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# prove_melanomaprova_melanoma

This model is a fine-tuned version of [UnipaPolitoUnimore/vit-large-patch32-384-melanoma](https://huggingface.co/UnipaPolitoUnimore/vit-large-patch32-384-melanoma) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5191
- Accuracy: 0.8467

## 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: 1e-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: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8964        | 0.99  | 31   | 1.0906          | 0.52     |
| 0.6588        | 1.98  | 62   | 1.0817          | 0.52     |
| 0.6774        | 2.98  | 93   | 0.9474          | 0.52     |
| 0.7785        | 4.0   | 125  | 0.8185          | 0.6267   |
| 0.6732        | 4.99  | 156  | 0.7531          | 0.7267   |
| 0.5438        | 5.98  | 187  | 0.6972          | 0.7333   |
| 0.5497        | 6.98  | 218  | 0.6714          | 0.7533   |
| 0.4161        | 8.0   | 250  | 0.6440          | 0.7667   |
| 0.4968        | 8.99  | 281  | 0.6438          | 0.78     |
| 0.5861        | 9.98  | 312  | 0.6266          | 0.7933   |
| 0.5182        | 10.98 | 343  | 0.6158          | 0.7867   |
| 0.6797        | 12.0  | 375  | 0.6237          | 0.8133   |
| 0.622         | 12.99 | 406  | 0.5858          | 0.8333   |
| 0.6419        | 13.98 | 437  | 0.5735          | 0.8267   |
| 0.3727        | 14.98 | 468  | 0.5641          | 0.8133   |
| 0.3822        | 16.0  | 500  | 0.5520          | 0.8267   |
| 0.4766        | 16.99 | 531  | 0.5642          | 0.8267   |
| 0.4791        | 17.98 | 562  | 0.5309          | 0.8267   |
| 0.3918        | 18.98 | 593  | 0.5749          | 0.8267   |
| 0.3847        | 20.0  | 625  | 0.5317          | 0.84     |
| 0.3722        | 20.99 | 656  | 0.5719          | 0.8267   |
| 0.5402        | 21.98 | 687  | 0.5316          | 0.84     |
| 0.4358        | 22.98 | 718  | 0.5292          | 0.8333   |
| 0.2957        | 24.0  | 750  | 0.5172          | 0.8467   |
| 0.4801        | 24.99 | 781  | 0.5376          | 0.84     |
| 0.3656        | 25.98 | 812  | 0.5118          | 0.8333   |
| 0.3956        | 26.98 | 843  | 0.5081          | 0.8533   |
| 0.3343        | 28.0  | 875  | 0.5198          | 0.8533   |
| 0.3839        | 28.99 | 906  | 0.5269          | 0.8467   |
| 0.4286        | 29.98 | 937  | 0.5163          | 0.8467   |
| 0.2736        | 30.98 | 968  | 0.5359          | 0.8333   |
| 0.3465        | 32.0  | 1000 | 0.5277          | 0.84     |
| 0.4244        | 32.99 | 1031 | 0.5385          | 0.8333   |
| 0.308         | 33.98 | 1062 | 0.5141          | 0.8533   |
| 0.3494        | 34.98 | 1093 | 0.5129          | 0.8533   |
| 0.3851        | 36.0  | 1125 | 0.5199          | 0.84     |
| 0.3949        | 36.99 | 1156 | 0.5250          | 0.84     |
| 0.3235        | 37.98 | 1187 | 0.5142          | 0.8533   |
| 0.3076        | 38.98 | 1218 | 0.5166          | 0.8533   |
| 0.3679        | 39.68 | 1240 | 0.5191          | 0.8467   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "prove_melanomaprova_melanoma", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8466666666666667, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

prove\_melanomaprova\_melanoma
==============================


This model is a fine-tuned version of UnipaPolitoUnimore/vit-large-patch32-384-melanoma on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5191
* Accuracy: 0.8467


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: 1e-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: 40


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 40 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 40 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 40### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3