---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- rock-glacier-dataset
metrics:
- accuracy
model-index:
- name: skynet
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: rock-glacier-dataset
      type: rock-glacier-dataset
      config: image-classification
      split: train
      args: image-classification
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9688888888888889
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# skynet

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: 0.1080
- Accuracy: 0.9689

## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4521        | 0.3   | 75   | 0.4436          | 0.824    |
| 0.3561        | 0.61  | 150  | 0.2802          | 0.9244   |
| 0.2306        | 0.91  | 225  | 0.2124          | 0.9307   |
| 0.1621        | 1.21  | 300  | 0.1695          | 0.9458   |
| 0.1396        | 1.52  | 375  | 0.1589          | 0.9476   |
| 0.1157        | 1.82  | 450  | 0.1342          | 0.9547   |
| 0.0707        | 2.13  | 525  | 0.1342          | 0.96     |
| 0.0578        | 2.43  | 600  | 0.1294          | 0.9591   |
| 0.0687        | 2.73  | 675  | 0.1285          | 0.9609   |
| 0.0431        | 3.04  | 750  | 0.1066          | 0.9671   |
| 0.0249        | 3.34  | 825  | 0.1069          | 0.968    |
| 0.0614        | 3.64  | 900  | 0.1073          | 0.968    |
| 0.0469        | 3.95  | 975  | 0.1080          | 0.9689   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2