Full notebook :

https://github.com/MustafaAlahmid/hugging_face_models/blob/main/Vit-classifier_food_dataset.ipynb

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license: apache-2.0 tags: - generated_from_trainer datasets: - food101 metrics: - accuracy model-index: - name: my_awesome_food_model results: - task: name: Image Classification type: image-classification dataset: name: food101 type: food101 config: default split: train[:1000] args: default metrics: - name: Accuracy type: accuracy value: 0.985

my_awesome_food_model

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: 1.2335
• Accuracy: 0.985

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: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• 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

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.0523	1.0	50	1.9226	0.935
1.3718	2.0	100	1.3422	0.995
1.2298	3.0	150	1.2335	0.985

Framework versions

• Transformers 4.26.0
• Pytorch 1.13.1+cu116
• Datasets 2.9.0
• Tokenizers 0.13.2