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# vit-base-food101

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the ethz/food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7395
- Accuracy: 0.8017

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|
| 2.5327        | 0.1320 | 500   | 2.3914          | 0.5946   |
| 1.5713        | 0.2640 | 1000  | 1.5558          | 0.6978   |
| 1.2869        | 0.3960 | 1500  | 1.2575          | 0.7271   |
| 1.1479        | 0.5280 | 2000  | 1.1093          | 0.7476   |
| 1.0838        | 0.6600 | 2500  | 1.0286          | 0.7571   |
| 0.9623        | 0.7920 | 3000  | 0.9798          | 0.7641   |
| 0.9855        | 0.9240 | 3500  | 0.9395          | 0.7670   |
| 0.9263        | 1.0560 | 4000  | 0.9113          | 0.7723   |
| 0.8691        | 1.1880 | 4500  | 0.8844          | 0.7782   |
| 0.8025        | 1.3200 | 5000  | 0.8694          | 0.7768   |
| 0.7783        | 1.4520 | 5500  | 0.8574          | 0.7820   |
| 0.7774        | 1.5839 | 6000  | 0.8457          | 0.7799   |
| 0.7716        | 1.7159 | 6500  | 0.8309          | 0.7871   |
| 0.8445        | 1.8479 | 7000  | 0.8230          | 0.7868   |
| 0.8214        | 1.9799 | 7500  | 0.8107          | 0.7902   |
| 0.7226        | 2.1119 | 8000  | 0.8077          | 0.7897   |
| 0.7712        | 2.2439 | 8500  | 0.8015          | 0.7914   |
| 0.7306        | 2.3759 | 9000  | 0.7970          | 0.7889   |
| 0.6829        | 2.5079 | 9500  | 0.7919          | 0.7912   |
| 0.7593        | 2.6399 | 10000 | 0.7883          | 0.7901   |
| 0.6856        | 2.7719 | 10500 | 0.7802          | 0.7943   |
| 0.7156        | 2.9039 | 11000 | 0.7765          | 0.7976   |
| 0.6688        | 3.0359 | 11500 | 0.7735          | 0.7978   |
| 0.6245        | 3.1679 | 12000 | 0.7711          | 0.7972   |
| 0.668         | 3.2999 | 12500 | 0.7679          | 0.7989   |
| 0.6732        | 3.4319 | 13000 | 0.7657          | 0.7985   |
| 0.686         | 3.5639 | 13500 | 0.7645          | 0.7982   |
| 0.7121        | 3.6959 | 14000 | 0.7612          | 0.7984   |
| 0.6513        | 3.8279 | 14500 | 0.7599          | 0.7993   |
| 0.6963        | 3.9599 | 15000 | 0.7585          | 0.7993   |
| 0.7219        | 4.0919 | 15500 | 0.7554          | 0.7999   |
| 0.6253        | 4.2239 | 16000 | 0.7526          | 0.8016   |
| 0.6278        | 4.3559 | 16500 | 0.7504          | 0.8026   |
| 0.6605        | 4.4879 | 17000 | 0.7502          | 0.8028   |
| 0.6447        | 4.6199 | 17500 | 0.7493          | 0.8028   |
| 0.6469        | 4.7518 | 18000 | 0.7463          | 0.8040   |
| 0.6745        | 4.8838 | 18500 | 0.7462          | 0.8028   |
| 0.5882        | 5.0158 | 19000 | 0.7463          | 0.7995   |
| 0.6241        | 5.1478 | 19500 | 0.7428          | 0.8046   |
| 0.62          | 5.2798 | 20000 | 0.7439          | 0.8013   |
| 0.6435        | 5.4118 | 20500 | 0.7422          | 0.8018   |
| 0.6273        | 5.5438 | 21000 | 0.7418          | 0.8030   |
| 0.623         | 5.6758 | 21500 | 0.7415          | 0.8050   |
| 0.6181        | 5.8078 | 22000 | 0.7385          | 0.8055   |
| 0.6382        | 5.9398 | 22500 | 0.7388          | 0.8071   |
| 0.587         | 6.0718 | 23000 | 0.7379          | 0.8058   |
| 0.603         | 6.2038 | 23500 | 0.7374          | 0.8038   |
| 0.6334        | 6.3358 | 24000 | 0.7366          | 0.8054   |
| 0.613         | 6.4678 | 24500 | 0.7364          | 0.8048   |
| 0.5917        | 6.5998 | 25000 | 0.7355          | 0.8051   |
| 0.6167        | 6.7318 | 25500 | 0.7352          | 0.8059   |
| 0.6121        | 6.8638 | 26000 | 0.7347          | 0.8066   |
| 0.6133        | 6.9958 | 26500 | 0.7342          | 0.8059   |
| 0.6304        | 7.1278 | 27000 | 0.7338          | 0.8057   |
| 0.6041        | 7.2598 | 27500 | 0.7342          | 0.8063   |
| 0.6333        | 7.3918 | 28000 | 0.7334          | 0.8059   |
| 0.6234        | 7.5238 | 28500 | 0.7335          | 0.8061   |
| 0.5961        | 7.6558 | 29000 | 0.7334          | 0.8073   |
| 0.61          | 7.7878 | 29500 | 0.7333          | 0.8070   |
| 0.6586        | 7.9197 | 30000 | 0.7331          | 0.8070   |


### Framework versions

- Transformers 4.50.0
- Pytorch 2.6.0
- Datasets 3.4.1
- Tokenizers 0.21.1

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