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: 2.3870
Accuracy: 0.5658

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: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 2
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
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
4.7526	1.0	612	4.7474	0.2541
3.9574	2.0	1224	3.8794	0.4050
3.4665	3.0	1836	3.3852	0.4621
3.0017	4.0	2448	3.0551	0.4944
2.7217	5.0	3060	2.8251	0.5137
2.5752	6.0	3672	2.6569	0.5399
2.5064	7.0	4284	2.5447	0.5501
2.3956	8.0	4896	2.4493	0.5631
2.1768	9.0	5508	2.4040	0.5631
2.2168	10.0	6120	2.3870	0.5658

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1

abdumalikov/image-classification-v1

recomendation-system

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

abdumalikov/
image-classification-v1