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# hf_train_output

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 rock-glacier-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3894
- Accuracy: 0.9258

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5619        | 0.55  | 50   | 0.5432          | 0.7692   |
| 0.4582        | 1.1   | 100  | 0.4435          | 0.8352   |
| 0.3548        | 1.65  | 150  | 0.3739          | 0.8599   |
| 0.217         | 2.2   | 200  | 0.2913          | 0.9093   |
| 0.1709        | 2.75  | 250  | 0.2619          | 0.9148   |
| 0.0919        | 3.3   | 300  | 0.2475          | 0.9148   |
| 0.0652        | 3.85  | 350  | 0.3275          | 0.8901   |
| 0.0495        | 4.4   | 400  | 0.2515          | 0.9093   |
| 0.0321        | 4.95  | 450  | 0.2878          | 0.9066   |
| 0.0247        | 5.49  | 500  | 0.2612          | 0.9148   |
| 0.017         | 6.04  | 550  | 0.2687          | 0.9176   |
| 0.0131        | 6.59  | 600  | 0.3062          | 0.9093   |
| 0.0113        | 7.14  | 650  | 0.2587          | 0.9231   |
| 0.0099        | 7.69  | 700  | 0.2815          | 0.9203   |
| 0.009         | 8.24  | 750  | 0.2675          | 0.9286   |
| 0.0084        | 8.79  | 800  | 0.2711          | 0.9286   |
| 0.0077        | 9.34  | 850  | 0.2663          | 0.9313   |
| 0.0073        | 9.89  | 900  | 0.3003          | 0.9258   |
| 0.0069        | 10.44 | 950  | 0.2758          | 0.9313   |
| 0.0064        | 10.99 | 1000 | 0.2999          | 0.9258   |
| 0.0061        | 11.54 | 1050 | 0.2931          | 0.9313   |
| 0.0057        | 12.09 | 1100 | 0.2989          | 0.9313   |
| 0.0056        | 12.64 | 1150 | 0.2974          | 0.9313   |
| 0.0053        | 13.19 | 1200 | 0.3099          | 0.9258   |
| 0.005         | 13.74 | 1250 | 0.3131          | 0.9313   |
| 0.0049        | 14.29 | 1300 | 0.3201          | 0.9258   |
| 0.0046        | 14.84 | 1350 | 0.3109          | 0.9313   |
| 0.0045        | 15.38 | 1400 | 0.3168          | 0.9313   |
| 0.0043        | 15.93 | 1450 | 0.3226          | 0.9231   |
| 0.0042        | 16.48 | 1500 | 0.3234          | 0.9231   |
| 0.0041        | 17.03 | 1550 | 0.3283          | 0.9258   |
| 0.0039        | 17.58 | 1600 | 0.3304          | 0.9258   |
| 0.0038        | 18.13 | 1650 | 0.3321          | 0.9231   |
| 0.0037        | 18.68 | 1700 | 0.3362          | 0.9231   |
| 0.0036        | 19.23 | 1750 | 0.3307          | 0.9286   |
| 0.0035        | 19.78 | 1800 | 0.3357          | 0.9231   |
| 0.0034        | 20.33 | 1850 | 0.3244          | 0.9313   |
| 0.0033        | 20.88 | 1900 | 0.3497          | 0.9231   |
| 0.0032        | 21.43 | 1950 | 0.3443          | 0.9231   |
| 0.0031        | 21.98 | 2000 | 0.3398          | 0.9286   |
| 0.003         | 22.53 | 2050 | 0.3388          | 0.9286   |
| 0.003         | 23.08 | 2100 | 0.3399          | 0.9286   |
| 0.0029        | 23.63 | 2150 | 0.3548          | 0.9231   |
| 0.0028        | 24.18 | 2200 | 0.3475          | 0.9286   |
