README.md

---
base_model: microsoft/dit-large
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: MRR_image_classification_dit_29_jan_small75-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: 0.47560975609756095
---

<!-- 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_small75-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: 1.5785
- Accuracy: 0.4756

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8795        | 0.98  | 10   | 1.6437          | 0.3049   |
| 1.6681        | 1.95  | 20   | 1.6446          | 0.4146   |
| 1.5603        | 2.93  | 30   | 1.5785          | 0.4756   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1