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: 0.0103
Precision: 0.9987
Recall: 0.9987

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: 0.0002
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall
0.0797	1.0	190	0.0811	0.9789	0.9775
0.0536	2.0	380	0.0205	0.9947	0.9947
0.0374	3.0	570	0.0283	0.9922	0.9921
0.0209	4.0	760	0.0046	1.0	1.0
0.0104	5.0	950	0.0128	0.9960	0.9960
0.0159	6.0	1140	0.0152	0.9947	0.9947
0.0119	7.0	1330	0.0084	0.9974	0.9974
0.0044	8.0	1520	0.0111	0.9987	0.9987
0.0077	9.0	1710	0.0094	0.9987	0.9987
0.0106	10.0	1900	0.0103	0.9987	0.9987

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Tokenizers 0.15.0

Madhukar7559/vit-fire-detection

vit-fire-detection

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Madhukar7559/
vit-fire-detection