https://github.com/jmcheon/transformers
https://github.com/jmcheon/transformers
computer-vision natural-language-processing python3 pytorch research-paper-implementation tensorflow transformer-architecture transformer-models
Last synced: 2 months ago
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- Host: GitHub
- URL: https://github.com/jmcheon/transformers
- Owner: jmcheon
- Created: 2024-11-27T00:27:16.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-27T11:02:17.000Z (over 1 year ago)
- Last Synced: 2025-03-27T12:22:41.941Z (over 1 year ago)
- Topics: computer-vision, natural-language-processing, python3, pytorch, research-paper-implementation, tensorflow, transformer-architecture, transformer-models
- Language: Jupyter Notebook
- Homepage:
- Size: 21.4 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Transformers
This repository contains implementations of moderne AI architectures from research papers.
## Paper Naming Convention
Each implementation follows this naming format:
`{paper_alias}_{paper_year}`
## 📌 Computer Vision and Pattern Recognition
- **R-CNN_2013** - [Rich feature hierarchies for accurate object detection and semantic segmentation](https://arxiv.org/abs/1311.2524)
- **VGG_2014** - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/pdf/1409.1556)
- **FCN_2014** - [Fully Convolutional Networks for Semantic Segmentation](https://arxiv.org/pdf/1411.4038)
- **UNet_2015** - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)
- **YOLOv1_2015** - [You Only Look Once: Unified, Real-Time Object Detection](https://arxiv.org/abs/1506.02640)
- **SegNet_2015** - [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://arxiv.org/abs/1511.00561)
- **ResNet_2015** - [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)
- **MobileNetV1_2017** - [MobileNets: Efficient Convolutional Nerual Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861)
- **MobileNetV2_2018** - [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381)
- **SID_2018** - [Learning to See in the Dark](https://arxiv.org/abs/1805.01934)
- **Vision_Transformer_2020** - [An Image is Worth 16x16 Words: Transformer for Image Recognition at Scale](https://arxiv.org/abs/2010.11929)
- **CLIP_2021** - [Learning Transferable Visual Models from Natural Language Supervision](https://arxiv.org/abs/2103.00020)
- **Swin_Transformer_2021** - [Hierarchical Vision Transformer Using Shifted Windows](https://arxiv.org/abs/2103.14030)
- **SwinIR_2021** - [SwinIR: Image Restoration Using Swin Transformer](https://arxiv.org/abs/2108.10257)
## 📌 Computation and Language
- **seq2seq_2014** - [Sequence to Sequence Learning with Neural Networks](https://arxiv.org/abs/1409.32152)
- **Transformer_2017** - [Attention Is All You Need](https://arxiv.org/abs/1706.03762)
- **BERT_2018** - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)