README.md

metadata

license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagenet-1k
metrics:
  - accuracy
model-index:
  - name: vit-base-patch16-224-finetuned-eurosat
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagenet-1k
          type: imagenet-1k
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.817

vit-base-patch16-224-finetuned-eurosat

This model is a fine-tuned version of google/vit-base-patch16-224 on the imagenet-1k dataset. It achieves the following results on the evaluation set:

• Loss: 0.6981
• Accuracy: 0.817

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: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• 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
0.8014	1.0	10009	0.7430	0.8052
0.6591	2.0	20018	0.7097	0.8132
0.562	3.0	30027	0.6981	0.817

Framework versions

• Transformers 4.39.3
• Pytorch 2.2.2+cu118
• Datasets 2.19.1
• Tokenizers 0.15.2