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README.md

Model Selection

Installation

Example scripts support all models in PyTorch-Image-Models. You need to install timm to use PyTorch-Image-Models.

pip install timm

Dataset

Supported Methods

Supported methods include:

Experiment and Results

Model Ranking on image classification tasks

The shell files give the scripts to ranking pre-trained models on a given dataset. For example, if you want to use LogME to calculate the transfer performance of ResNet50(ImageNet pre-trained) on Aircraft, use the following script

# Using LogME to ranking pre-trained ResNet50 on Aircraft
# Assume you have put the datasets under the path `data/cub200`, 
# or you are glad to download the datasets automatically from the Internet to this path
CUDA_VISIBLE_DEVICES=0 python logme.py ./data/FGVCAircraft -d Aircraft -a resnet50 -l fc --save_features

We use LEEP, NCE HScore and LogME to compute scores by applying 10 pre-trained models to different datasets. The correlation(Weighted kendall Tau/Pearson Correlation) between scores and fine-tuned accuracies are presented.

Model Ranking Benchmark on Aircraft

Model Finetuned Acc HScore LEEP LogME NCE
GoogleNet 82.7 28.37 -4.310 0.934 -4.248
Inception V3 88.8 43.89 -4.202 0.953 -4.170
ResNet50 86.6 46.23 -4.215 0.946 -4.201
ResNet101 85.6 46.13 -4.230 0.948 -4.222
ResNet152 85.3 46.25 -4.230 0.950 -4.229
DenseNet121 85.4 31.53 -4.228 0.938 -4.215
DenseNet169 84.5 41.81 -4.245 0.943 -4.270
Densenet201 84.6 46.01 -4.206 0.942 -4.189
MobileNet V2 82.8 34.43 -4.198 0.941 -4.208
MNasNet 72.8 35.28 -4.192 0.948 -4.195
Pearson Corr - 0.688 0.127 0.582 0.173
Weighted Tau - 0.664 -0.264 0.595 0.002

Model Ranking Benchmark on Caltech101

Model Finetuned Acc HScore LEEP LogME NCE
GoogleNet 91.7 75.88 -1.462 1.228 -0.665
Inception V3 94.3 93.73 -1.119 1.387 -0.560
ResNet50 91.8 91.65 -1.020 1.262 -0.616
ResNet101 93.1 92.54 -0.899 1.305 -0.603
ResNet152 93.2 92.91 -0.875 1.324 -0.605
DenseNet121 91.9 75.02 -0.979 1.172 -0.609
DenseNet169 92.5 86.37 -0.864 1.212 -0.580
Densenet201 93.4 89.90 -0.914 1.228 -0.590
MobileNet V2 89.1 75.82 -1.115 1.150 -0.693
MNasNet 91.5 77.00 -1.043 1.178 -0.690
Pearson Corr - 0.748 0.324 0.794 0.843
Weighted Tau - 0.721 0.127 0.697 0.810

Model Ranking Benchmark on CIFAR10

Model Finetuned Acc HScore LEEP LogME NCE
GoogleNet 96.2 5.911 -1.385 0.293 -1.139
Inception V3 97.5 6.363 -1.259 0.349 -1.060
ResNet50 96.8 6.567 -1.010 0.388 -1.007
ResNet101 97.7 6.901 -0.829 0.463 -0.838
ResNet152 97.9 6.945 -0.838 0.469 -0.851
DenseNet121 97.2 6.210 -1.035 0.302 -1.006
DenseNet169 97.4 6.547 -0.934 0.343 -0.946
Densenet201 97.4 6.706 -0.888 0.369 -0.866
MobileNet V2 95.7 5.928 -1.100 0.291 -1.089
MNasNet 96.8 6.018 -1.066 0.304 -1.086
Pearson Corr - 0.839 0.604 0.733 0.786
Weighted Tau - 0.800 0.638 0.785 0.714

Model Ranking Benchmark on CIFAR100

Model Finetuned Acc HScore LEEP LogME NCE
GoogleNet 83.2 29.33 -3.234 1.037 -2.751
Inception V3 86.6 36.47 -2.995 1.070 -2.615
ResNet50 84.5 40.20 -2.612 1.099 -2.516
ResNet101 87.0 43.80 -2.365 1.130 -2.285
ResNet152 87.6 44.19 -2.410 1.133 -2.369
DenseNet121 84.8 32.13 -2.665 1.029 -2.504
DenseNet169 85.0 37.51 -2.494 1.051 -2.418
Densenet201 86.0 39.75 -2.470 1.061 -2.305
MobileNet V2 80.8 30.36 -2.800 1.039 -2.653
MNasNet 83.9 32.05 -2.732 1.051 -2.643
Pearson Corr - 0.815 0.513 0.698 0.705
Weighted Tau - 0.775 0.659 0.790 0.654

