README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - image_folder
metrics:
  - accuracy
base_model: Visual-Attention-Network/van-base
model-index:
  - name: van-base-finetuned-eurosat-imgaug
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: image_folder
          type: image_folder
          args: default
        metrics:
          - type: accuracy
            value: 0.9885185185185185
            name: Accuracy

van-base-finetuned-eurosat-imgaug

This model is a fine-tuned version of Visual-Attention-Network/van-base on the image_folder dataset. It achieves the following results on the evaluation set:

• Loss: 0.0379
• Accuracy: 0.9885

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
0.0887	1.0	190	0.0589	0.98
0.055	2.0	380	0.0390	0.9878
0.0223	3.0	570	0.0379	0.9885

Framework versions

• Transformers 4.18.0
• Pytorch 1.10.0+cu111
• Datasets 2.0.0
• Tokenizers 0.11.6