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69 lines
3.9 KiB
Markdown
69 lines
3.9 KiB
Markdown
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<!-- [ALGORITHM] -->
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# <summary><a href="https://arxiv.org/abs/1606.04080"> MatchingNet (NeurIPS'2016)</a></summary>
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```bibtex
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@inproceedings{vinyals2016matching,
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title={Matching networks for one shot learning},
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author={Vinyals, Oriol and Blundell, Charles and Lillicrap, Tim and Wierstra, Daan and others},
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booktitle={Advances in Neural Information Processing Systems},
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pages={3630--3638},
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year={2016}
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}
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```
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## How to Reproduce MatchingNet
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It consists of two steps:
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- **Step1: Base training**
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- use all the images of base classes to train a base model.
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- use validation set to select the best model.
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- **Step2: Meta Testing**:
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- use best model from step1.
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### An example of CUB dataset with Conv4
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```bash
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# base training
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python ./tools/classification/train.py \
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configs/classification/matching_net/cub/matching-net_conv4_1xb105_cub_5way-1shot.py
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# meta testing
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python ./tools/classification/test.py \
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configs/classification/matching_net/cub/matching-net_conv4_1xb105_cub_5way-1shot.py \
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work_dir/matching-net_conv4_1xb105_cub_5way-1shot/best_accuracy_mean.pth
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```
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**Note**:
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- All the result are trained with single gpu.
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- The base training of 1 shot and 5 shot use same training setting,
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but different validation setting.
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## Results on CUB dataset of 1000 episodes
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| Arch | Input Size | Batch Size | way | shot | mean Acc | std | ckpt | log |
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| :-------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: |
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| [conv4](/configs/classification/matching_net/cub/matching-net_conv4_1xb105_cub_5way-1shot.py) | 84x84 | 64 | 5 | 1 | - | - | [ckpt]() | [log]() |
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| [conv4](/configs/classification/matching_net/cub/matching-net_conv4_1xb105_cub_5way-5shot.py) | 84x84 | 64 | 5 | 5 | - | - | [ckpt]() | [log]() |
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| [resnet12](/configs/classification/matching_net/cub/matching-net_resnet12_1xb105_cub_5way-1shot.py) | 84x84 | 64 | 5 | 1 | - | - | [ckpt]() | [log]() |
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| [resnet12](/configs/classification/matching_net/cub/matching-net_resnet12_1xb105_cub_5way-5shot.py) | 84x84 | 64 | 5 | 5 | - | - | [ckpt]() | [log]() |
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## Results on Mini-ImageNet dataset of 1000 episodes
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| Arch | Input Size | Batch Size | way | shot | mean Acc | std | ckpt | log |
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| :-------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: |
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| [conv4](/configs/classification/matching_net/mini_imagenet/matching-net_conv4_1xb105_mini-imagenet_5way-1shot.py) | 84x84 | 64 | 5 | 1 | - | - | [ckpt]() | [log]() |
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| [conv4](/configs/classification/matching_net/mini_imagenet/matching-net_conv4_1xb105_mini-imagenet_5way-5shot.py) | 84x84 | 64 | 5 | 5 | - | - | [ckpt]() | [log]() |
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| [resnet12](/configs/classification/matching_net/mini_imagenet/matching-net_resnet12_1xb105_mini-imagenet_5way-1shot.py) | 84x84 | 64 | 5 | 1 | - | - | [ckpt]() | [log]() |
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| [resnet12](/configs/classification/matching_net/mini_imagenet/matching-net_resnet12_1xb105_mini-imagenet_5way-5shot.py) | 84x84 | 64 | 5 | 5 | - | - | [ckpt]() | [log]() |
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## Results on Tiered-ImageNet of 1000 episodes
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| Arch | Input Size | Batch Size | way | shot | mean Acc | std | ckpt | log |
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| :-------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: |
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| [conv4](/configs/classification/matching_net/tiered_imagenet/matching-net_conv4_1xb105_tiered-imagenet_5way-1shot.py) | 84x84 | 64 | 5 | 1 | - | - | [ckpt]() | [log]() |
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| [conv4](/configs/classification/matching_net/tiered_imagenet/matching-net_conv4_1xb105_tiered-imagenet_5way-5shot.py) | 84x84 | 64 | 5 | 5 | - | - | [ckpt]() | [log]() |
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| [resnet12](/configs/classification/matching_net/tiered_imagenet/matching-net_resnet12_1xb105_tiered-imagenet_5way-1shot.py) | 84x84 | 64 | 5 | 1 | - | - | [ckpt]() | [log]() |
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| [resnet12](/configs/classification/matching_net/tiered_imagenet/matching-net_resnet12_1xb105_tiered-imagenet_5way-5shot.py) | 84x84 | 64 | 5 | 5 | - | - | [ckpt]() | [log]() |
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