transformers tensorboard safetensors deit image-classification vision generated_from_trainer base_model:facebook/deit-base-distilled-patch16-224 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/deit-base-distilled-patch16-224
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: "DeiT-base-DatasetDict({\n    train: Dataset({\n        features: ['img',\
    \ 'fine_label', 'coarse_label'],\n        num_rows: 50000\n    })\n    test: Dataset({\n\
    \        features: ['img', 'fine_label', 'coarse_label'],\n        num_rows: 10000\n\
    \    })\n    validation: Dataset({\n        features: ['img', 'fine_label', 'coarse_label'],\n\
    \        num_rows: 10000\n    })\n})"
  results: []
---

<!-- 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. -->

# DeiT-base-DatasetDict({
    train: Dataset({
        features: ['img', 'fine_label', 'coarse_label'],
        num_rows: 50000
    })
    test: Dataset({
        features: ['img', 'fine_label', 'coarse_label'],
        num_rows: 10000
    })
    validation: Dataset({
        features: ['img', 'fine_label', 'coarse_label'],
        num_rows: 10000
    })
})

This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the cifar100 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3054
- Accuracy: 0.906

## 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: 64
- eval_batch_size: 1
- seed: 777
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 1.1232        | 1.0   | 782  | 0.8416   | 0.5390          |
| 0.9017        | 2.0   | 1564 | 0.8699   | 0.4365          |
| 0.7565        | 3.0   | 2346 | 0.8858   | 0.3678          |
| 0.706         | 4.0   | 3128 | 0.8952   | 0.3446          |
| 0.6353        | 5.0   | 3910 | 0.8986   | 0.3331          |
| 0.5384        | 6.0   | 4692 | 0.9001   | 0.3223          |
| 0.5004        | 7.0   | 5474 | 0.9018   | 0.3249          |
| 0.4672        | 8.0   | 6256 | 0.904    | 0.3113          |
| 0.4526        | 9.0   | 7038 | 0.9054   | 0.3081          |
| 0.4289        | 10.0  | 7820 | 0.906    | 0.3054          |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
