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vit-part1-friends

Image Classification
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vit-part1-friends
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---
license: apache-2.0
base_model: moreover18/vit-base-patch16-224-in21k-YB
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-part1-friends
  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: 0.9381107491856677
---

<!-- 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. -->

# vit-part1-friends

This model is a fine-tuned version of [moreover18/vit-base-patch16-224-in21k-YB](https://huggingface.co/moreover18/vit-base-patch16-224-in21k-YB) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2036
- Accuracy: 0.9381

## 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: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1627        | 1.3   | 50   | 0.2258          | 0.9202   |
| 0.1183        | 2.6   | 100  | 0.2148          | 0.9235   |
| 0.1223        | 3.9   | 150  | 0.2055          | 0.9267   |
| 0.0992        | 5.19  | 200  | 0.1976          | 0.9332   |
| 0.0824        | 6.49  | 250  | 0.2036          | 0.9381   |


### Framework versions

- Transformers 4.37.1
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.15.1