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

Loss: 1.2517
Accuracy: 0.5876

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: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.7547	0.9811	13	2.3422	0.3285
1.7119	1.9623	26	1.8850	0.4964
1.249	2.9434	39	1.5653	0.5292
0.8838	4.0	53	1.3675	0.5693
0.8896	4.9811	66	1.2907	0.5803
0.7262	5.9623	79	1.2625	0.5803
0.6817	6.8679	91	1.2517	0.5876

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

sharmajai901/UL_interior_classification

UL_interior_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sharmajai901/
UL_interior_classification