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# MAE

This model is a fine-tuned version of [facebook/vit-mae-base](https://huggingface.co/facebook/vit-mae-base) on the Circularmachines/batch_indexing_machine_224x224_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2263

## 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: 4.6875e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.249         | 1.0   | 7705  | 0.2445          |
| 0.2269        | 2.0   | 15410 | 0.2373          |
| 0.2401        | 3.0   | 23115 | 0.2334          |
| 0.2202        | 4.0   | 30820 | 0.2305          |
| 0.2173        | 5.0   | 38525 | 0.2283          |
| 0.2347        | 6.0   | 46230 | 0.2282          |
| 0.2304        | 7.0   | 53935 | 0.2268          |
| 0.2267        | 8.0   | 61640 | 0.2262          |
| 0.2177        | 9.0   | 69345 | 0.2254          |
| 0.2175        | 10.0  | 77050 | 0.2262          |


### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["masked-auto-encoding", "generated_from_trainer"], "model-index": [{"name": "MAE", "results": []}]}
transformers pytorch tensorboard vit_mae pretraining masked-auto-encoding generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit_mae #pretraining #masked-auto-encoding #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

MAE
===


This model is a fine-tuned version of facebook/vit-mae-base on the Circularmachines/batch\_indexing\_machine\_224x224\_images dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2263


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: 4.6875e-06
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 10.0


### Training results



### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4.6875e-06
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 10.0 ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vit_mae #pretraining #masked-auto-encoding #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4.6875e-06
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 10.0 ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3