---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: isl-nodel
  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: 0.7540407589599438
---

<!-- 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. -->

# isl-nodel

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9554
- Accuracy: 0.7540

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6213        | 1.0   | 89   | 2.3886          | 0.6128   |
| 1.66          | 2.0   | 178  | 1.5769          | 0.7119   |
| 1.3588        | 3.0   | 267  | 1.3264          | 0.7358   |
| 1.1062        | 4.0   | 356  | 1.1833          | 0.7386   |
| 1.1883        | 5.0   | 445  | 1.1025          | 0.7442   |
| 1.159         | 6.0   | 534  | 1.0324          | 0.7505   |
| 0.9934        | 7.0   | 623  | 0.9626          | 0.7674   |
| 0.8885        | 8.0   | 712  | 1.0080          | 0.7435   |
| 0.9325        | 9.0   | 801  | 0.9395          | 0.7681   |
| 0.9254        | 10.0  | 890  | 0.9554          | 0.7540   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3