README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - cifar10_quality_drift
metrics:
  - accuracy
  - f1
model-index:
  - name: resnet-50-cifar10-quality-drift
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: cifar10_quality_drift
          type: cifar10_quality_drift
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.724
          - name: F1
            type: f1
            value: 0.7221970011456912

resnet-50-cifar10-quality-drift

This model is a fine-tuned version of microsoft/resnet-50 on the cifar10_quality_drift dataset. It achieves the following results on the evaluation set:

• Loss: 0.8235
• Accuracy: 0.724
• F1: 0.7222

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
1.7311	1.0	750	1.1310	0.6333	0.6300
1.1728	2.0	1500	0.8495	0.7153	0.7155
1.0322	3.0	2250	0.8235	0.724	0.7222

Framework versions

• Transformers 4.20.1
• Pytorch 1.12.0+cu113
• Datasets 2.3.2
• Tokenizers 0.12.1