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# resnet-50-LongSleeveCleanedData

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: 0.0889
- Accuracy: 0.9788

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9906        | 0.99  | 143  | 1.0394          | 0.6134   |
| 0.7315        | 2.0   | 287  | 0.6790          | 0.7631   |
| 0.559         | 3.0   | 431  | 0.4735          | 0.8547   |
| 0.4905        | 4.0   | 575  | 0.3148          | 0.8983   |
| 0.3465        | 5.0   | 719  | 0.2225          | 0.9363   |
| 0.3372        | 6.0   | 863  | 0.1839          | 0.9486   |
| 0.3349        | 7.0   | 1007 | 0.1617          | 0.9587   |
| 0.3159        | 7.99  | 1150 | 0.1323          | 0.9620   |
| 0.2805        | 9.0   | 1294 | 0.1660          | 0.9587   |
| 0.2657        | 10.0  | 1438 | 0.1456          | 0.9531   |
| 0.2929        | 11.0  | 1582 | 0.1086          | 0.9698   |
| 0.2763        | 12.0  | 1726 | 0.0886          | 0.9765   |
| 0.2475        | 13.0  | 1870 | 0.1041          | 0.9732   |
| 0.2148        | 14.0  | 2014 | 0.0955          | 0.9777   |
| 0.209         | 14.99 | 2157 | 0.1061          | 0.9709   |
| 0.2408        | 16.0  | 2301 | 0.0784          | 0.9743   |
| 0.222         | 17.0  | 2445 | 0.0839          | 0.9698   |
| 0.208         | 18.0  | 2589 | 0.0873          | 0.9732   |
| 0.2214        | 19.0  | 2733 | 0.0889          | 0.9788   |
| 0.2375        | 19.88 | 2860 | 0.0864          | 0.9743   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "resnet-50-LongSleeveCleanedData", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9787709497206704, "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-LongSleeveCleanedData
===============================


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: 0.0889
* Accuracy: 0.9788


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: 20


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* 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: 20 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* 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: 20 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3