---
library_name: transformers
license: apache-2.0
base_model: microsoft/resnet-50
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: 0.5625
---

<!-- 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: 0.6309
- Accuracy: 0.5625

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 3    | 0.6749          | 0.5625   |
| No log        | 2.0   | 6    | 0.6746          | 0.5625   |
| No log        | 3.0   | 9    | 0.6696          | 0.5625   |
| 2.1049        | 4.0   | 12   | 0.6614          | 0.5312   |
| 2.1049        | 5.0   | 15   | 0.6552          | 0.5625   |
| 2.1049        | 6.0   | 18   | 0.6494          | 0.5625   |
| 2.0436        | 7.0   | 21   | 0.6427          | 0.5625   |
| 2.0436        | 8.0   | 24   | 0.6399          | 0.5625   |
| 2.0436        | 9.0   | 27   | 0.6325          | 0.5625   |
| 1.7828        | 10.0  | 30   | 0.6314          | 0.5625   |
| 1.7828        | 11.0  | 33   | 0.6309          | 0.5625   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0