| 0.0028        | 24.73 | 2250 | 0.3480          | 0.9286   |
| 0.0027        | 25.27 | 2300 | 0.3542          | 0.9231   |
| 0.0026        | 25.82 | 2350 | 0.3589          | 0.9231   |
| 0.0026        | 26.37 | 2400 | 0.3449          | 0.9286   |
| 0.0025        | 26.92 | 2450 | 0.3604          | 0.9231   |
| 0.0025        | 27.47 | 2500 | 0.3493          | 0.9286   |
| 0.0024        | 28.02 | 2550 | 0.3631          | 0.9258   |
| 0.0024        | 28.57 | 2600 | 0.3590          | 0.9258   |
| 0.0023        | 29.12 | 2650 | 0.3604          | 0.9258   |
| 0.0023        | 29.67 | 2700 | 0.3667          | 0.9258   |
| 0.0022        | 30.22 | 2750 | 0.3571          | 0.9286   |
| 0.0022        | 30.77 | 2800 | 0.3660          | 0.9258   |
| 0.0021        | 31.32 | 2850 | 0.3638          | 0.9286   |
| 0.0021        | 31.87 | 2900 | 0.3729          | 0.9258   |
| 0.0021        | 32.42 | 2950 | 0.3706          | 0.9258   |
| 0.002         | 32.97 | 3000 | 0.3669          | 0.9286   |
| 0.002         | 33.52 | 3050 | 0.3740          | 0.9258   |
| 0.002         | 34.07 | 3100 | 0.3693          | 0.9286   |
| 0.002         | 34.62 | 3150 | 0.3700          | 0.9286   |
| 0.0019        | 35.16 | 3200 | 0.3752          | 0.9258   |
| 0.0019        | 35.71 | 3250 | 0.3753          | 0.9258   |
| 0.0019        | 36.26 | 3300 | 0.3721          | 0.9286   |
| 0.0018        | 36.81 | 3350 | 0.3764          | 0.9258   |
| 0.0018        | 37.36 | 3400 | 0.3758          | 0.9258   |
| 0.0018        | 37.91 | 3450 | 0.3775          | 0.9258   |
| 0.0018        | 38.46 | 3500 | 0.3812          | 0.9258   |
| 0.0018        | 39.01 | 3550 | 0.3817          | 0.9258   |
| 0.0017        | 39.56 | 3600 | 0.3815          | 0.9258   |
| 0.0017        | 40.11 | 3650 | 0.3825          | 0.9258   |
| 0.0017        | 40.66 | 3700 | 0.3852          | 0.9258   |
| 0.0017        | 41.21 | 3750 | 0.3854          | 0.9258   |
| 0.0017        | 41.76 | 3800 | 0.3823          | 0.9258   |
| 0.0016        | 42.31 | 3850 | 0.3829          | 0.9258   |
| 0.0016        | 42.86 | 3900 | 0.3873          | 0.9258   |
| 0.0016        | 43.41 | 3950 | 0.3842          | 0.9258   |
| 0.0016        | 43.96 | 4000 | 0.3857          | 0.9258   |
| 0.0016        | 44.51 | 4050 | 0.3873          | 0.9258   |
| 0.0016        | 45.05 | 4100 | 0.3878          | 0.9258   |
| 0.0016        | 45.6  | 4150 | 0.3881          | 0.9258   |
| 0.0016        | 46.15 | 4200 | 0.3888          | 0.9258   |
| 0.0016        | 46.7  | 4250 | 0.3891          | 0.9258   |
| 0.0016        | 47.25 | 4300 | 0.3878          | 0.9258   |
| 0.0016        | 47.8  | 4350 | 0.3890          | 0.9258   |
| 0.0016        | 48.35 | 4400 | 0.3890          | 0.9258   |
| 0.0015        | 48.9  | 4450 | 0.3895          | 0.9258   |
| 0.0015        | 49.45 | 4500 | 0.3896          | 0.9258   |
| 0.0015        | 50.0  | 4550 | 0.3894          | 0.9258   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2

glaciar cordillera