Model Zoo For OpenCV DNN and Benchmarks.
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Updated
Nov 6, 2024 - Python
Model Zoo For OpenCV DNN and Benchmarks.
A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
Pre-trained Deep Learning models and demos (high quality and extremely fast)
A repository that implements some proposed methods from popular conference from scratch.
The NNEF Tools repository contains tools to generate and consume NNEF documents
AML's goal is to make benchmarking of various AI architectures on Ampere CPUs a pleasurable experience :)
The Model Zoo of Cognitive Diagnosis Models, including classic Item Response Ranking (IRT), Multidimensional Item Response Ranking (MIRT), Deterministic Input, Noisy "And" model(DINA), and advanced Fuzzy Cognitive Diagnosis Framework (FuzzyCDF), Neural Cognitive Diagnosis Model (NCDM) and Item Response Ranking framework (IRR).
RobustBench: a standardized adversarial robustness benchmark [NeurIPS 2021 Benchmarks and Datasets Track]
A library that includes Keras3 layers, blocks and models with pretrained weights, providing support for transfer learning, feature extraction, and more.
An official implementation of ProbeGen
An efficient PyTorch library for deep generative modeling.
Code Repository for the ICML 2024 paper: "Towards Scalable and Versatile Weight Space Learning".
NanoDet-Plusβ‘Super fast and lightweight anchor-free object detection model. π₯Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphoneπ₯
Tencent Pre-training framework in PyTorch & Pre-trained Model Zoo
Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo
[CVPR 2021] VirTex: Learning Visual Representations from Textual Annotations
Implementations of various Deep Learning models in PyTorch and TensorFlow.
π°π·νμ΄ν μΉμμ μ 곡νλ λͺ¨λΈ νλΈμ νκ΅μ΄ λ²μμ μν μ μ₯μμ λλ€. (Translate PyTorch model hub in Koreanπ°π·)
A computer vision playground to try and test end to end(test to deploy) computer vision pipeline.
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