license: apache-2.0 | |
tags: | |
- generated_from_trainer | |
datasets: | |
- food101 | |
metrics: | |
- accuracy | |
model-index: | |
- name: my_awesome_food_model | |
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: 0.879 | |
<!-- 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. --> | |
# my_awesome_food_model | |
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: 1.6510 | |
- Accuracy: 0.879 | |
## 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: 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 | | |
|:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| 2.7286 | 0.99 | 62 | 2.5619 | 0.828 | | |
| 1.8685 | 2.0 | 125 | 1.8156 | 0.866 | | |
| 1.6392 | 2.98 | 186 | 1.6510 | 0.879 | | |
### Framework versions | |
- Transformers 4.28.1 | |
- Pytorch 2.0.0+cu118 | |
- Datasets 2.11.0 | |
- Tokenizers 0.13.3 | |