metadata

license: apache-2.0
tags:
  - generated_from_trainer
  - image-classification
  - pytorch
datasets:
  - food101
metrics:
  - accuracy
base_model: google/vit-base-patch16-224-in21k
model-index:
  - name: food101_outputs
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: food-101
          type: food101
          args: default
        metrics:
          - type: accuracy
            value: 0.8912871287128713
            name: Accuracy

nateraw/food

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