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# 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

{"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": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "fashion_mnist_quality_drift", "type": "fashion_mnist_quality_drift", "config": "default", "split": "training", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.73, "name": "Accuracy"}, {"type": "f1", "value": 0.7289360255705818, "name": "F1"}]}]}]}
transformers pytorch tensorboard resnet image-classification generated_from_trainer dataset:fashion_mnist_quality_drift license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-fashion_mnist_quality_drift #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

resnet-50-fashion-mnist-quality-drift
=====================================


This model is a fine-tuned version of 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



### Framework versions


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



### 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 ### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-fashion_mnist_quality_drift #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-fashion_mnist_quality_drift #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


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