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# Vision

[Beginner](/levels/beginner/index.md) [Intermediate](/levels/intermediate/index.md) [Advanced](/levels/advanced/index.md) [Expert](/levels/expert/index.md)

45 resources · page 2 of 2

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### [EfficientNet](/foundations/llm-fundamentals/efficientnet/index.md)

Creator not supplied; see original source.

ICML 2019 paper (PDF) EfficientNet: scaling convolutional networks by depth, width and resolution.

Advanced–Expert · unknown · unknown resource · und 

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### [Faster R-CNN](/foundations/llm-fundamentals/faster-r-cnn/index.md)

Creator not supplied; see original source.

NeurIPS paper (PDF, 2015) Faster R-CNN: real-time object detection with region proposal networks.

Advanced–Expert · unknown · unknown resource · zh 

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### [Inception / GoogLeNet](/foundations/llm-fundamentals/inception-googlenet/index.md)

Creator not supplied; see original source.

arXiv PDF of the GoogLeNet paper (2014) introducing the Inception architecture.

Advanced–Expert · unknown · unknown resource · und 

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### [Mask R-CNN](/foundations/llm-fundamentals/mask-r-cnn/index.md)

Creator not supplied; see original source.

arXiv paper (2017) "Mask R-CNN", introducing Mask R-CNN for instance segmentation.

Advanced–Expert · unknown · unknown resource · en 

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### [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](/foundations/llm-fundamentals/mobilenet/index.md)

Creator not supplied; see original source.

arXiv PDF of the MobileNets paper (2017) on efficient convolutional networks for mobile vision.

Advanced–Expert · unknown · unknown resource · und 

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### [Moirai](/foundations/llm-fundamentals/moirai/index.md)

Creator not supplied; see original source.

arXiv PDF of the Moirai paper (2024), Salesforce's universal time-series forecasting transformer.

Advanced–Expert · unknown · unknown resource · und 

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### [ResNet](/foundations/llm-fundamentals/resnet/index.md)

Creator not supplied; see original source.

arXiv paper (2015) "Deep Residual Learning for Image Recognition", introducing ResNet residual networks.

Advanced–Expert · unknown · unknown resource · en 

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### [SAM](/foundations/llm-fundamentals/sam/index.md)

Creator not supplied; see original source.

arXiv paper (2023) introducing Segment Anything (SAM), a promptable image segmentation model and dataset.

Advanced–Expert · unknown · unknown resource · en 

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### [A decoder-only foundation model for time-series forecasting (TimesFM)](/foundations/llm-fundamentals/timesfm-2/index.md)

Creator not supplied; see original source.

arXiv paper (2023) introducing TimesFM, a decoder-only foundation model for time-series forecasting.

Advanced–Expert · unknown · unknown resource · en 

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### [U-Net](/foundations/llm-fundamentals/u-net/index.md)

Creator not supplied; see original source.

arXiv paper (2015) "U-Net: Convolutional Networks for Biomedical Image Segmentation", introducing the encoder-decoder architecture with skip connections.

Advanced–Expert · unknown · unknown resource · en 

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### [VGG](/foundations/llm-fundamentals/vgg/index.md)

Creator not supplied; see original source.

arXiv PDF of the VGG paper (2014) on very deep convolutional networks for large-scale image recognition.

Advanced–Expert · unknown · unknown resource · und 

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### [ViT](/foundations/llm-fundamentals/vit/index.md)

Creator not supplied; see original source.

arXiv paper (2020) introducing the Vision Transformer (ViT) for image recognition.

Advanced–Expert · unknown · unknown resource · en 

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### [YOLO](/foundations/llm-fundamentals/yolo/index.md)

Creator not supplied; see original source.

arXiv paper (2015) "You Only Look Once: Unified, Real-Time Object Detection", introducing YOLO, a single-stage real-time object detector.

Advanced · unknown · unknown resource · en 

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