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: 0.0182
Accuracy: 1.0
F1 Macro: 1.0
Precision Macro: 1.0
Recall Macro: 1.0
Precision Dry: 1.0
Recall Dry: 1.0
F1 Dry: 1.0
Precision Firm: 1.0
Recall Firm: 1.0
F1 Firm: 1.0
Precision Humid: 1.0
Recall Humid: 1.0
F1 Humid: 1.0
Precision Lump: 1.0
Recall Lump: 1.0
F1 Lump: 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 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 Dry	Recall Dry	F1 Dry	Precision Firm	Recall Firm	F1 Firm	Precision Humid	Recall Humid	F1 Humid	Precision Lump	Recall Lump	F1 Lump	Precision Rockies	Recall Rockies	F1 Rockies
No log	1.0	5	1.4684	0.4127	0.3382	0.5128	0.4131	0.5385	0.6364	0.5833	0.3590	1.0	0.5283	0.1667	0.2	0.1818	1.0	0.1579	0.2727	0.5	0.0714	0.125
1.3894	2.0	10	1.0494	0.6032	0.4495	0.4741	0.4948	1.0	0.5455	0.7059	0.9286	0.9286	0.9286	0.0	0.0	0.0	0.4419	1.0	0.6129	0.0	0.0	0.0
1.3894	3.0	15	0.7123	0.7143	0.5480	0.6701	0.6143	0.9167	1.0	0.9565	0.875	1.0	0.9333	0.0	0.0	0.0	0.5588	1.0	0.7170	1.0	0.0714	0.1333
0.6378	4.0	20	0.3507	0.9206	0.8756	0.9583	0.8514	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.4	0.5714	0.7917	1.0	0.8837	1.0	0.8571	0.9231
0.6378	5.0	25	0.2000	0.9365	0.8780	0.9061	0.8657	1.0	1.0	1.0	1.0	1.0	1.0	0.6667	0.4	0.5	0.8636	1.0	0.9268	1.0	0.9286	0.9630
0.22	6.0	30	0.1444	0.9683	0.9556	0.9429	0.9789	1.0	1.0	1.0	1.0	1.0	1.0	0.7143	1.0	0.8333	1.0	0.8947	0.9444	1.0	1.0	1.0
0.22	7.0	35	0.1251	0.9524	0.9464	0.9617	0.9389	1.0	1.0	1.0	0.9333	1.0	0.9655	1.0	0.8	0.8889	1.0	0.8947	0.9444	0.875	1.0	0.9333
0.0828	8.0	40	0.0619	0.9841	0.9744	0.9667	0.9857	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091	1.0	1.0	1.0	1.0	0.9286	0.9630
0.0828	9.0	45	0.0550	0.9683	0.9400	0.9810	0.9200	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.6	0.75	0.9048	1.0	0.95	1.0	1.0	1.0
0.0345	10.0	50	0.0433	0.9841	0.9877	0.9867	0.9895	1.0	1.0	1.0	0.9333	1.0	0.9655	1.0	1.0	1.0	1.0	0.9474	0.9730	1.0	1.0	1.0
0.0345	11.0	55	0.1126	0.9524	0.9249	0.9233	0.9314	1.0	1.0	1.0	1.0	1.0	1.0	0.6667	0.8	0.7273	0.95	1.0	0.9744	1.0	0.8571	0.9231
0.007	12.0	60	0.0398	0.9841	0.9764	0.9667	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091	1.0	0.9474	0.9730	1.0	1.0	1.0
0.007	13.0	65	0.0538	0.9841	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0029	14.0	70	0.0246	0.9841	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0029	15.0	75	0.0182	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0017	16.0	80	0.0075	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0017	17.0	85	0.0090	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0012	18.0	90	0.0066	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0012	19.0	95	0.0072	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.001	20.0	100	0.0149	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.001	21.0	105	0.0193	0.9841	0.9764	0.9667	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091	1.0	0.9474	0.9730	1.0	1.0	1.0
0.0008	22.0	110	0.0161	0.9841	0.9764	0.9667	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091	1.0	0.9474	0.9730	1.0	1.0	1.0
0.0008	23.0	115	0.0108	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0007	24.0	120	0.0079	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0007	25.0	125	0.0070	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0006	26.0	130	0.0066	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0006	27.0	135	0.0064	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0006	28.0	140	0.0063	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0006	29.0	145	0.0065	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0005	30.0	150	0.0066	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0005	31.0	155	0.0066	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0005	32.0	160	0.0065	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0005	33.0	165	0.0064	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0005	34.0	170	0.0064	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0005	35.0	175	0.0063	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0005	36.0	180	0.0063	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0005	37.0	185	0.0063	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	38.0	190	0.0063	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	39.0	195	0.0064	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	40.0	200	0.0063	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	41.0	205	0.0063	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	42.0	210	0.0063	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	43.0	215	0.0062	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	44.0	220	0.0061	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	45.0	225	0.0061	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	46.0	230	0.0061	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	47.0	235	0.0061	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	48.0	240	0.0060	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	49.0	245	0.0060	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	50.0	250	0.0060	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0

Framework versions

Transformers 4.56.1
Pytorch 2.5.1+cu124
Datasets 4.0.0
Tokenizers 0.22.0

dacunaq/vit-base-patch16-384-finetuned-humid-classes-10

vit-base-patch16-384-finetuned-humid-classes-10

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-10