predict on a preset list of label classes. In object detection, for instance, many of the most popular models like YOLOv8 and YOLO-NAS are pretrained with the classes from the MS COCO dataset. If you download the weights checkpoints for these models and run prediction on your dataset, you will generate object detection bounding boxes for the 80 COCO classes.

• CLIP

  103 22,051 1.2 Jupyter Notebook

  CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

  

In computer vision, this is known as zero-shot learning, or zero-shot prediction, because the goal is to generate predictions without explicitly being given any example predictions to learn from. With the advent of high quality multimodal models like CLIP and foundation models like Segment Anything, it is now possible to generate remarkably good zero-shot predictions for a variety of computer vision tasks, including:

• segment-anything

  56 44,026 4.2 Jupyter Notebook

  The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

  

In computer vision, this is known as zero-shot learning, or zero-shot prediction, because the goal is to generate predictions without explicitly being given any example predictions to learn from. With the advent of high quality multimodal models like CLIP and foundation models like Segment Anything, it is now possible to generate remarkably good zero-shot predictions for a variety of computer vision tasks, including:

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