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smids_deit_base_f1

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
smids_deit_base_f1
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---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_deit_base_f1
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8747913188647746
---

<!-- 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. -->

# smids_deit_base_f1

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4898
- Accuracy: 0.8748

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3398        | 1.0   | 375  | 0.4288          | 0.8164   |
| 0.2944        | 2.0   | 750  | 0.4228          | 0.8297   |
| 0.1957        | 3.0   | 1125 | 0.4014          | 0.8497   |
| 0.176         | 4.0   | 1501 | 0.4565          | 0.8514   |
| 0.1333        | 5.0   | 1876 | 0.3698          | 0.8731   |
| 0.1322        | 6.0   | 2251 | 0.5002          | 0.8481   |
| 0.0952        | 7.0   | 2626 | 0.4711          | 0.8648   |
| 0.0941        | 8.0   | 3002 | 0.4872          | 0.8698   |
| 0.0946        | 9.0   | 3377 | 0.5003          | 0.8564   |
| 0.0911        | 9.99  | 3750 | 0.4898          | 0.8748   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2