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: 0.0584
Accuracy: 0.9887

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: 256
eval_batch_size: 256
seed: 1337
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 390
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.5611	1.0	196	0.6937	0.9571
0.4895	2.0	392	0.2266	0.9816
0.3321	3.0	588	0.1464	0.9781
0.2757	4.0	784	0.0966	0.985
0.2305	5.0	980	0.0869	0.9833
0.2114	6.0	1176	0.0707	0.987
0.1924	7.0	1372	0.0612	0.9879
0.1852	8.0	1568	0.0595	0.9881
0.1720	9.0	1764	0.0590	0.9887
0.1675	10.0	1960	0.0583	0.9886

Framework versions

Transformers 5.15.1
Pytorch 2.6.0+cu124
Datasets 4.4.1
Tokenizers 0.22.1

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

Try ML Intern: https://smolagents-ml-intern.hf.space
Source code: https://github.com/huggingface/ml-intern

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'hanying/vit-base-cifar10'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.