---
base_model: microsoft/dit-large
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: MRR_image_classification_dit_29_jan-finetuned-eurosat
  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: 0.8250355618776671
---

<!-- 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. -->

# MRR_image_classification_dit_29_jan-finetuned-eurosat

This model is a fine-tuned version of [microsoft/dit-large](https://huggingface.co/microsoft/dit-large) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4995
- Accuracy: 0.8250

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0588        | 1.0   | 175  | 0.8931          | 0.6622   |
| 0.7206        | 2.0   | 351  | 0.6266          | 0.7774   |
| 0.6833        | 2.99  | 525  | 0.4995          | 0.8250   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1