---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch32-384
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch32-384-finetuned-eurosat-albumentations
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9726027397260274
---

<!-- 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. -->

# vit-base-patch32-384-finetuned-eurosat-albumentations

This model is a fine-tuned version of [google/vit-base-patch32-384](https://huggingface.co./google/vit-base-patch32-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1871
- Accuracy: 0.9726

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.7204        | 0.9412  | 12   | 0.5695          | 0.7397   |
| 0.4269        | 1.9804  | 25   | 0.2537          | 0.9178   |
| 0.1605        | 2.9412  | 37   | 0.3347          | 0.8767   |
| 0.0758        | 3.9804  | 50   | 0.2203          | 0.9041   |
| 0.0405        | 4.9412  | 62   | 0.3563          | 0.9178   |
| 0.0358        | 5.9804  | 75   | 0.2326          | 0.9315   |
| 0.0188        | 6.9412  | 87   | 0.2046          | 0.9315   |
| 0.026         | 7.9804  | 100  | 0.2195          | 0.8904   |
| 0.0582        | 8.9412  | 112  | 0.3378          | 0.9178   |
| 0.0113        | 9.9804  | 125  | 0.2685          | 0.9178   |
| 0.0081        | 10.9412 | 137  | 0.2443          | 0.9315   |
| 0.0091        | 11.9804 | 150  | 0.4675          | 0.9041   |
| 0.0065        | 12.9412 | 162  | 0.3252          | 0.9452   |
| 0.0026        | 13.9804 | 175  | 0.1871          | 0.9726   |
| 0.0043        | 14.9412 | 187  | 0.2256          | 0.9589   |
| 0.0094        | 15.9804 | 200  | 0.1980          | 0.9452   |
| 0.0028        | 16.9412 | 212  | 0.2928          | 0.9315   |
| 0.0003        | 17.9804 | 225  | 0.2241          | 0.9726   |
| 0.0006        | 18.9412 | 237  | 0.2396          | 0.9726   |
| 0.0012        | 19.9804 | 250  | 0.2663          | 0.9315   |
| 0.0001        | 20.9412 | 262  | 0.2266          | 0.9726   |
| 0.0002        | 21.9804 | 275  | 0.2637          | 0.9452   |
| 0.0001        | 22.9412 | 287  | 0.2873          | 0.9452   |
| 0.0003        | 23.9804 | 300  | 0.2068          | 0.9589   |
| 0.0001        | 24.9412 | 312  | 0.2485          | 0.9452   |
| 0.0047        | 25.9804 | 325  | 0.3375          | 0.9178   |
| 0.0015        | 26.9412 | 337  | 0.3132          | 0.9589   |
| 0.0001        | 27.9804 | 350  | 0.3148          | 0.9452   |
| 0.0025        | 28.9412 | 362  | 0.2533          | 0.9452   |
| 0.0038        | 29.9804 | 375  | 0.2860          | 0.9315   |
| 0.0025        | 30.9412 | 387  | 0.2785          | 0.9452   |
| 0.0031        | 31.9804 | 400  | 0.3246          | 0.9452   |
| 0.0           | 32.9412 | 412  | 0.3367          | 0.9452   |
| 0.0006        | 33.9804 | 425  | 0.2625          | 0.9726   |
| 0.0           | 34.9412 | 437  | 0.2689          | 0.9589   |
| 0.0007        | 35.9804 | 450  | 0.2891          | 0.9726   |
| 0.0003        | 36.9412 | 462  | 0.4523          | 0.9315   |
| 0.0003        | 37.9804 | 475  | 0.3426          | 0.9452   |
| 0.0001        | 38.9412 | 487  | 0.3167          | 0.9589   |
| 0.0           | 39.9804 | 500  | 0.3237          | 0.9589   |
| 0.0002        | 40.9412 | 512  | 0.3085          | 0.9589   |
| 0.0           | 41.9804 | 525  | 0.3095          | 0.9589   |
| 0.0           | 42.9412 | 537  | 0.3049          | 0.9589   |
| 0.0002        | 43.9804 | 550  | 0.3039          | 0.9589   |
| 0.0001        | 44.9412 | 562  | 0.3044          | 0.9589   |
| 0.0001        | 45.9804 | 575  | 0.3031          | 0.9726   |
| 0.0           | 46.9412 | 587  | 0.3028          | 0.9726   |
| 0.0           | 47.9804 | 600  | 0.3027          | 0.9726   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 2.21.0
- Tokenizers 0.20.3