Model Ranking Benchmark on DTD

Model Finetuned Acc HScore LEEP LogME NCE
GoogleNet 73.6 34.61 -2.333 0.682 0.682
Inception V3 77.2 57.17 -2.135 0.691 0.691
ResNet50 75.2 78.26 -1.985 0.695 0.695
ResNet101 76.2 117.23 -1.974 0.689 0.689
ResNet152 75.4 32.30 -1.924 0.698 0.698
DenseNet121 74.9 35.23 -2.001 0.670 0.670
DenseNet169 74.8 43.36 -1.817 0.686 0.686
Densenet201 74.5 45.96 -1.926 0.689 0.689
MobileNet V2 72.9 37.99 -2.098 0.664 0.664
MNasNet 72.8 38.03 -2.033 0.679 0.679
Pearson Corr - 0.532 0.217 0.617 0.471
Weighted Tau - 0.416 -0.004 0.550 0.083

Model Ranking Benchmark on OxfordIIITPets

Model Finetuned Acc HScore LEEP LogME NCE
GoogleNet 91.9 28.02 -1.064 0.854 -0.815
Inception V3 93.5 33.29 -0.888 1.119 -0.711
ResNet50 92.5 32.55 -0.805 0.952 -0.721
ResNet101 94.0 32.76 -0.769 0.985 -0.717
ResNet152 94.5 32.86 -0.732 1.009 -0.679
DenseNet121 92.9 27.09 -0.837 0.797 -0.753
DenseNet169 93.1 30.09 -0.779 0.829 -0.699
Densenet201 92.8 31.25 -0.810 0.860 -0.716
MobileNet V2 90.5 27.83 -0.902 0.765 -0.822
MNasNet 89.4 27.95 -0.854 0.785 -0.812
Pearson Corr - 0.427 -0.127 0.589 0.501
Weighted Tau - 0.425 -0.143 0.502 0.119

Model Ranking Benchmark on StanfordCars

Model Finetuned Acc HScore LEEP LogME NCE
GoogleNet 91.0 41.47 -4.612 1.246 -4.312
Inception V3 92.3 73.68 -4.268 1.259 -4.110
ResNet50 91.7 72.94 -4.366 1.253 -4.221
ResNet101 91.7 73.98 -4.281 1.255 -4.218
ResNet152 92.0 76.17 -4.215 1.260 -4.142
DenseNet121 91.5 45.82 -4.437 1.249 -4.271
DenseNet169 91.5 63.40 -4.286 1.252 -4.175
Densenet201 91.0 70.50 -4.319 1.251 -4.151
MobileNet V2 91.0 51.12 -4.463 1.250 -4.306
MNasNet 88.5 51.91 -4.423 1.254 -4.338
Pearson Corr - 0.503 0.433 0.274 0.695
Weighted Tau - 0.638 0.703 0.654 0.750

Model Ranking Benchmark on SUN397

Model Finetuned Acc HScore LEEP LogME NCE
GoogleNet 62.0 71.35 -3.744 1.621 -3.055
Inception V3 65.7 114.21 -3.372 1.648 -2.844
ResNet50 64.7 110.39 -3.198 1.638 -2.894
ResNet101 64.8 113.63 -3.103 1.642 -2.837
ResNet152 66.0 116.51 -3.056 1.646 -2.822
DenseNet121 62.3 72.16 -3.311 1.614 -2.945
DenseNet169 63.0 95.80 -3.165 1.623 -2.903
Densenet201 64.7 103.09 -3.205 1.624 -2.896
MobileNet V2 60.5 75.90 -3.338 1.617 -2.968
MNasNet 60.7 80.91 -3.234 1.625 -2.933
Pearson Corr - 0.913 0.428 0.824 0.782
Weighted Tau - 0.918 0.581 0.748 0.873

Citation

If you use these methods in your research, please consider citing.

@inproceedings{bao_information-theoretic_2019,
	title = {An Information-Theoretic Approach to Transferability in Task Transfer Learning},
	booktitle = {ICIP},
	author = {Bao, Yajie and Li, Yang and Huang, Shao-Lun and Zhang, Lin and Zheng, Lizhong and Zamir, Amir and Guibas, Leonidas},
	year = {2019}
}

@inproceedings{nguyen_leep:_2020,
	title = {LEEP: A New Measure to Evaluate Transferability of Learned Representations},
	booktitle = {ICML},
	author = {Nguyen, Cuong and Hassner, Tal and Seeger, Matthias and Archambeau, Cedric},
	year = {2020}
}

@inproceedings{you_logme:_2021,
	title = {LogME: Practical Assessment of Pre-trained Models for Transfer Learning},
	booktitle = {ICML},
	author = {You, Kaichao and Liu, Yong and Wang, Jianmin and Long, Mingsheng},
	year = {2021}
}

@inproceedings{tran_transferability_2019,
	title = {Transferability and hardness of supervised classification tasks},
	booktitle = {ICCV},
	author = {Tran, Anh T. and Nguyen, Cuong V. and Hassner, Tal},
	year = {2019}
}