Beetle Eye
Beetle Eye is an open-source computer vision library for building machine learning models. It provides pre-trained models and datasets for image classification, object detection, and semantic segmentation. The library is lightweight, customizable, and supports distributed trainin
opensource
machine-learning
image-classification
object-detection
semantic-segmentation
pretrained-models
datasets
lightweight
customizable
distributed-training
Beetle Eye: Open-Source Computer Vision Library
An open-source computer vision library for building machine learning models, providing pre-trained models and datasets for image classification, object detection, and semantic segmentation.
What is Beetle Eye?
Beetle Eye is an open-source computer vision library designed to make building machine learning models for computer vision tasks easy and accessible. It provides pre-trained models, datasets, and tools to help developers and researchers hit the ground running.
Some key features and benefits of Beetle Eye include:
- Lightweight and modular architecture - Only includes what you need for your specific computer vision task
- Pre-trained models - Comes with accuracy-focused models pre-trained on common datasets like ImageNet, COCO, Pascal VOC
- Datasets - Provides popular datasets used to train computer vision models
- Customizable - Models can be further trained and customized for your specific use case
- Supports multi-GPU and distributed training for scalability
- Active open source community behind ongoing development and maintenance
With its commitment to performance, customizability, and community support, Beetle Eye aims to make developing production-ready computer vision capabilities accessible to more developers and organizations.
Beetle Eye Features
Features
- Pre-trained computer vision models
- Image classification
- Object detection
- Semantic segmentation
- Lightweight and customizable
- Supports distributed training
Pricing
- Open Source
Pros
Open source and free to use
Pre-trained models allow quick prototyping
Good performance with lightweight models
Active community support
Cons
Limited model selection compared to larger frameworks
Less flexibility than coding a model from scratch
Requires some machine learning knowledge to use effectively
Official Links
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