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

nielsr/van-base-finetuned-eurosat-imgaug

van-base-finetuned-eurosat-imgaug

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nielsr/
van-base-finetuned-eurosat-imgaug