metadata
license: apache-2.0
tags:
- generated_from_trainer
- image-classification
- pytorch
datasets:
- food101
metrics:
- accuracy
model_index:
- name: food101_outputs
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: nateraw/food101
type: food101
args: default
metric:
name: Accuracy
type: accuracy
value: 0.8912871287128713
food101_outputs
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the nateraw/food101 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4501
- Accuracy: 0.8913
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: 128
- eval_batch_size: 128
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8271 | 1.0 | 592 | 0.6070 | 0.8562 |
| 0.4376 | 2.0 | 1184 | 0.4947 | 0.8691 |
| 0.2089 | 3.0 | 1776 | 0.4876 | 0.8747 |
| 0.0882 | 4.0 | 2368 | 0.4639 | 0.8857 |
| 0.0452 | 5.0 | 2960 | 0.4501 | 0.8913 |
Framework versions
- Transformers 4.9.0.dev0
- Pytorch 1.9.0+cu102
- Datasets 1.9.1.dev0
- Tokenizers 0.10.3