---
license: apache-2.0
base_model: Safawat/Electrcical-IMAGE-finetuned-eurosat
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Electrcical-IMAGE-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: 0.9034653465346535
---

<!-- 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. -->

# Electrcical-IMAGE-finetuned-eurosat

This model is a fine-tuned version of [Safawat/Electrcical-IMAGE-finetuned-eurosat](https://huggingface.co/Safawat/Electrcical-IMAGE-finetuned-eurosat) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3388
- Accuracy: 0.9035

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.2536        | 0.9825  | 28   | 0.3073          | 0.8985   |
| 0.1702        | 2.0     | 57   | 0.3259          | 0.8812   |
| 0.1528        | 2.9825  | 85   | 0.3370          | 0.8837   |
| 0.157         | 4.0     | 114  | 0.3373          | 0.8812   |
| 0.1577        | 4.9825  | 142  | 0.3574          | 0.8861   |
| 0.1485        | 6.0     | 171  | 0.3388          | 0.9035   |
| 0.1715        | 6.9825  | 199  | 0.3340          | 0.8936   |
| 0.2079        | 8.0     | 228  | 0.3255          | 0.8960   |
| 0.1846        | 8.9825  | 256  | 0.3368          | 0.8985   |
| 0.1829        | 10.0    | 285  | 0.3175          | 0.8985   |
| 0.1874        | 10.9825 | 313  | 0.3384          | 0.8911   |
| 0.1826        | 12.0    | 342  | 0.3284          | 0.8985   |
| 0.1911        | 12.9825 | 370  | 0.3504          | 0.8911   |
| 0.1552        | 14.0    | 399  | 0.3116          | 0.8936   |
| 0.1855        | 14.9825 | 427  | 0.3132          | 0.8960   |
| 0.1537        | 16.0    | 456  | 0.3000          | 0.9010   |
| 0.118         | 16.9825 | 484  | 0.3261          | 0.8985   |
| 0.1483        | 18.0    | 513  | 0.3143          | 0.8960   |
| 0.1169        | 18.9825 | 541  | 0.3102          | 0.9035   |
| 0.1268        | 19.6491 | 560  | 0.3104          | 0.8985   |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1