metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: finetuned-indian-food
    results: []

finetuned-indian-food

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

Loss: 0.2293
Accuracy: 0.9405
Precision: 0.9395
Recall: 0.9420
F1: 0.9402

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	Precision	Recall	F1
0.8589	0.3	100	0.5618	0.8714	0.8981	0.8620	0.8696
0.6973	0.6	200	0.5544	0.8608	0.8742	0.8690	0.8630
0.4078	0.9	300	0.4671	0.8831	0.8915	0.8840	0.8812
0.3818	1.2	400	0.4203	0.8884	0.9017	0.8864	0.8877
0.2262	1.5	500	0.3481	0.9107	0.9177	0.9085	0.9098
0.2137	1.8	600	0.3761	0.9022	0.9094	0.9027	0.9026
0.4515	2.1	700	0.3722	0.9044	0.9091	0.9041	0.9017
0.3024	2.4	800	0.3105	0.9203	0.9198	0.9220	0.9188
0.1748	2.7	900	0.2767	0.9288	0.9274	0.9293	0.9272
0.1959	3.0	1000	0.2825	0.9256	0.9318	0.9243	0.9230
0.1663	3.3	1100	0.2549	0.9341	0.9362	0.9366	0.9356
0.0513	3.6	1200	0.2254	0.9416	0.9436	0.9422	0.9424
0.1478	3.9	1300	0.2293	0.9405	0.9395	0.9420	0.9402

Framework versions

Transformers 4.39.3
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2