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# vit-base-patch16-224-finetuned-eurosat

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6541
- Accuracy: 0.8389

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0843        | 1.0   | 266  | 0.9241          | 0.7967   |
| 0.8596        | 2.0   | 533  | 0.7022          | 0.8322   |
| 0.6834        | 2.99  | 798  | 0.6541          | 0.8389   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"tags": ["generated_from_trainer"], "datasets": ["food101"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224", "model-index": [{"name": "vit-base-patch16-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "food101", "type": "food101", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.838943894389439, "name": "Accuracy"}]}]}]}
transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:food101 base_model:google/vit-base-patch16-224 model-index autotrain_compatible endpoints_compatible region:us