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# my_MFCC_VITmodelBBMetrics

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5158
- Accuracy: 0.8476
- F1: 0.8571
- Precision: 0.8727
- Recall: 0.8421

## 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: 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: 80

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 0.89  | 6    | 0.6917          | 0.5238   | 0.5098 | 0.5778    | 0.4561 |
| 0.691         | 1.93  | 13   | 0.6847          | 0.5333   | 0.6711 | 0.5435    | 0.8772 |
| 0.6845        | 2.96  | 20   | 0.6855          | 0.5905   | 0.5657 | 0.6667    | 0.4912 |
| 0.6845        | 4.0   | 27   | 0.6714          | 0.5905   | 0.6718 | 0.5946    | 0.7719 |
| 0.6618        | 4.89  | 33   | 0.6785          | 0.5714   | 0.7020 | 0.5638    | 0.9298 |
| 0.6491        | 5.93  | 40   | 0.6531          | 0.6381   | 0.6724 | 0.6610    | 0.6842 |
| 0.6491        | 6.96  | 47   | 0.6870          | 0.5619   | 0.5106 | 0.6486    | 0.4211 |
| 0.6385        | 8.0   | 54   | 0.6522          | 0.6381   | 0.5870 | 0.7714    | 0.4737 |
| 0.6255        | 8.89  | 60   | 0.6236          | 0.6857   | 0.736  | 0.6765    | 0.8070 |
| 0.6255        | 9.93  | 67   | 0.6916          | 0.5714   | 0.6400 | 0.5882    | 0.7018 |
| 0.6007        | 10.96 | 74   | 0.6111          | 0.6476   | 0.6992 | 0.6515    | 0.7544 |
| 0.5578        | 12.0  | 81   | 0.6595          | 0.6667   | 0.7407 | 0.6410    | 0.8772 |
| 0.5578        | 12.89 | 87   | 0.5702          | 0.6857   | 0.6857 | 0.75      | 0.6316 |
| 0.5362        | 13.93 | 94   | 0.6348          | 0.5905   | 0.6126 | 0.6296    | 0.5965 |
| 0.5233        | 14.96 | 101  | 0.5627          | 0.7143   | 0.7059 | 0.8       | 0.6316 |
| 0.5233        | 16.0  | 108  | 0.5433          | 0.6952   | 0.7288 | 0.7049    | 0.7544 |
| 0.4729        | 16.89 | 114  | 0.5223          | 0.7048   | 0.6593 | 0.8824    | 0.5263 |
| 0.4991        | 17.93 | 121  | 0.6039          | 0.6857   | 0.6598 | 0.8       | 0.5614 |
| 0.4991        | 18.96 | 128  | 0.5529          | 0.7048   | 0.7257 | 0.7321    | 0.7193 |
| 0.5122        | 20.0  | 135  | 0.6092          | 0.6476   | 0.7132 | 0.6389    | 0.8070 |
| 0.4328        | 20.89 | 141  | 0.4766          | 0.8      | 0.8174 | 0.8103    | 0.8246 |
| 0.4328        | 21.93 | 148  | 0.6353          | 0.6667   | 0.6067 | 0.8438    | 0.4737 |
| 0.4381        | 22.96 | 155  | 0.5544          | 0.6952   | 0.7091 | 0.7358    | 0.6842 |
| 0.3789        | 24.0  | 162  | 0.6128          | 0.6952   | 0.7143 | 0.7273    | 0.7018 |
| 0.3789        | 24.89 | 168  | 0.5411          | 0.7429   | 0.7429 | 0.8125    | 0.6842 |
| 0.3942        | 25.93 | 175  | 0.5121          | 0.7619   | 0.7619 | 0.8333    | 0.7018 |
| 0.3541        | 26.96 | 182  | 0.5795          | 0.7143   | 0.7500 | 0.7143    | 0.7895 |
| 0.3541        | 28.0  | 189  | 0.6004          | 0.7143   | 0.7115 | 0.7872    | 0.6491 |
| 0.3733        | 28.89 | 195  | 0.5270          | 0.7905   | 0.8036 | 0.8182    | 0.7895 |
| 0.3802        | 29.93 | 202  | 0.4939          | 0.7524   | 0.7451 | 0.8444    | 0.6667 |
| 0.3802        | 30.96 | 209  | 0.5092          | 0.7619   | 0.7863 | 0.7667    | 0.8070 |
| 0.3753        | 32.0  | 216  | 0.4730          | 0.7524   | 0.7903 | 0.7313    | 0.8596 |
| 0.3201        | 32.89 | 222  | 0.4910          | 0.8      | 0.8000 | 0.875     | 0.7368 |
| 0.3201        | 33.93 | 229  | 0.6333          | 0.7143   | 0.6809 | 0.8649    | 0.5614 |
| 0.3066        | 34.96 | 236  | 0.4247          | 0.8286   | 0.8364 | 0.8679    | 0.8070 |
| 0.2954        | 36.0  | 243  | 0.4680          | 0.7905   | 0.8167 | 0.7778    | 0.8596 |
