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: 0.3125
Accuracy: 0.8921

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
0.8296	0.9756	20	0.5683	0.8230
0.4462	2.0	41	0.3949	0.8603
0.3588	2.9756	61	0.3633	0.8575
0.3196	4.0	82	0.3247	0.8852
0.2921	4.9756	102	0.3374	0.8728
0.2688	6.0	123	0.3125	0.8921
0.2366	6.8293	140	0.3137	0.8866

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1

sharmajai901/UL_base_classification

UL_base_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sharmajai901/
UL_base_classification