---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-50-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: 0.8239812959251837
---

<!-- 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. -->

# resnet-50-finetuned-eurosat

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9095
- Accuracy: 0.8240

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.78          | 0.96  | 17   | 1.7432          | 0.4321   |
| 1.7105        | 1.96  | 34   | 1.6596          | 0.6307   |
| 1.6045        | 2.96  | 51   | 1.5369          | 0.6758   |
| 1.6526        | 3.96  | 68   | 1.4111          | 0.7139   |
| 1.4018        | 4.96  | 85   | 1.2686          | 0.7602   |
| 1.2812        | 5.96  | 102  | 1.1433          | 0.7714   |
| 1.3282        | 6.96  | 119  | 1.0643          | 0.7910   |
| 1.1246        | 7.96  | 136  | 0.9794          | 0.8133   |
| 1.0731        | 8.96  | 153  | 0.9279          | 0.8087   |
| 1.0531        | 9.96  | 170  | 0.9095          | 0.8240   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1