PaddleClas
Pretrained-Language-Model
PaddleClas | Pretrained-Language-Model | |
---|---|---|
2 | 1 | |
5,262 | 2,960 | |
0.7% | 0.5% | |
5.4 | 6.1 | |
7 days ago | 3 months ago | |
Python | Python | |
Apache License 2.0 | - |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
PaddleClas
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Baidu AI Research Team Introduces ‘PP-ShiTu’: A Practical Lightweight Image Recognition System
Quick 5 Min Read | Paper | Github
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Baidu Research Introduces PP-LCNet: A Lightweight CPU Convolutional Neural Network With Better Accuracy And Performance
3 Min Quick Read | Paper| Github PaddleClas
Pretrained-Language-Model
What are some alternatives?
Swin-Transformer-Object-Detection - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.
SqueezeLLM - [ICML 2024] SqueezeLLM: Dense-and-Sparse Quantization
Swin-Transformer-Tensorflow - Unofficial implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" (https://arxiv.org/abs/2103.14030)
DWPose - "Effective Whole-body Pose Estimation with Two-stages Distillation" (ICCV 2023, CV4Metaverse Workshop)
Swin-Transformer-Semantic-Segmentation - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.
Torch-Pruning - [CVPR 2023] Towards Any Structural Pruning; LLMs / SAM / Diffusion / Transformers / YOLOv8 / CNNs
efficientnet - Implementation of EfficientNet model. Keras and TensorFlow Keras.
model-optimization - A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
Video-Swin-Transformer - This is an official implementation for "Video Swin Transformers".
Efficient-AI-Backbones - Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.
Lion - Code for "Lion: Adversarial Distillation of Proprietary Large Language Models (EMNLP 2023)"