metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - ethz/food101
metrics:
  - accuracy
model-index:
  - name: google/vit-base-patch16-224-in21k-v2-finetuned
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: food101
          type: ethz/food101
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7968976897689769
language:
  - en
pipeline_tag: image-classification

google/vit-base-patch16-224-in21k-v2-finetuned

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: 1.0612
Accuracy: 0.7969

Model description

Model type: Language model
Language(s) (NLP): English
License: Apache 2.0
Related Model: google/vit-base-patch16-224-in21k
Original Checkpoints: google/vit-base-patch16-224-in21k
Resources for more information: Research paper

Intended uses & limitations

This model can be used to classify what type of food in the image provided.

Training and evaluation data

The model was trained on food101 dataset with 80:20 train-test-split.

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
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.9201	1.0	947	1.9632	0.7297
1.2002	2.0	1894	1.2327	0.7805
0.9561	3.0	2841	1.0612	0.7969

Framework versions

Transformers 4.40.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1