This model is a fine-tuned version of google/vit-base-patch16-384 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0087
Accuracy: 1.0
F1 Macro: 1.0
Precision Macro: 1.0
Recall Macro: 1.0
Precision Norockies: 1.0
Recall Norockies: 1.0
F1 Norockies: 1.0
Precision Rockies: 1.0
Recall Rockies: 1.0
F1 Rockies: 1.0

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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Precision Macro	Recall Macro	Precision Norockies	Recall Norockies	F1 Norockies	Precision Rockies	Recall Rockies	F1 Rockies
No log	1.0	5	0.5577	0.7742	0.4364	0.3871	0.5	0.7742	1.0	0.8727	0.0	0.0	0.0
0.5622	2.0	10	0.4660	0.7742	0.4364	0.3871	0.5	0.7742	1.0	0.8727	0.0	0.0	0.0
0.5622	3.0	15	0.2639	0.9032	0.8342	0.9444	0.7857	0.8889	1.0	0.9412	1.0	0.5714	0.7273
0.1315	4.0	20	0.0970	0.9516	0.9248	0.9706	0.8929	0.9412	1.0	0.9697	1.0	0.7857	0.88
0.1315	5.0	25	0.0087	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0168	6.0	30	0.0026	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0168	7.0	35	0.2913	0.9355	0.8967	0.9615	0.8571	0.9231	1.0	0.96	1.0	0.7143	0.8333
0.0449	8.0	40	0.0011	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0449	9.0	45	0.0919	0.9677	0.9513	0.98	0.9286	0.96	1.0	0.9796	1.0	0.8571	0.9231
0.0027	10.0	50	0.1446	0.9677	0.9513	0.98	0.9286	0.96	1.0	0.9796	1.0	0.8571	0.9231
0.0027	11.0	55	0.0477	0.9677	0.9513	0.98	0.9286	0.96	1.0	0.9796	1.0	0.8571	0.9231
0.0003	12.0	60	0.0075	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0003	13.0	65	0.0056	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0003	14.0	70	0.0358	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0003	15.0	75	0.0848	0.9677	0.9513	0.98	0.9286	0.96	1.0	0.9796	1.0	0.8571	0.9231
0.0002	16.0	80	0.1125	0.9677	0.9513	0.98	0.9286	0.96	1.0	0.9796	1.0	0.8571	0.9231
0.0002	17.0	85	0.1242	0.9677	0.9513	0.98	0.9286	0.96	1.0	0.9796	1.0	0.8571	0.9231
0.0001	18.0	90	0.1268	0.9677	0.9513	0.98	0.9286	0.96	1.0	0.9796	1.0	0.8571	0.9231
0.0001	19.0	95	0.1254	0.9677	0.9513	0.98	0.9286	0.96	1.0	0.9796	1.0	0.8571	0.9231
0.0001	20.0	100	0.1197	0.9677	0.9513	0.98	0.9286	0.96	1.0	0.9796	1.0	0.8571	0.9231
0.0001	21.0	105	0.1098	0.9677	0.9513	0.98	0.9286	0.96	1.0	0.9796	1.0	0.8571	0.9231
0.0001	22.0	110	0.0988	0.9677	0.9513	0.98	0.9286	0.96	1.0	0.9796	1.0	0.8571	0.9231
0.0001	23.0	115	0.0844	0.9677	0.9513	0.98	0.9286	0.96	1.0	0.9796	1.0	0.8571	0.9231
0.0001	24.0	120	0.0737	0.9677	0.9513	0.98	0.9286	0.96	1.0	0.9796	1.0	0.8571	0.9231
0.0001	25.0	125	0.0661	0.9677	0.9513	0.98	0.9286	0.96	1.0	0.9796	1.0	0.8571	0.9231
0.0001	26.0	130	0.0612	0.9677	0.9513	0.98	0.9286	0.96	1.0	0.9796	1.0	0.8571	0.9231
0.0001	27.0	135	0.0576	0.9677	0.9513	0.98	0.9286	0.96	1.0	0.9796	1.0	0.8571	0.9231
0.0001	28.0	140	0.0557	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	29.0	145	0.0532	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	30.0	150	0.0505	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	31.0	155	0.0483	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	32.0	160	0.0473	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	33.0	165	0.0463	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	34.0	170	0.0454	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	35.0	175	0.0439	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	36.0	180	0.0432	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	37.0	185	0.0421	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	38.0	190	0.0416	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	39.0	195	0.0416	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	40.0	200	0.0409	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	41.0	205	0.0407	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	42.0	210	0.0407	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	43.0	215	0.0403	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	44.0	220	0.0402	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	45.0	225	0.0403	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	46.0	230	0.0402	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	47.0	235	0.0402	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	48.0	240	0.0402	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	49.0	245	0.0402	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630
0.0001	50.0	250	0.0402	0.9839	0.9763	0.9898	0.9643	0.9796	1.0	0.9897	1.0	0.9286	0.9630

Framework versions

Transformers 4.56.1
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0

dacunaq/vit-base-patch16-384-finetuned-humid-classes-15

vit-base-patch16-384-finetuned-humid-classes-15

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dacunaq/
vit-base-patch16-384-finetuned-humid-classes-15