---
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: 0.5026722090261283
---

<!-- 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: 1.5983
- Accuracy: 0.5027

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.89  | 6    | 1.7745          | 0.2838   |
| 1.9066        | 1.89  | 12   | 1.7447          | 0.3895   |
| 1.9066        | 2.89  | 18   | 1.7216          | 0.4154   |
| 1.9875        | 3.89  | 24   | 1.6914          | 0.4311   |
| 1.8094        | 4.89  | 30   | 1.6659          | 0.4629   |
| 1.8094        | 5.89  | 36   | 1.6485          | 0.4852   |
| 1.8906        | 6.89  | 42   | 1.6278          | 0.4869   |
| 1.8906        | 7.89  | 48   | 1.6098          | 0.5021   |
| 1.8516        | 8.89  | 54   | 1.6131          | 0.5175   |
| 1.718         | 9.89  | 60   | 1.5983          | 0.5027   |


### Framework versions

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