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# beit-sketch-classifier-pt-metaset

This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6732
- Accuracy: 0.8277

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.8069        | 1.0   | 76608  | 0.7673          | 0.7988   |
| 0.6922        | 2.0   | 153216 | 0.6982          | 0.8159   |
| 0.6289        | 3.0   | 229824 | 0.6709          | 0.8236   |
| 0.5332        | 4.0   | 306432 | 0.6635          | 0.8271   |
| 0.4283        | 5.0   | 383040 | 0.6732          | 0.8277   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "beit-sketch-classifier-pt-metaset", "results": []}]}
transformers pytorch beit image-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #beit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

beit-sketch-classifier-pt-metaset
=================================


This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6732
* Accuracy: 0.8277


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: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #beit #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: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #beit #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: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2