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update model card README.md
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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- cifar10_quality_drift
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metrics:
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- accuracy
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- f1
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model-index:
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- name: resnet-50-cifar10-quality-drift
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: cifar10_quality_drift
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type: cifar10_quality_drift
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.724
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- name: F1
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type: f1
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value: 0.7221970011456912
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# resnet-50-cifar10-quality-drift
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This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the cifar10_quality_drift dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8235
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- Accuracy: 0.724
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- F1: 0.7222
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0002
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 3
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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| 1.7311 | 1.0 | 750 | 1.1310 | 0.6333 | 0.6300 |
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| 1.1728 | 2.0 | 1500 | 0.8495 | 0.7153 | 0.7155 |
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| 1.0322 | 3.0 | 2250 | 0.8235 | 0.724 | 0.7222 |
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### Framework versions
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- Transformers 4.20.1
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- Pytorch 1.12.0+cu113
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- Datasets 2.3.2
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- Tokenizers 0.12.1
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