
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# my_awesome_pokemon_model

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pokemon-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 7.3838
- Accuracy: 0.0755

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.926         | 1.0   | 76   | 5.4705          | 0.0007   |
| 3.7521        | 1.99  | 152  | 5.9651          | 0.0129   |
| 1.9692        | 2.99  | 228  | 5.8631          | 0.0144   |
| 0.7605        | 4.0   | 305  | 5.9688          | 0.0482   |
| 0.4163        | 5.0   | 381  | 6.1329          | 0.0655   |
| 0.3085        | 5.99  | 457  | 6.2311          | 0.0806   |
| 0.2155        | 6.99  | 533  | 6.4040          | 0.0683   |
| 0.2188        | 8.0   | 610  | 6.4869          | 0.0748   |
| 0.2241        | 9.0   | 686  | 6.6527          | 0.0763   |
| 0.1505        | 9.99  | 762  | 6.7076          | 0.0755   |
| 0.1429        | 10.99 | 838  | 6.7627          | 0.0719   |
| 0.1378        | 12.0  | 915  | 6.8740          | 0.0712   |
| 0.1335        | 13.0  | 991  | 6.9456          | 0.0741   |
| 0.1335        | 13.99 | 1067 | 6.8821          | 0.0748   |
| 0.1131        | 14.99 | 1143 | 6.9655          | 0.0763   |
| 0.1041        | 16.0  | 1220 | 7.0660          | 0.0763   |
| 0.0844        | 17.0  | 1296 | 7.1479          | 0.0770   |
| 0.086         | 17.99 | 1372 | 7.1182          | 0.0748   |
| 0.1028        | 18.99 | 1448 | 7.1395          | 0.0734   |
| 0.0456        | 20.0  | 1525 | 7.2099          | 0.0748   |
| 0.0617        | 21.0  | 1601 | 7.2512          | 0.0734   |
| 0.0711        | 21.99 | 1677 | 7.3157          | 0.0813   |
| 0.0623        | 22.99 | 1753 | 7.2590          | 0.0791   |
| 0.0419        | 24.0  | 1830 | 7.3413          | 0.0712   |
| 0.0924        | 25.0  | 1906 | 7.3051          | 0.0784   |
| 0.0471        | 25.99 | 1982 | 7.3136          | 0.0763   |
| 0.0654        | 26.99 | 2058 | 7.3667          | 0.0734   |
| 0.0836        | 28.0  | 2135 | 7.4039          | 0.0770   |
| 0.06          | 29.0  | 2211 | 7.3998          | 0.0799   |
| 0.0694        | 29.9  | 2280 | 7.3838          | 0.0755   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["pokemon-classification"], "metrics": ["accuracy"], "model-index": [{"name": "my_awesome_pokemon_model", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "pokemon-classification", "type": "pokemon-classification", "config": "full", "split": "validation", "args": "full"}, "metrics": [{"type": "accuracy", "value": 0.07553956834532374, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:pokemon-classification license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-pokemon-classification #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

my\_awesome\_pokemon\_model
===========================


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


* Loss: 7.3838
* Accuracy: 0.0755


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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 30


### Training results



### Framework versions


* Transformers 4.29.2
* 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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-pokemon-classification #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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3