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# resnet-50-resnet50_fashion

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1064
- Accuracy: 0.9740

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 7
- total_train_batch_size: 56
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6532        | 0.99  | 86   | 0.6781          | 0.6345   |
| 0.5407        | 2.0   | 173  | 0.5222          | 0.8590   |
| 0.4086        | 2.99  | 259  | 0.3595          | 0.8924   |
| 0.3449        | 4.0   | 346  | 0.2616          | 0.9184   |
| 0.3518        | 4.99  | 432  | 0.2288          | 0.9443   |
| 0.308         | 6.0   | 519  | 0.2758          | 0.9425   |
| 0.3209        | 7.0   | 606  | 0.3777          | 0.9369   |
| 0.284         | 7.99  | 692  | 0.1704          | 0.9555   |
| 0.2466        | 9.0   | 779  | 0.1571          | 0.9462   |
| 0.3123        | 9.99  | 865  | 0.6492          | 0.9406   |
| 0.2827        | 11.0  | 952  | 0.4968          | 0.9406   |
| 0.2736        | 11.99 | 1038 | 0.1370          | 0.9592   |
| 0.2476        | 13.0  | 1125 | 0.1616          | 0.9499   |
| 0.195         | 14.0  | 1212 | 0.1362          | 0.9610   |
| 0.2536        | 14.99 | 1298 | 0.1298          | 0.9536   |
| 0.2022        | 16.0  | 1385 | 0.7470          | 0.9518   |
| 0.2406        | 16.99 | 1471 | 0.1241          | 0.9647   |
| 0.2019        | 18.0  | 1558 | 0.1278          | 0.9536   |
| 0.2073        | 18.99 | 1644 | 0.1134          | 0.9685   |
| 0.1873        | 20.0  | 1731 | 0.6738          | 0.9629   |
| 0.2446        | 21.0  | 1818 | 0.1033          | 0.9685   |
| 0.1999        | 21.99 | 1904 | 0.1181          | 0.9647   |
| 0.1716        | 23.0  | 1991 | 0.1099          | 0.9610   |
| 0.175         | 23.99 | 2077 | 0.1064          | 0.9740   |
| 0.1962        | 25.0  | 2164 | 0.1174          | 0.9722   |
| 0.1943        | 25.99 | 2250 | 1.0625          | 0.9518   |
| 0.2044        | 27.0  | 2337 | 0.8419          | 0.9573   |
| 0.1835        | 28.0  | 2424 | 0.1112          | 0.9703   |
| 0.191         | 28.99 | 2510 | 0.1142          | 0.9685   |
| 0.1676        | 30.0  | 2597 | 0.1080          | 0.9647   |
| 0.1533        | 30.99 | 2683 | 0.1494          | 0.9647   |
| 0.1991        | 32.0  | 2770 | 0.1000          | 0.9703   |
| 0.1845        | 32.99 | 2856 | 0.0989          | 0.9740   |
| 0.1605        | 34.0  | 2943 | 0.0975          | 0.9685   |
| 0.1928        | 35.0  | 3030 | 0.4555          | 0.9629   |
| 0.1506        | 35.99 | 3116 | 0.1059          | 0.9703   |
| 0.1912        | 37.0  | 3203 | 0.1016          | 0.9647   |
| 0.1689        | 37.99 | 3289 | 0.5421          | 0.9666   |
| 0.1467        | 39.0  | 3376 | 0.1095          | 0.9647   |
| 0.1513        | 39.99 | 3462 | 0.3828          | 0.9703   |
| 0.1768        | 41.0  | 3549 | 0.0945          | 0.9703   |
| 0.1633        | 42.0  | 3636 | 0.2250          | 0.9592   |
| 0.1945        | 42.99 | 3722 | 0.2015          | 0.9685   |
| 0.1896        | 44.0  | 3809 | 0.1114          | 0.9666   |
| 0.1629        | 44.99 | 3895 | 0.0954          | 0.9666   |
| 0.1825        | 46.0  | 3982 | 0.0974          | 0.9740   |
| 0.1664        | 46.99 | 4068 | 0.0939          | 0.9703   |
| 0.1535        | 48.0  | 4155 | 0.0935          | 0.9722   |
| 0.1801        | 49.0  | 4242 | 0.0999          | 0.9703   |
| 0.1502        | 49.67 | 4300 | 0.1959          | 0.9703   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "resnet-50-resnet50_fashion", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.974025974025974, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard resnet image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

resnet-50-resnet50\_fashion
===========================


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


* Loss: 0.1064
* Accuracy: 0.9740


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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 7
* total\_train\_batch\_size: 56
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 50


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 7
* total\_train\_batch\_size: 56
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 7
* total\_train\_batch\_size: 56
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 7
* total\_train\_batch\_size: 56
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 50### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3