---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- fl_image_category_ds
metrics:
- accuracy
model-index:
- name: fl_image_category
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: fl_image_category_ds
      type: fl_image_category_ds
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6216216216216216
---

<!-- 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. -->

# fl_image_category

This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the fl_image_category_ds dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9667
- Accuracy: 0.6216

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.274         | 1.0   | 88   | 1.2030          | 0.4986   |
| 1.069         | 2.0   | 176  | 1.0716          | 0.5605   |
| 1.0592        | 3.0   | 264  | 1.0385          | 0.5676   |
| 0.9571        | 4.0   | 352  | 0.9746          | 0.6131   |
| 0.8975        | 5.0   | 440  | 0.9667          | 0.6216   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2