KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#dsh #ai-agents #dsh-plugin #codex #developer-tools #claude-code #windows #desktop-app #typescript #rust #ai-agent #cli

metriclearningbench

★ 66 Open GitHub ↗

Metric learning models in PyTorch with results on CUB2011, CARS196, Stanford Online Products

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Metric learning [models](./model.py) in PyTorch, recall@1
| |[CUB2011](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html) | [CARS196](http://ai.stanford.edu/~jkrause/cars/car_dataset.html) | [Stanford Online Products](http://cvgl.stanford.edu/projects/lifted_struct/)
|:---:|:---:|:---:|:---:|
| [Margin contrastive loss](https://arxiv.org/abs/1706.07567), semi-hard | [0.58](./data/log.txt.margin_cub2011) @ epoch60 |  [0.80](./data/log.txt.margin_cars196) @ epoch60 | [0.7526](./data/log.txt.margin_stanfordonlineproducts) @ epoch90 | 
| [Lifted structured embedding](https://arxiv.org/abs/1511.06452) |
| [Triplet loss](https://arxiv.org/abs/1503.03832)|

Original impl of [Margin contrastive loss](https://arxiv.org/abs/1706.07567) published at: https://github.com/apache/incubator-mxnet/tree/19ede063c4756fa49cfe741e654180aee33991c6/example/gluon/embedding_learning (temporarily removed in https://github.com/apache/incubator-mxnet/pull/20602)

# Examples
```shell
# evaluation results are saved in ./data/log.txt

# train margin contrastive loss on CUB2011 using ResNet-50
python train.py --dataset cub2011 --model margin --base resnet50

# download GoogLeNet weights and train using LiftedStruct loss
wget -P ./data https://github.com/vadimkantorov/metriclearningbench/releases/download/data/googlenet.h5
python train.py --dataset cub2011 --model liftedstruct --base inception_v1_googlenet

# evaluate raw final layer embeddings on CUB2011 using ResNet-50
python train.py --dataset cub2011 --model untrained  --epochs 1
```

metriclearningbench

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README