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---
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
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