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

Loss: 1.4039
Accuracy: 0.2941

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.9231	3	1.5830	0.3137
No log	1.8462	6	1.5342	0.2941
No log	2.7692	9	1.4845	0.2941
1.5308	4.0	13	1.4705	0.2745
1.5308	4.9231	16	1.4534	0.3137
1.5308	5.8462	19	1.4583	0.2745
1.3601	6.7692	22	1.4218	0.2941
1.3601	8.0	26	1.4283	0.2745
1.3601	8.9231	29	1.3973	0.3137
1.2778	9.2308	30	1.4039	0.2941

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

pimcore/car-countries-classification

car-countries-classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

pimcore/
car-countries-classification