---
license: other
base_model: nvidia/mit-b0
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: segformer-finetuned-food101
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: food101
      type: food101
      config: default
      split: train[:5000]
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.888
---

<!-- 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. -->

# segformer-finetuned-food101

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3478
- Accuracy: 0.888

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0272        | 0.98  | 23   | 1.8039          | 0.329    |
| 1.5806        | 2.0   | 47   | 1.2465          | 0.608    |
| 1.0564        | 2.98  | 70   | 0.7507          | 0.756    |
| 0.7358        | 4.0   | 94   | 0.6263          | 0.784    |
| 0.6482        | 4.98  | 117  | 0.5551          | 0.795    |
| 0.5692        | 6.0   | 141  | 0.5849          | 0.794    |
| 0.5552        | 6.98  | 164  | 0.4931          | 0.831    |
| 0.4956        | 8.0   | 188  | 0.5166          | 0.83     |
| 0.4748        | 8.98  | 211  | 0.4808          | 0.834    |
| 0.424         | 10.0  | 235  | 0.4238          | 0.852    |
| 0.4314        | 10.98 | 258  | 0.4858          | 0.838    |
| 0.4071        | 12.0  | 282  | 0.4304          | 0.858    |
| 0.3928        | 12.98 | 305  | 0.4621          | 0.851    |
| 0.3695        | 14.0  | 329  | 0.4398          | 0.859    |
| 0.3704        | 14.98 | 352  | 0.4172          | 0.855    |
| 0.3299        | 16.0  | 376  | 0.4225          | 0.856    |
| 0.3391        | 16.98 | 399  | 0.4165          | 0.855    |
| 0.3023        | 18.0  | 423  | 0.3828          | 0.869    |
| 0.3318        | 18.98 | 446  | 0.4190          | 0.861    |
| 0.2994        | 20.0  | 470  | 0.4190          | 0.861    |
| 0.323         | 20.98 | 493  | 0.4034          | 0.866    |
| 0.2883        | 22.0  | 517  | 0.4083          | 0.874    |
| 0.2959        | 22.98 | 540  | 0.4202          | 0.862    |
| 0.2665        | 24.0  | 564  | 0.3740          | 0.881    |
| 0.2765        | 24.98 | 587  | 0.4123          | 0.866    |
| 0.2728        | 26.0  | 611  | 0.3763          | 0.868    |
| 0.2817        | 26.98 | 634  | 0.3939          | 0.864    |
| 0.2467        | 28.0  | 658  | 0.3938          | 0.87     |
| 0.2772        | 28.98 | 681  | 0.4013          | 0.866    |
| 0.2243        | 29.36 | 690  | 0.3478          | 0.888    |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0