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# beit-base-patch16-224-pt22k-ft22k-rim_one-new

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4550
- Accuracy: 0.8767

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.73  | 2    | 0.2411          | 0.9178   |
| No log        | 1.73  | 4    | 0.2182          | 0.8973   |
| No log        | 2.73  | 6    | 0.3085          | 0.8973   |
| No log        | 3.73  | 8    | 0.2794          | 0.8973   |
| 0.1392        | 4.73  | 10   | 0.2398          | 0.9110   |
| 0.1392        | 5.73  | 12   | 0.2925          | 0.8973   |
| 0.1392        | 6.73  | 14   | 0.2798          | 0.9110   |
| 0.1392        | 7.73  | 16   | 0.2184          | 0.9178   |
| 0.1392        | 8.73  | 18   | 0.3007          | 0.9110   |
| 0.0416        | 9.73  | 20   | 0.3344          | 0.9041   |
| 0.0416        | 10.73 | 22   | 0.3626          | 0.9110   |
| 0.0416        | 11.73 | 24   | 0.4842          | 0.8904   |
| 0.0416        | 12.73 | 26   | 0.3664          | 0.8973   |
| 0.0416        | 13.73 | 28   | 0.3458          | 0.9110   |
| 0.0263        | 14.73 | 30   | 0.2810          | 0.9110   |
| 0.0263        | 15.73 | 32   | 0.4695          | 0.8699   |
| 0.0263        | 16.73 | 34   | 0.3723          | 0.9041   |
| 0.0263        | 17.73 | 36   | 0.3447          | 0.9041   |
| 0.0263        | 18.73 | 38   | 0.3708          | 0.8904   |
| 0.0264        | 19.73 | 40   | 0.4052          | 0.9110   |
| 0.0264        | 20.73 | 42   | 0.4492          | 0.9041   |
| 0.0264        | 21.73 | 44   | 0.4649          | 0.8904   |
| 0.0264        | 22.73 | 46   | 0.4061          | 0.9178   |
| 0.0264        | 23.73 | 48   | 0.4136          | 0.9110   |
| 0.0139        | 24.73 | 50   | 0.4183          | 0.8973   |
| 0.0139        | 25.73 | 52   | 0.4504          | 0.8904   |
| 0.0139        | 26.73 | 54   | 0.4368          | 0.8973   |
| 0.0139        | 27.73 | 56   | 0.4711          | 0.9110   |
| 0.0139        | 28.73 | 58   | 0.3928          | 0.9110   |
| 0.005         | 29.73 | 60   | 0.4550          | 0.8767   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "beit-base-patch16-224-pt22k-ft22k-rim_one-new", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "RIM ONE DL", "type": "rimonedl", "split": "test"}, "metrics": [{"type": "f1", "value": 0.9197860962566845, "name": "F1"}]}, {"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "RIMONEDL", "type": "rim one", "split": "test"}, "metrics": [{"type": "precision", "value": 0.9247311827956989, "name": "precision"}, {"type": "recall", "value": 0.9148936170212766, "name": "Recall"}, {"type": "accuracy", "value": 0.8972602739726028, "name": "Accuracy"}, {"type": "roc_auc", "value": 0.8901391162029461, "name": "AUC"}]}]}]}
transformers pytorch tensorboard beit image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #beit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

beit-base-patch16-224-pt22k-ft22k-rim\_one-new
==============================================


This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4550
* Accuracy: 0.8767


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: 30


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1



### 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: 30 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #beit #image-classification #generated_from_trainer #dataset-imagefolder #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: 30 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #beit #image-classification #generated_from_trainer #dataset-imagefolder #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: 30### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1