File size: 1,484 Bytes
f5ad972 42e23b8 f5ad972 e797da1 f5ad972 42e23b8 f5ad972 42e23b8 f5ad972 42e23b8 f5ad972 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: google-vit-base-patch16-224-in21k-finetuned-food-classification-86M-v0.1
results: []
---
# food-classification-86M-v0.1
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6079
- Accuracy: 0.892
## Model description
Food image classification.
## Intended uses & limitations
This was trained for fun and my own learning. But if you want to use it, go ahead.
### 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7263 | 0.99 | 62 | 2.5435 | 0.816 |
| 1.8437 | 2.0 | 125 | 1.7773 | 0.863 |
| 1.5811 | 2.98 | 186 | 1.6079 | 0.892 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|