SPIE_MULTICLASS_GOOGLE_2_0

This model is a fine-tuned version of WinKawaks/vit-tiny-patch16-224 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.2216
• Accuracy: 0.9189

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: 0
• 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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.7954	0.96	18	0.6255	0.7842
0.4474	1.9733	37	0.3137	0.8890
0.2726	2.9867	56	0.2842	0.8996
0.2299	4.0	75	0.2312	0.9235
0.2007	4.8	90	0.2216	0.9189

Framework versions

• Transformers 4.45.1
• Pytorch 2.4.0
• Datasets 3.0.1
• Tokenizers 0.20.0