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

Loss: 2.3072
Accuracy: 0.459

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: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.0023	1.0	63	2.8601	0.389
2.5716	2.0	126	2.4576	0.42
2.3300	3.0	189	2.3072	0.459

Framework versions

Transformers 5.10.1
Pytorch 2.11.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

Valencio/LLM_course_ViT_model_image_classification

LLM_course_ViT_model_image_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Valencio/
LLM_course_ViT_model_image_classification