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# vit-base-patch16-224-Trial007-YEL_STEM

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1499
- Accuracy: 1.0

## 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: 60
- eval_batch_size: 60
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 240
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7357        | 0.8   | 1    | 0.6631          | 0.6296   |
| 0.6975        | 1.6   | 2    | 0.5971          | 0.8519   |
| 0.8058        | 2.4   | 3    | 0.5292          | 0.8519   |
| 0.615         | 4.0   | 5    | 0.3763          | 0.8519   |
| 0.4979        | 4.8   | 6    | 0.2673          | 0.8889   |
| 0.3894        | 5.6   | 7    | 0.1499          | 1.0      |
| 0.4106        | 6.4   | 8    | 0.1380          | 0.9630   |
| 0.2802        | 8.0   | 10   | 0.1507          | 0.9630   |
| 0.2689        | 8.8   | 11   | 0.0646          | 1.0      |
| 0.1935        | 9.6   | 12   | 0.0485          | 1.0      |
| 0.1596        | 10.4  | 13   | 0.0517          | 1.0      |
| 0.1914        | 12.0  | 15   | 0.0677          | 0.9630   |
| 0.1588        | 12.8  | 16   | 0.0186          | 1.0      |
| 0.1073        | 13.6  | 17   | 0.0099          | 1.0      |
| 0.3463        | 14.4  | 18   | 0.0066          | 1.0      |
| 0.1446        | 16.0  | 20   | 0.0038          | 1.0      |
| 0.1463        | 16.8  | 21   | 0.0042          | 1.0      |
| 0.106         | 17.6  | 22   | 0.0109          | 1.0      |
| 0.1014        | 18.4  | 23   | 0.0186          | 1.0      |
| 0.1378        | 20.0  | 25   | 0.0114          | 1.0      |
| 0.1828        | 20.8  | 26   | 0.0039          | 1.0      |
| 0.105         | 21.6  | 27   | 0.0023          | 1.0      |
| 0.1075        | 22.4  | 28   | 0.0019          | 1.0      |
| 0.1202        | 24.0  | 30   | 0.0019          | 1.0      |
| 0.1821        | 24.8  | 31   | 0.0018          | 1.0      |
| 0.2104        | 25.6  | 32   | 0.0018          | 1.0      |
| 0.1069        | 26.4  | 33   | 0.0019          | 1.0      |
| 0.1012        | 28.0  | 35   | 0.0021          | 1.0      |
| 0.1168        | 28.8  | 36   | 0.0019          | 1.0      |
| 0.1041        | 29.6  | 37   | 0.0016          | 1.0      |
| 0.0966        | 30.4  | 38   | 0.0014          | 1.0      |
| 0.1018        | 32.0  | 40   | 0.0012          | 1.0      |
| 0.158         | 32.8  | 41   | 0.0012          | 1.0      |
| 0.1331        | 33.6  | 42   | 0.0011          | 1.0      |
| 0.1881        | 34.4  | 43   | 0.0011          | 1.0      |
| 0.1144        | 36.0  | 45   | 0.0011          | 1.0      |
| 0.1546        | 36.8  | 46   | 0.0011          | 1.0      |
| 0.0928        | 37.6  | 47   | 0.0010          | 1.0      |
| 0.1112        | 38.4  | 48   | 0.0010          | 1.0      |
| 0.1144        | 40.0  | 50   | 0.0010          | 1.0      |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-patch16-224-Trial007-YEL_STEM", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 1.0, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

vit-base-patch16-224-Trial007-YEL\_STEM
=======================================


This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1499
* Accuracy: 1.0


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: 60
* eval\_batch\_size: 60
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 240
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 50


### Training results



### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 1.12.1
* Datasets 2.12.0
* Tokenizers 0.13.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 60
* eval\_batch\_size: 60
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 240
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 1.12.1
* Datasets 2.12.0
* Tokenizers 0.13.1
TAGS
#transformers #pytorch #vit #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: 60
* eval\_batch\_size: 60
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 240
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 1.12.1
* Datasets 2.12.0
* Tokenizers 0.13.1