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: 0.4783
Accuracy: 0.85

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	0.8	2	0.6696	0.75
No log	2.0	5	0.6296	0.7
No log	2.8	7	0.5853	0.775
0.639	4.0	10	0.5731	0.8
0.639	4.8	12	0.5430	0.825
0.639	6.0	15	0.5223	0.85
0.639	6.8	17	0.5036	0.8
0.5453	8.0	20	0.4783	0.85

Framework versions

Transformers 4.39.1
Pytorch 2.2.1+cpu
Datasets 2.18.0
Tokenizers 0.15.2

akhileshav8/image_classification_for_fracture

image_classification_for_fracture

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

akhileshav8/
$image_classification_for_fracture$
image_classification_for_fracture