vision: 44 curated resources
vision
44 resources · page 1 of 2
AI in the Sciences and Engineering
by Computational and Applied Mathematics Laboratory @ ETH Zurich
ETH CAMLab AI_Science_Engineering GitHub repo: course materials for AI in the Sciences and Engineering.
AirfRANS
by SimAI
AirfRANS GitHub repo: dataset and scripts for ML surrogates of RANS airfoil flow simulations.
APEBench
by TUM Physics-based Simulation
TUM APEBench GitHub repo (NeurIPS 2024): benchmark suite for autoregressive neural PDE emulators.
Introducing: Be My AI (Be My Eyes)
by Be My Eyes
Be My Eyes announcement of Be My AI, a GPT-4-based visual assistant that describes images for blind and low-vision users.
Convolutional Neural Operator
by Computational and Applied Mathematics Laboratory @ ETH Zurich
ETH CAMLab Convolutional Neural Operator GitHub repo: official implementation for learning PDE solution operators.
DiffEqFlux.jl
by SciML Open Source Scientific Machine Learning
SciML DiffEqFlux.jl GitHub repo: neural differential equation layers in Julia.
facebookresearch/dinov2
Meta DINOv2 GitHub repo: PyTorch code and models for self-supervised visual features.
Distill (archive)
by Distill
Distill: archived journal of interactive, visual machine learning research articles.
InternVL
by OpenGVLab
OpenGVLab InternVL GitHub repo: open-source multimodal model family with code and weights.
MBZUAI ORYX
by ORYX
MBZUAI ORYX GitHub organisation: code for vision-language models and multimodal evaluation.
NeuralPDE.jl
by SciML Open Source Scientific Machine Learning
SciML NeuralPDE.jl GitHub repo: physics-informed neural network solvers for PDEs in Julia.
Qwen3-VL
by Qwen
QwenLM Qwen3-VL GitHub repo: multimodal large language model series with usage code.
SciML Book (MIT 18.337)
by Chris Rackauckas (MIT 18.337)
SciML Book: lecture notes from MIT 18.337 on parallel computing and scientific machine learning.
Solver-in-the-Loop
by TUM Physics-based Simulation
TUM Solver-in-the-Loop GitHub repo: learning from differentiable physics to correct iterative PDE solvers.
pytorch-image-models (timm)
by Hugging Face
Hugging Face timm GitHub repo: PyTorch image models, encoders and training scripts.
Ultralytics YOLO
by Ultralytics
Ultralytics GitHub repo: YOLO models for object detection, segmentation and pose estimation.
VinAI
by VinAI
VinAI site: Vietnamese company doing AI research and building applied AI products.
WGA: AI provisions of the 2023 MBA
by Writers Guild of America West
WGA page on artificial intelligence provisions in the 2023 Minimum Basic Agreement.
ImageNet Classification with Deep Convolutional Neural Networks (NeurIPS Proceedings)
by Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton (University of Toronto)
AlexNet paper (NIPS 2012) by Krizhevsky, Sutskever and Hinton: deep convolutional network for ImageNet classification.
NYU Deep Learning (LeCun/Canziani)
NYU Deep Learning course (Spring 2021) by Yann LeCun and Alfredo Canziani, with lectures, labs and notebooks.
PDE-Transformer
by TUM Physics-based Simulation
PDE-Transformer project page: transformer architecture for learning partial differential equations from data.
Physics-based Deep Learning (TUM)
by Nils Thuerey et al. (TU Munich)
Physics-based Deep Learning book from TUM: hands-on guide to combining physical simulation with deep learning.
PointNet
by Charles R. Qi et al. (Stanford University)
PointNet project page: deep network architecture that consumes raw point clouds for 3D classification and segmentation.
Roboflow documentation
by Roboflow
Roboflow documentation for building computer-vision datasets, training models and deploying them.
UvA Deep Learning tutorials
by Phillip Lippe (University of Amsterdam)
University of Amsterdam deep learning tutorial notebooks in PyTorch and JAX/Flax, covering core architectures.
ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)
by Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton (University of Toronto)
NeurIPS paper (PDF, 2012) AlexNet: ImageNet classification with deep convolutional neural networks.
Chronos: Learning the Language of Time Series
by Abdul Fatir Ansari et al. (Amazon)
arXiv paper (2024) introducing Chronos, pretrained time-series forecasting models using language-model tokenization.
CLIP
by Alec Radford et al. (OpenAI)
arXiv paper (2021) introducing CLIP, contrastive language-image pretraining for transferable visual models.
DenseNet
by Gao Huang et al. (Cornell University)
arXiv paper (2016) "Densely Connected Convolutional Networks", introducing DenseNet, where each layer receives feature maps from all earlier layers.
DETR
by Nicolas Carion et al. (Meta)
arXiv HTML version of the DETR paper (2020) on end-to-end object detection with transformers.
DINOv2: Learning Robust Visual Features without Supervision
by Maxime Oquab et al. (Meta AI)
arXiv HTML version of the DINOv2 paper (2023) on self-supervised visual features.
EfficientNet
by Mingxing Tan and Quoc V. Le (Google Brain)
ICML 2019 paper (PDF) EfficientNet: scaling convolutional networks by depth, width and resolution.