Alfred

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Alfred-py is a deep learning utility library that provides massive utilities to boost daily efficiency for deep learning tasks. It can be called from the terminal as a tool or imported as an API.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is alfred?

Alfred-py is a comprehensive deep learning utility library designed to streamline various deep learning workflows. It offers a wide array of functionalities, including visualization tools for drawing boxes, masks, and 3D keypoints, command-line tools for viewing annotation data in multiple formats (YOLO, VOC, COCO), and deployment capabilities for TensorRT models. The library also provides common deep learning utilities, such as `torch.device()`, and renders 3D models. It supports various data operations like converting between YOLO, VOC, and COCO formats, merging datasets, and evaluating model performance with mAP calculations. Alfred-py is both a library for API calls and a terminal-based tool, making it versatile for developers and data scientists.

Best used for

Ideal for developers and data scientists who need to efficiently manage deep learning tasks, visualize complex data, and deploy models. Especially valuable for those working with various annotation formats (YOLO, VOC, COCO) and requiring robust evaluation tools for model performance.

Common actions

streamline deep learning

visualize data

convert data formats

deploy models

evaluate model performance

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Capabilities

Key features

Visualize annotations
Command line tools
Deploy TensorRT models
Convert data formats
Evaluate model performance
3D keypoint visualization

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What deep learning frameworks does alfred-py support?

Alfred-py provides utilities for common deep learning tasks, including `torch.device()` for PyTorch environments. It also supports deploying models to TensorRT, indicating compatibility with models convertible to that format. The library is designed to be flexible across various deep learning workflows.

Can alfred-py convert between different annotation formats?

Yes, alfred-py offers robust capabilities for converting between popular annotation formats. Users can convert YOLO to VOC, VOC to YOLO, VOC to COCO, and COCO to VOC. This feature is highly useful for managing diverse datasets and ensuring compatibility across different tools.

How does alfred-py help with model evaluation?

Alfred-py includes evaluation tools to calculate metrics like mAP for object detection models. It simplifies the process of evaluating performance by allowing users to provide ground truth and detection result directories, then automatically printing out recall, precision, and mAP results.

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AI Agents & Automation › AI Frameworks & Infra Productivity & Business › Workflow Automation

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