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resnet-50-fashion-mnist-quality-drift

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resnet-50-fashion-mnist-quality-drift
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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- fashion_mnist_quality_drift
metrics:
- accuracy
- f1
model-index:
- name: resnet-50-fashion-mnist-quality-drift
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: fashion_mnist_quality_drift
      type: fashion_mnist_quality_drift
      config: default
      split: training
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.73
    - name: F1
      type: f1
      value: 0.7289360255705818
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# resnet-50-fashion-mnist-quality-drift

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the fashion_mnist_quality_drift dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7473
- Accuracy: 0.73
- F1: 0.7289

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.5138        | 1.0   | 750  | 0.9237          | 0.684    | 0.6826 |
| 0.9377        | 2.0   | 1500 | 0.7861          | 0.722    | 0.7253 |
| 0.8276        | 3.0   | 2250 | 0.7473          | 0.73     | 0.7289 |


### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1