|
--- |
|
license: apache-2.0 |
|
base_model: google/vit-base-patch16-224-in21k |
|
tags: |
|
- image-classification |
|
- generated_from_trainer |
|
datasets: |
|
- imagefolder |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: finetuned-food |
|
results: |
|
- task: |
|
name: Image Classification |
|
type: image-classification |
|
dataset: |
|
name: food_images_classification |
|
type: imagefolder |
|
config: default |
|
split: train |
|
args: default |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9281675392670157 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# finetuned-food |
|
|
|
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 food_images_classification dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2816 |
|
- Accuracy: 0.9282 |
|
|
|
## 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: 0.0002 |
|
- train_batch_size: 15 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 4 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 0.8456 | 0.39 | 500 | 0.8593 | 0.7634 | |
|
| 0.7824 | 0.78 | 1000 | 0.6625 | 0.8172 | |
|
| 0.4806 | 1.18 | 1500 | 0.4951 | 0.8618 | |
|
| 0.6206 | 1.57 | 2000 | 0.4434 | 0.88 | |
|
| 0.5096 | 1.96 | 2500 | 0.4937 | 0.8683 | |
|
| 0.4576 | 2.35 | 3000 | 0.4060 | 0.8907 | |
|
| 0.3284 | 2.75 | 3500 | 0.3414 | 0.9081 | |
|
| 0.2022 | 3.14 | 4000 | 0.3330 | 0.9118 | |
|
| 0.1332 | 3.53 | 4500 | 0.3043 | 0.9208 | |
|
| 0.1821 | 3.92 | 5000 | 0.2816 | 0.9282 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.32.1 |
|
- Pytorch 2.2.0.post100 |
|
- Datasets 2.12.0 |
|
- Tokenizers 0.13.2 |
|
|
