metadata
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[:20200]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8853960396039604
my_awesome_food_model
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the food101 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4703
- Accuracy: 0.8854
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.4019 | 1.0 | 1010 | 1.3796 | 0.8156 |
| 0.6238 | 2.0 | 2020 | 0.6604 | 0.8448 |
| 0.3691 | 3.0 | 3030 | 0.5661 | 0.8522 |
| 0.3947 | 4.0 | 4040 | 0.5226 | 0.8614 |
| 0.3511 | 5.0 | 5050 | 0.5125 | 0.8644 |
| 0.2504 | 6.0 | 6060 | 0.5180 | 0.8656 |
| 0.1285 | 7.0 | 7070 | 0.5312 | 0.8668 |
| 0.2301 | 8.0 | 8080 | 0.4779 | 0.875 |
| 0.0844 | 9.0 | 9090 | 0.4823 | 0.8839 |
| 0.1189 | 10.0 | 10100 | 0.4703 | 0.8854 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3