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MultiGrain

MultiGrain is a neural network architecture that solves both image classification and image retrieval tasks.

The method is described in "MultiGrain: a unified image embedding for classes and instances" (arXiv link).

BibTeX reference:

@ARTICLE{2019arXivMultiGrain,
       author = {Berman, Maxim and J{\'e}gou, Herv{\'e} and Vedaldi Andrea and
         Kokkinos, Iasonas and Douze, Matthijs},
        title = "{{MultiGrain}: a unified image embedding for classes and instances}",
      journal = {arXiv e-prints},
         year = "2019",
        month = "Feb",
}

Please cite it if you use it.

Installation

The MultiGrain code requires

  • Python 3.5 or higher
  • PyTorch 1.0 or higher

and the requirements highlighted in requirements.txt

The requirements can be installed:

  • Ether by setting up a dedicated conda environment: conda env create -f environment.yml followed by source activate multigrain
  • Or with pip: pip install -r requirements.txt

Using the code

Extracting features with pre-trained networks

We provide pre-trained networks with ResNet-50 trunks for the following settings (top-1 accuracies given at scale 224):

λ p augmentation top-1 weights
1 1 full 76.8 joint_1B_1.0.pth
1 3 full 76.9 joint_3B_1.0.pth
0.5 1 full 77.0 joint_1B_0.5.pth
0.5 3 full 77.4 joint_3B_0.5.pth
0.5 3 autoaugment 78.2 joint_3BAA_0.5.pth

We provide fine-tuned networks for scales bigger than 224, as described in the Supplementary E. Only the pooling coefficient is fine-tuned:

network scale p top-1 weights
NASNet-A-Mobile 350 px 1.7 75.1 joint_1B_1.0.pth
SENet154 400 px 1.6 83.0 joint_3B_1.0.pth
PNASNet-5-Large 500 px 1.7 83.6 joint_1B_0.5.pth

To load a network, use the following PyTorch code:

import torch
from multigrain.lib import get_multigrain

net = get_multigrain('resnet50')

checkpoint = torch.load('base_1B_1.0.pth')

net.load_state_dict(checkpoint['model_state'])

The network takes images in any resolution. A normalization pre-processing step is used, with mean [0.485, 0.456, 0.406]. and standard deviation [0.229, 0.224, 0.225].

The pretrained weights do not include whitening of the features (important for retrieval), which are specific to each evaluation scale; follow steps below to compute and apply a whitening.

Evaluation of the networks

scripts/evaluate.py evaluates the network on standard benchmarks.

Classification results

Evaluate a network on ImageNet-val is straightforward using options from evaluate.py. For instance the following command:

IMAGENET_PATH=  # the path that contains the /val and /train image directories

python scripts/evaluate.py --expdir experiments/joint_3B_0.5/eval_p4_500 \
--imagenet-path $IMAGENET_PATH --input-size 500 --dataset imagenet-val \
--pooling-exponent 4 --resume-from joint_3B_0.5.pth

using the joint_3B_0.5.pth pretrained weights, should reproduce the top-1/top5 results of 78.6%/94.4% given in the article in Table 2 for ResNet-50 MultiGrain p=3, λ=0.5 and p*=4 scale s*=500.

Retrieval results

The implementation of the evaluation on the retrieval benchmarks in evaluate.py is in progress, but one may already use the dataloaders implemented in datasets/retrieval.py for this purpose.

Training

The training is performed in three steps. See help (-h flag) for detailed parameter list of each script. Only the initial joint training script benefits from multi-gpu hardware, the remaining scripts are not parallelized.

Joint training

scripts/train.py trains a MultiGrain architecture.

Important parameters:

  • --repeated-augmentations: number of repeated augmentations in the batches, N=3 was used in our joint trainings; N=1 is vanilla uniform sampling.
  • --pooling-exponent: pooling exponent in GeM pooling, p=1: vanilla average pooling.
  • --classif-weight: weighting factor between margin loss and classification loss (parameter λ in paper)

Other useful parameters:

  • --expdir: dedicated directory for the experiments
  • --resume-from: takes either an expdir or a model checkpoint file to restore from
  • --pretrained-backbone: initialized backbone weights from model zoo

Input size fine-tuning of GeM exponent

scripts/finetune_p.py determines the optimal p* for a given input resolution by fine-tuning (see supplementary E. in paper for details). Alternatively one may use cross-validation to determine p*, as done in the main article.

Whitening of the retrieval features

scripts/whiten.py computes a PCA whitening and modifies the network accordingly, integrating the reversed transformation in the fully-connected classification layer as described in the article. The scripts takes a list and directory of whitening images; the list given in data/whiten.txt is relative to the multimedia commons file structure.

Example training procedure

For example, the results with p=3 and λ=0.5 at scale s*=500 can be obtained with

# train network
python scripts/train.py --expdir experiments/joint_3B_0.5 --repeated-augmentations 3 \
--pooling-exponent 3 --classif-weight 0.5 --imagenet-path $IMAGENET_PATH

# fine-tune p*
python scripts/finetune_p.py --expdir experiments/joint_3B_0.5/finetune500 \
--resume-from experiments/joint_3B_0.5 --input-size 500 --imagenet-path $IMAGENET_PATH

# whitening 
python scripts/whiten.py --expdir experiments/joint_3B_0.5/finetune500_whitened \
--resume-from experiments/joint_3B_0.5/finetune500 --input-size 500 --whiten-path $WHITEN_PATH

Fine-tuning existing network

In appendix E. we report fine-tuning results on several pretrained networks. This experience can be reproduced using the finetune_p.py script. For example, in the case of SENet154 at scale s*=450, the following command should yield 83.1 top-1 accuracy with p*=1.6:

python scripts/finetune_p.py --expdir experiments/se154/finetune450 \
--pretrained-backbone --imagenet-path $IMAGENET_PATH --input-size 450 --backbone senet154 \
--no-validate-first

Contributing

See the CONTRIBUTING file for how to help out.

License

MultiGrain is CC BY-NC 4.0 licensed, as found in the LICENSE file.

The AutoAugment implementation is based on https://github.com/DeepVoltaire/AutoAugment. The Distance Weighted Sampling and margin loss implementation is based on the authors implementation https://github.com/chaoyuaw/sampling_matters.

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