metadata

license: apache-2.0
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
widget:
  - src: >-
      https://huggingface.co/rajistics/finetuned-indian-food/resolve/main/003.jpg
    example_title: fried_rice
  - src: >-
      https://huggingface.co/rajistics/finetuned-indian-food/resolve/main/126.jpg
    example_title: paani_puri
  - src: >-
      https://huggingface.co/rajistics/finetuned-indian-food/resolve/main/401.jpg
    example_title: chapati
base_model: google/vit-base-patch16-224-in21k
model-index:
  - name: finetuned-indian-food
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: indian_food_images
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - type: accuracy
            value: 0.9330499468650372
            name: Accuracy

finetuned-indian-food

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the indian_food_images dataset. It achieves the following results on the evaluation set:

Loss: 0.2632
Accuracy: 0.9330

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: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.1794	0.3	100	0.9208	0.8565
0.6513	0.6	200	0.5410	0.8842
0.5904	0.9	300	0.4978	0.8799
0.4461	1.2	400	0.3669	0.9192
0.5633	1.5	500	0.4340	0.8842
0.2489	1.8	600	0.3355	0.9171
0.3171	2.1	700	0.3286	0.9192
0.3785	2.4	800	0.3232	0.9171
0.2278	2.7	900	0.3338	0.9192
0.0894	3.0	1000	0.2870	0.9245
0.2092	3.3	1100	0.2884	0.9288
0.1466	3.6	1200	0.2673	0.9320
0.1789	3.9	1300	0.2632	0.9330

Framework versions

Transformers 4.21.1
Pytorch 1.12.0+cu113
Datasets 2.4.0
Tokenizers 0.12.1