metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
base_model: google/vit-base-patch32-224-in21k
model-index:
  - name: vit-base-patch32-224-in21k-finetuned-eurosat
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - type: accuracy
            value: 0.9944827586206897
            name: Accuracy

vit-base-patch32-224-in21k-finetuned-eurosat

This model is a fine-tuned version of google/vit-base-patch32-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.8115
Accuracy: 0.9945

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
1.8903	1.0	102	1.5728	0.9517
1.2226	2.0	204	0.9374	0.9917
1.1069	3.0	306	0.8115	0.9945

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.8.0
Tokenizers 0.13.2