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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
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