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# finetuned-pokemon

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the pokemon_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5483
- Accuracy: 0.8849

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2294        | 0.18  | 100  | 4.2177          | 0.2408   |
| 3.3443        | 0.35  | 200  | 3.3392          | 0.4415   |
| 2.6829        | 0.53  | 300  | 2.5744          | 0.6848   |
| 2.1544        | 0.71  | 400  | 1.9695          | 0.7430   |
| 1.5132        | 0.88  | 500  | 1.4847          | 0.7761   |
| 0.8297        | 1.06  | 600  | 1.1503          | 0.7974   |
| 0.6333        | 1.23  | 700  | 0.8776          | 0.8349   |
| 0.4634        | 1.41  | 800  | 0.7625          | 0.8462   |
| 0.4646        | 1.59  | 900  | 0.7139          | 0.8293   |
| 0.4092        | 1.76  | 1000 | 0.6812          | 0.8368   |
| 0.4207        | 1.94  | 1100 | 0.6669          | 0.8330   |
| 0.2222        | 2.12  | 1200 | 0.6346          | 0.8449   |
| 0.201         | 2.29  | 1300 | 0.5713          | 0.8574   |
| 0.2236        | 2.47  | 1400 | 0.5948          | 0.8587   |
| 0.2694        | 2.65  | 1500 | 0.5563          | 0.8680   |
| 0.1744        | 2.82  | 1600 | 0.5784          | 0.8593   |
| 0.2472        | 3.0   | 1700 | 0.6019          | 0.8518   |
| 0.0779        | 3.17  | 1800 | 0.5899          | 0.8693   |
| 0.0966        | 3.35  | 1900 | 0.5861          | 0.8587   |
| 0.0962        | 3.53  | 2000 | 0.5981          | 0.8630   |
| 0.1396        | 3.7   | 2100 | 0.6345          | 0.8455   |
| 0.083         | 3.88  | 2200 | 0.5822          | 0.8668   |
| 0.0649        | 4.06  | 2300 | 0.6148          | 0.8530   |
| 0.0476        | 4.23  | 2400 | 0.5906          | 0.8687   |
| 0.1119        | 4.41  | 2500 | 0.5902          | 0.8649   |
| 0.0748        | 4.59  | 2600 | 0.5671          | 0.8780   |
| 0.0934        | 4.76  | 2700 | 0.5834          | 0.8724   |
| 0.0648        | 4.94  | 2800 | 0.6095          | 0.8580   |
| 0.051         | 5.11  | 2900 | 0.6209          | 0.8574   |
| 0.0341        | 5.29  | 3000 | 0.5671          | 0.8780   |
| 0.1553        | 5.47  | 3100 | 0.5838          | 0.8730   |
| 0.0425        | 5.64  | 3200 | 0.5808          | 0.8712   |
| 0.0269        | 5.82  | 3300 | 0.5734          | 0.8693   |
| 0.1104        | 6.0   | 3400 | 0.5823          | 0.8718   |
| 0.1136        | 6.17  | 3500 | 0.5698          | 0.8774   |
| 0.0276        | 6.35  | 3600 | 0.5996          | 0.8699   |
| 0.0408        | 6.53  | 3700 | 0.5642          | 0.8768   |
| 0.0147        | 6.7   | 3800 | 0.5913          | 0.8762   |
| 0.0395        | 6.88  | 3900 | 0.5838          | 0.8630   |
| 0.014         | 7.05  | 4000 | 0.5722          | 0.8762   |
| 0.025         | 7.23  | 4100 | 0.5749          | 0.8768   |
| 0.0536        | 7.41  | 4200 | 0.5693          | 0.8806   |
| 0.0602        | 7.58  | 4300 | 0.5483          | 0.8849   |
| 0.0618        | 7.76  | 4400 | 0.5597          | 0.8774   |
| 0.0447        | 7.94  | 4500 | 0.5539          | 0.8812   |
| 0.0811        | 8.11  | 4600 | 0.5692          | 0.8762   |
| 0.0514        | 8.29  | 4700 | 0.5665          | 0.8812   |
| 0.0635        | 8.47  | 4800 | 0.5737          | 0.8793   |
| 0.0474        | 8.64  | 4900 | 0.5817          | 0.8799   |
| 0.03          | 8.82  | 5000 | 0.5833          | 0.8774   |
| 0.0224        | 8.99  | 5100 | 0.5743          | 0.8818   |
| 0.0205        | 9.17  | 5200 | 0.5794          | 0.8831   |
| 0.0326        | 9.35  | 5300 | 0.5838          | 0.8806   |
| 0.0617        | 9.52  | 5400 | 0.5777          | 0.8812   |
| 0.019         | 9.7   | 5500 | 0.5781          | 0.8806   |
| 0.0412        | 9.88  | 5600 | 0.5797          | 0.8806   |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "finetuned-pokemon", "results": []}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

finetuned-pokemon
=================


This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the pokemon\_images dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5483
* Accuracy: 0.8849


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: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3