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# beit-finetuned-pokemon

This model is a fine-tuned version of [ydmeira/beit-finetuned-pokemon](https://huggingface.co/ydmeira/beit-finetuned-pokemon) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0219
- Mean Iou: 0.4955
- Mean Accuracy: 0.9910
- Overall Accuracy: 0.9910
- Per Category Iou: [0.0, 0.9909617791470107]
- Per Category Accuracy: [nan, 0.9909617791470107]

## 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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou          | Per Category Accuracy     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------:|:-------------------------:|
| 0.0354        | 0.21  | 1000  | 0.0347          | 0.4978   | 0.9955        | 0.9955           | [0.0, 0.9955007125868244] | [nan, 0.9955007125868244] |
| 0.0273        | 0.43  | 2000  | 0.0277          | 0.4951   | 0.9903        | 0.9903           | [0.0, 0.9902709092544748] | [nan, 0.9902709092544748] |
| 0.0307        | 0.64  | 3000  | 0.0788          | 0.4875   | 0.9751        | 0.9751           | [0.0, 0.9750850921785902] | [nan, 0.9750850921785902] |
| 0.0295        | 0.85  | 4000  | 0.0412          | 0.4939   | 0.9877        | 0.9877           | [0.0, 0.9877162657609527] | [nan, 0.9877162657609527] |
| 0.0255        | 1.07  | 5000  | 0.0842          | 0.4862   | 0.9723        | 0.9723           | [0.0, 0.972304346385062]  | [nan, 0.972304346385062]  |
| 0.0253        | 1.28  | 6000  | 0.0325          | 0.4950   | 0.9901        | 0.9901           | [0.0, 0.9900621363084688] | [nan, 0.9900621363084688] |
| 0.0239        | 1.49  | 7000  | 0.0440          | 0.4917   | 0.9835        | 0.9835           | [0.0, 0.9834701005512881] | [nan, 0.9834701005512881] |
| 0.0238        | 1.71  | 8000  | 0.0338          | 0.4950   | 0.9900        | 0.9900           | [0.0, 0.9899977115151821] | [nan, 0.9899977115151821] |
| 0.0223        | 1.92  | 9000  | 0.0319          | 0.4950   | 0.9900        | 0.9900           | [0.0, 0.989994712810938]  | [nan, 0.989994712810938]  |
| 0.0231        | 2.13  | 10000 | 0.0382          | 0.4921   | 0.9841        | 0.9841           | [0.0, 0.984106425591889]  | [nan, 0.984106425591889]  |
| 0.0205        | 2.35  | 11000 | 0.0450          | 0.4926   | 0.9851        | 0.9851           | [0.0, 0.9851146530893756] | [nan, 0.9851146530893756] |
| 0.0201        | 2.56  | 12000 | 0.0265          | 0.4954   | 0.9908        | 0.9908           | [0.0, 0.9908277212846449] | [nan, 0.9908277212846449] |
| 0.0188        | 2.77  | 13000 | 0.0377          | 0.4933   | 0.9866        | 0.9866           | [0.0, 0.9865726862234793] | [nan, 0.9865726862234793] |
| 0.0181        | 2.99  | 14000 | 0.0219          | 0.4955   | 0.9910        | 0.9910           | [0.0, 0.9909617791470107] | [nan, 0.9909617791470107] |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "beit-finetuned-pokemon", "results": []}]}
transformers pytorch tensorboard beit generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #beit #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

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


This model is a fine-tuned version of ydmeira/beit-finetuned-pokemon on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0219
* Mean Iou: 0.4955
* Mean Accuracy: 0.9910
* Overall Accuracy: 0.9910
* Per Category Iou: [0.0, 0.9909617791470107]
* Per Category Accuracy: [nan, 0.9909617791470107]


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: 6e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.22.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.22.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #beit #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.22.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #beit #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3### Training results### Framework versions


* Transformers 4.22.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1