vit-fire-detection – AI Model by EdBianchi | AlphaNeural AI

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EdBianchi/
vit-fire-detection

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image-classification

generated_from_trainer

google/vit-base-patch16-224-in21k

autotrain_compatible

endpoints_compatible

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vit-fire-detection

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0126
Precision: 0.9960
Recall: 0.9960

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
0.1018	1.0	190	0.0375	0.9934	0.9934
0.0484	2.0	380	0.0167	0.9961	0.9960
0.0357	3.0	570	0.0253	0.9948	0.9947
0.0133	4.0	760	0.0198	0.9961	0.9960
0.012	5.0	950	0.0203	0.9947	0.9947
0.0139	6.0	1140	0.0204	0.9947	0.9947
0.0076	7.0	1330	0.0175	0.9961	0.9960
0.0098	8.0	1520	0.0115	0.9974	0.9974
0.0062	9.0	1710	0.0133	0.9960	0.9960
0.0012	10.0	1900	0.0126	0.9960	0.9960

Framework versions

Transformers 4.25.1
Pytorch 1.14.0.dev20221111
Datasets 2.8.0
Tokenizers 0.12.1