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: 0.0123
Accuracy: 0.9978

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0989	0.17	500	0.0477	0.9871
0.0531	0.33	1000	0.0493	0.9865
0.046	0.5	1500	0.0381	0.9894
0.0392	0.67	2000	0.1129	0.9734
0.0459	0.83	2500	0.0364	0.9904
0.0255	1.0	3000	0.0305	0.9934
0.0188	1.17	3500	0.0247	0.9949
0.0222	1.33	4000	0.0206	0.9921
0.02	1.5	4500	0.0154	0.9952
0.0191	1.67	5000	0.0132	0.9952
0.0141	1.83	5500	0.0294	0.9905
0.0201	2.0	6000	0.0155	0.9968
0.0114	2.17	6500	0.0161	0.9965
0.0071	2.33	7000	0.0124	0.9975
0.0083	2.5	7500	0.0141	0.9969
0.0143	2.67	8000	0.0242	0.9932
0.0088	2.83	8500	0.0123	0.9972
0.0034	3.0	9000	0.0120	0.9972
0.0064	3.17	9500	0.0100	0.9978
0.0012	3.33	10000	0.0166	0.996
0.006	3.5	10500	0.0110	0.998
0.0007	3.67	11000	0.0126	0.9972
0.0034	3.83	11500	0.0122	0.9979
0.0057	4.0	12000	0.0123	0.9978

Framework versions

Transformers 4.29.0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3

zetazlife/fisura-hormigon

fisura-hormigon

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

zetazlife/
fisura-hormigon