This model is a fine-tuned version of google/vit-large-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0444
Accuracy: 0.9872

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 64
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 0.1
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7011	1.0	89	0.5072	0.8933
0.3277	2.0	178	0.1940	0.9572
0.2666	3.0	267	0.1520	0.9569
0.2031	4.0	356	0.1297	0.9509
0.1648	5.0	445	0.1150	0.9667
0.1991	6.0	534	0.1269	0.9594
0.1467	7.0	623	0.0700	0.9822
0.1347	8.0	712	0.0576	0.9853
0.0974	9.0	801	0.0518	0.9857
0.0647	10.0	890	0.0444	0.9872

Framework versions

Transformers 5.5.4
Pytorch 2.11.0+cu130
Datasets 4.8.4
Tokenizers 0.22.2

byungkwankim/vit-large-rfuav-drone-detection

vit-large-rfuav-drone-detection

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

byungkwankim/
vit-large-rfuav-drone-detection