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This model is a small resnet18 trained on cifar10.

  • Test Accuracy: 0.9498
  • License: MIT

How to Get Started with the Model

Use the code below to get started with the model.

import detectors
import timm

model = timm.create_model("resnet18_cifar10", pretrained=True)

Training Data

Training data is cifar10.

Training Hyperparameters

  • config: scripts/train_configs/cifar10.json

  • model: resnet18_cifar10

  • dataset: cifar10

  • batch_size: 128

  • epochs: 300

  • validation_frequency: 5

  • seed: 1

  • criterion: CrossEntropyLoss

  • criterion_kwargs: {}

  • optimizer: SGD

  • lr: 0.1

  • optimizer_kwargs: {'momentum': 0.9, 'weight_decay': 0.0005, 'nesterov': 'True'}

  • scheduler: ReduceLROnPlateau

  • scheduler_kwargs: {'factor': 0.1, 'patience': 3, 'threshold': 0.001, 'mode': 'max'}

  • debug: False

Testing Data

Testing data is cifar10.


This model card was created by Eduardo Dadalto.

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Dataset used to train edadaltocg/resnet18_cifar10

Evaluation results