| 0.2954        | 36.89 | 249  | 0.4455          | 0.8      | 0.8000 | 0.875     | 0.7368 |
| 0.3099        | 37.93 | 256  | 0.4517          | 0.7905   | 0.8036 | 0.8182    | 0.7895 |
| 0.2834        | 38.96 | 263  | 0.3856          | 0.8095   | 0.8246 | 0.8246    | 0.8246 |
| 0.2554        | 40.0  | 270  | 0.5559          | 0.7810   | 0.8034 | 0.7833    | 0.8246 |
| 0.2554        | 40.89 | 276  | 0.4823          | 0.7619   | 0.7706 | 0.8077    | 0.7368 |
| 0.2841        | 41.93 | 283  | 0.5250          | 0.7619   | 0.7573 | 0.8478    | 0.6842 |
| 0.2674        | 42.96 | 290  | 0.4649          | 0.7714   | 0.7931 | 0.7797    | 0.8070 |
| 0.2674        | 44.0  | 297  | 0.4545          | 0.7714   | 0.8    | 0.7619    | 0.8421 |
| 0.246         | 44.89 | 303  | 0.4981          | 0.7524   | 0.74   | 0.8605    | 0.6491 |
| 0.2304        | 45.93 | 310  | 0.4609          | 0.8095   | 0.8039 | 0.9111    | 0.7193 |
| 0.2304        | 46.96 | 317  | 0.5103          | 0.7810   | 0.7890 | 0.8269    | 0.7544 |
| 0.2102        | 48.0  | 324  | 0.3872          | 0.8667   | 0.8727 | 0.9057    | 0.8421 |
| 0.2115        | 48.89 | 330  | 0.5134          | 0.7905   | 0.8070 | 0.8070    | 0.8070 |
| 0.2115        | 49.93 | 337  | 0.4609          | 0.8095   | 0.8113 | 0.8776    | 0.7544 |
| 0.2404        | 50.96 | 344  | 0.4087          | 0.8381   | 0.8571 | 0.8226    | 0.8947 |
| 0.2125        | 52.0  | 351  | 0.5572          | 0.7524   | 0.7547 | 0.8163    | 0.7018 |
| 0.2125        | 52.89 | 357  | 0.5962          | 0.7619   | 0.7899 | 0.7581    | 0.8246 |
| 0.2167        | 53.93 | 364  | 0.4890          | 0.7714   | 0.7778 | 0.8235    | 0.7368 |
| 0.2012        | 54.96 | 371  | 0.5476          | 0.7619   | 0.7899 | 0.7581    | 0.8246 |
| 0.2012        | 56.0  | 378  | 0.4451          | 0.7905   | 0.7885 | 0.8723    | 0.7193 |
| 0.1925        | 56.89 | 384  | 0.5473          | 0.7714   | 0.7895 | 0.7895    | 0.7895 |
| 0.17          | 57.93 | 391  | 0.5303          | 0.8      | 0.7921 | 0.9091    | 0.7018 |
| 0.17          | 58.96 | 398  | 0.5442          | 0.7905   | 0.8070 | 0.8070    | 0.8070 |
| 0.2189        | 60.0  | 405  | 0.4505          | 0.8095   | 0.8214 | 0.8364    | 0.8070 |
| 0.2148        | 60.89 | 411  | 0.4706          | 0.8095   | 0.8148 | 0.8627    | 0.7719 |
| 0.2148        | 61.93 | 418  | 0.4652          | 0.7905   | 0.8167 | 0.7778    | 0.8596 |
| 0.1773        | 62.96 | 425  | 0.4383          | 0.8571   | 0.8624 | 0.9038    | 0.8246 |
| 0.1807        | 64.0  | 432  | 0.4327          | 0.8381   | 0.8468 | 0.8704    | 0.8246 |
| 0.1807        | 64.89 | 438  | 0.5184          | 0.8      | 0.8174 | 0.8103    | 0.8246 |
| 0.159         | 65.93 | 445  | 0.3494          | 0.8476   | 0.8596 | 0.8596    | 0.8596 |
| 0.172         | 66.96 | 452  | 0.4707          | 0.7810   | 0.7928 | 0.8148    | 0.7719 |
| 0.172         | 68.0  | 459  | 0.4849          | 0.8286   | 0.8364 | 0.8679    | 0.8070 |
| 0.1886        | 68.89 | 465  | 0.5062          | 0.8190   | 0.8376 | 0.8167    | 0.8596 |
| 0.1493        | 69.93 | 472  | 0.4996          | 0.7714   | 0.7818 | 0.8113    | 0.7544 |
| 0.1493        | 70.96 | 479  | 0.5345          | 0.7810   | 0.7928 | 0.8148    | 0.7719 |
| 0.1523        | 71.11 | 480  | 0.5158          | 0.8476   | 0.8571 | 0.8727    | 0.8421 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "my_MFCC_VITmodelBBMetrics", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8476190476190476, "name": "Accuracy"}, {"type": "f1", "value": 0.8571428571428571, "name": "F1"}, {"type": "precision", "value": 0.8727272727272727, "name": "Precision"}, {"type": "recall", "value": 0.8421052631578947, "name": "Recall"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us