README.md

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: food-101
          type: food101
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8912871287128713

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