AML's goal is to make benchmarking of various AI architectures on Ampere CPUs a pleasurable experience :)
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Updated
May 22, 2024 - Python
AML's goal is to make benchmarking of various AI architectures on Ampere CPUs a pleasurable experience :)
Discover pretrained models for deep learning in MATLAB
The NNEF Tools repository contains tools to generate and consume NNEF documents
A library that includes Keras3 layers, blocks and models with pretrained weights, providing support for transfer learning, feature extraction, and more.
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.
Model Zoo For OpenCV DNN and Benchmarks.
Pre-trained Deep Learning models and demos (high quality and extremely fast)
Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo
Tencent Pre-training framework in PyTorch & Pre-trained Model Zoo
RobustBench: a standardized adversarial robustness benchmark [NeurIPS'21 Benchmarks and Datasets Track]
Please do not feed the models
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).
NanoDet-Plusβ‘Super fast and lightweight anchor-free object detection model. π₯Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphoneπ₯
[CVPR 2021] VirTex: Learning Visual Representations from Textual Annotations
Evaluation of timing performance of deep neural networks workloads accelerated on Versal AI Engine in presence of contention on shared resources, mainly caused by ARM Cortex A72 dual-core microprocessor.
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 repository of custom models specialized to various use cases.
Benchmark and analysis of 165 pretrained SSL models. Code for "Evaluating Self-Supervised Learning via Risk Decomposition".
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