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ResNet 34 Ensemble Predictions on TinyImageNet

<p>Predictions generated by an ensemble of 4 ResNet 34 Deep Neural Networks Trained on TinyImagenet, as used in repository&nbsp;https://anonymous.4open.science/r/ensemble_attention-7616/README.md. Ensembles are trained to encourage/discourage predictive diversity. Each timestamped folder contains individual training runs, with the labels&nbsp;and probabilistic predictions of the ensemble on 1) the training set (train_labels.npy, train_preds.npy) and 2) the test set (ind_labels.npy, ind_preds.npy) for tinyimagenet. The file (tinyimagenet/resnet34/version_0/hparams.yaml) contains specific hyperparameters used on a particular training run. Figures visualizing training results&nbsp;can be generated by:<br> 1.unzipping the four folders in to the directory `ensemble_attention/scripts/outputs/`&nbsp;<br> 2.&nbsp;running the script `ensemble_attention/scripts/vis_scripts/all_weights_resnet34_tinyimagenet.py`.</p>

ShareScore

32/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
16
Reuse readiness
4
Engagement
4