PaddleFSL

PaddleFSL

PaddleFSL provides examples for few-shot learning (FSL), building on top of PaddlePaddle. Currently, it contains two backbone embedding networks, two popularly used baselines and six benchmark datasets for FSL image classification.

Datasets

The following benckmark FSL image classification datasets are used:

• Omniglot (B. M. Lake et al., 2015), which is downloaded from (https://drive.google.com/file/d/1iM5KOxzgGT3i3sxIvhwoh_Mrhuz2keG0/view)
• Mini-ImageNet (O. Vinyals et al., 2016), which is downloaded from (https://drive.google.com/file/d/1g1aIDy2Ar_MViF2gDXFYDBTR-HYecV07/view)
• Tiered-ImageNet (M. Ren et al., 2018), which is downloaded from (https://drive.google.com/file/d/16V_ZlkW4SsnNDtnGmaBRq2OoPmUOc5mY/view)
• CIFAR-FS (L. Bertinetto et al., 2018), which is downloaded from (https://drive.google.com/file/d/1GjGMI0q3bgcpcB_CjI40fX54WgLPuTpS/view)
• FC100 (B. N. Oreshkin et al., 2018), which is downloaded from (https://drive.google.com/file/d/1_ZsLyqI487NRDQhwvI7rg86FK3YAZvz1/view)
• CUB (W.-Y. Chen et al., 2019) , which is downloaded from (https://drive.google.com/file/d/1W9gyJonsDpG9E0JV-5eWkL3nB2OqLk7P/view)

We use the train / val / test splits as mentioned in the respective papers. Note there is no specific split for Omniglot, thus we use random train / val / test splits.

The above datasets exceed the file limit of Github, thus please download them from the aforementioned datasets and put them into the respective data/{dataset name} folder.

Backbones

The following popularly used embedding architecures are used:

• Conv4 (O. Vinyals et al., 2016), which consists of four convolutional blocks.
• ResNet12 (N. Mishra et al., 2018), which consists of four residual blocks. Note that our architecture is slightly different from theirs. We do not employ any projection layer after the four residual blocks, instead, directly use global average pooling to obtain final embeddings. Besides, the number of filters in each residual block are 64, 128, 256, 512 respectively, following B. Oreshkin et al., 2019.

Baselines

The following baselines are provided:

• Prototypical Networks (ProtoNet) (J. Snell et al., 2017).
• Relation Network (RelationNet) (F. Sung et al., 2018). Note that the relation network is not designed for ResNet12 backbone, therefore, we change a bit the configuration for stable training when employing ResNet12 backbone. Specifically, we replace ReLU by Leaky_ReLU and use cross entropy loss instead of MSE loss.

Dependencies

The codes have been tested on:

• python 3.6.12
• paddlepaddle 1.8.4
• visualdl 2.0.3

Running Examples

Training

python train.py --dataset=miniimagenet --backbone='Conv4' --method='protonet' --epochs=200 --k_shot=5 --n_way=5 --lr=0.001

python train.py --dataset=cifarfs --backbone='Resnet12' --method='relationnet' --epochs=200 --k_shot=1 --n_way=5 --lr=0.1 --lr_scheduler=True

Testing

python test.py --dataset=miniimagenet --backbone='Conv4' --method='protonet' --k_shot=5 --n_way=5 --test_mode=True --test_episodes=6000

python train.py --dataset=cifarfs --backbone='Resnet12' --method='relationnet' --k_shot=1 --n_way=5 --test_mode=True --test_episodes=6000

Visualization of training logs

visualdl --logdir ./logs/logs/miniimagenet

Results

Using this codes, we can reproduce the reported results of respecitve papers.

All the tasks are tested for 6,000 episodes. Average accuracy and 95% confidence interval are reported.

Blanks mean that public results are not avaliable.

Omniglot

		1-shot-5-way(%)		5-shot-5-way(%)		1-shot-20-way(%)		5-shot-20-way(%)
		reported	ours	reported	ours	reported	ours	reported	ours
Conv4	ProtoNet	97.4	99.45 ± 0.04	99.3	99.67 ± 0.03	96.0	98.51 ± 0.04	98.9	99.00 ± 0.03
	RelationNet	99.6 ± 0.2	99.23 ± 0.05	99.8± 0.1	99.83 ± 0.02	97.6 ± 0.2	98.64 ± 0.04	99.1± 0.1	99.19 ± 0.02
ResNet12	ProtoNet		99.40 ± 0.04		99.63 ± 0.03		98.35 ± 0.04		99.40 ± 0.02
	RelationNet		99.36 ± 0.04		99.80 ± 0.02		98.65 ± 0.04		99.54 ± 0.02

Mini-ImageNet

		1-shot-5-way(%)		5-shot-5-way(%)
		reported	ours	reported	ours
Conv4	ProtoNet	46.61 ± 0.78	49.75 ± 0.25	65.77 ± 0.70	66.53 ± 0.21
	RelationNet	50.44 ± 0.82	50.04 ± 0.26	65.32 ± 0.70	65.60 ± 0.21
ResNet12	ProtoNet		54.08 ± 0.29		68.44 ± 0.23
	RelationNet		53.82 ± 0.27		69.02 ± 0.21

Tiered-ImageNet

		1-shot-5-way(%)		5-shot-5-way(%)
		reported	ours	reported	ours
Conv4	ProtoNet	53.31 ± 0.89	52.14 ± 0.28	72.69 ± 0.74	71.07 ± 0.24
	RelationNet	54.48 ± 0.93	53.81± 0.29	71.32 ± 0.78	70.04 ± 0.25
ResNet12	ProtoNet		54.23 ± 0.31		71.82 ± 0.26
	RelationNet		56.76 ± 0.29		77.02 ± 0.23

CIFAR-FS

		1-shot-5-way(%)		5-shot-5-way(%)
		reported	ours	reported	ours
Conv4	ProtoNet	55.5 ± 0.70	55.96 ± 0.29	72.0 ± 0.60	72.27 ± 0.23
	RelationNet	55.0 ± 1.00	55.56 ± 0.30	69.3 ± 0.80	71.45 ± 0.24
ResNet12	ProtoNet		63.67 ± 0.31		78.16 ± 0.23
	RelationNet		63.46 ± 0.31		78.61 ± 0.22

FC100

		1-shot-5-way(%)		5-shot-5-way(%)
		reported	ours	reported	ours
Conv4	ProtoNet	35.3 ± 0.60	36.68 ± 0.22	48.6 ± 0.60	49.45 ± 0.22
	RelationNet		36.74 ± 0.23		47.14 ± 0.22
ResNet12	ProtoNet		36.99 ± 0.23		49.41 ± 0.23
	RelationNet		37.51 ± 0.23		51.51 ± 0.23

CUB

		1-shot-5-way(%)		5-shot-5-way(%)
		reported	ours	reported	ours
Conv4	ProtoNet	51.31 ± 0.91	53.30 ± 0.29	70.77 ± 0.69	69.08 ± 0.22
	RelationNet	62.45 ± 0.98	60.31 ± 0.31	76.11 ± 0.69	73.02 ± 0.23
ResNet12	ProtoNet		60.75 ± 0.32		71.42 ± 0.24
	RelationNet		60.79 ± 0.31		75.69 ± 0.22

Contributors

This project is maintained by Yaqing Wang (wangyaqing01@baidu.com) and Heda Song (v_songheda@baidu.com) at BIL, Baidu Research.

Disclaimer

This project targets at fast prototyping FSL solutions for research purpose. Baidu is not responsible for the 3rd party's generation with these codes.