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: 0.1026
Accuracy: 0.9831
F1 Macro: 0.9908
Precision Macro: 0.99
Recall Macro: 0.992
Precision Dry: 1.0
Recall Dry: 1.0
F1 Dry: 1.0
Precision Firm: 1.0
Recall Firm: 0.96
F1 Firm: 0.9796
Precision Humid: 1.0
Recall Humid: 1.0
F1 Humid: 1.0
Precision Lump: 0.95
Recall Lump: 1.0
F1 Lump: 0.9744
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	1.4732	0.4068	0.1157	0.0828	0.192	0.0	0.0	0.0	0.4138	0.96	0.5783	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1.4545	2.0	10	1.0338	0.6441	0.2918	0.2549	0.3419	0.0	0.0	0.0	0.7188	0.92	0.8070	0.0	0.0	0.0	0.5556	0.7895	0.6522	0.0	0.0	0.0
1.4545	3.0	15	0.7303	0.7119	0.3876	0.4865	0.4135	1.0	0.2	0.3333	0.8519	0.92	0.8846	0.0	0.0	0.0	0.5806	0.9474	0.72	0.0	0.0	0.0
0.683	4.0	20	0.5276	0.7458	0.5600	0.6883	0.5499	1.0	0.8	0.8889	0.7273	0.96	0.8276	1.0	0.2	0.3333	0.7143	0.7895	0.75	0.0	0.0	0.0
0.683	5.0	25	0.2760	0.9153	0.9126	0.9546	0.8909	1.0	1.0	1.0	0.9231	0.96	0.9412	1.0	0.6	0.75	0.85	0.8947	0.8718	1.0	1.0	1.0
0.2775	6.0	30	0.2623	0.9153	0.9022	0.8921	0.9204	1.0	1.0	1.0	1.0	0.96	0.9796	0.5714	0.8	0.6667	0.8889	0.8421	0.8649	1.0	1.0	1.0
0.2775	7.0	35	0.2016	0.9661	0.9684	0.9567	0.984	1.0	1.0	1.0	1.0	0.92	0.9583	1.0	1.0	1.0	0.95	1.0	0.9744	0.8333	1.0	0.9091
0.1077	8.0	40	0.1490	0.9492	0.9237	0.9410	0.9120	1.0	1.0	1.0	1.0	0.96	0.9796	0.8	0.8	0.8	0.9048	1.0	0.95	1.0	0.8	0.8889
0.1077	9.0	45	0.1615	0.9492	0.9313	0.9727	0.9120	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	0.6	0.75	0.8636	1.0	0.9268	1.0	1.0	1.0
0.0667	10.0	50	0.1802	0.9661	0.9672	0.9561	0.9815	1.0	1.0	1.0	1.0	0.96	0.9796	0.8333	1.0	0.9091	0.9474	0.9474	0.9474	1.0	1.0	1.0
0.0667	11.0	55	0.2828	0.9153	0.8650	0.9572	0.8320	1.0	1.0	1.0	0.96	0.96	0.96	1.0	0.4	0.5714	0.8261	1.0	0.9048	1.0	0.8	0.8889
0.0455	12.0	60	0.1683	0.9492	0.9464	0.9317	0.9709	1.0	1.0	1.0	1.0	0.96	0.9796	0.7143	1.0	0.8333	0.9444	0.8947	0.9189	1.0	1.0	1.0
0.0455	13.0	65	0.6514	0.8305	0.6833	0.9310	0.664	1.0	1.0	1.0	1.0	0.92	0.9583	1.0	0.2	0.3333	0.6552	1.0	0.7917	1.0	0.2	0.3333
0.1064	14.0	70	0.1321	0.9322	0.9364	0.9231	0.9604	1.0	1.0	1.0	0.96	0.96	0.96	0.7143	1.0	0.8333	0.9412	0.8421	0.8889	1.0	1.0	1.0
0.1064	15.0	75	0.1556	0.9492	0.9313	0.9727	0.9120	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	0.6	0.75	0.8636	1.0	0.9268	1.0	1.0	1.0
0.0278	16.0	80	0.1813	0.9492	0.9313	0.9727	0.9120	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	0.6	0.75	0.8636	1.0	0.9268	1.0	1.0	1.0
0.0278	17.0	85	0.1202	0.9661	0.9672	0.9561	0.9815	1.0	1.0	1.0	1.0	0.96	0.9796	0.8333	1.0	0.9091	0.9474	0.9474	0.9474	1.0	1.0	1.0
0.0089	18.0	90	0.1226	0.9492	0.9313	0.9727	0.9120	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	0.6	0.75	0.8636	1.0	0.9268	1.0	1.0	1.0
0.0089	19.0	95	0.1928	0.9492	0.9313	0.9727	0.9120	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	0.6	0.75	0.8636	1.0	0.9268	1.0	1.0	1.0
0.0029	20.0	100	0.1026	0.9831	0.9908	0.99	0.992	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	1.0	1.0
0.0029	21.0	105	0.1067	0.9492	0.9433	0.9222	0.9709	1.0	1.0	1.0	1.0	0.96	0.9796	0.8333	1.0	0.9091	0.9444	0.8947	0.9189	0.8333	1.0	0.9091
0.0025	22.0	110	0.1363	0.9831	0.9908	0.99	0.992	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	1.0	1.0
0.0025	23.0	115	0.1643	0.9661	0.9637	0.9810	0.952	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	0.8	0.8889	0.9048	1.0	0.95	1.0	1.0	1.0
0.0016	24.0	120	0.1479	0.9492	0.9313	0.9727	0.9120	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	0.6	0.75	0.8636	1.0	0.9268	1.0	1.0	1.0
0.0016	25.0	125	0.0944	0.9492	0.9555	0.9741	0.9415	1.0	1.0	1.0	0.9231	0.96	0.9412	1.0	1.0	1.0	0.9474	0.9474	0.9474	1.0	0.8	0.8889
0.0016	26.0	130	0.1062	0.9831	0.9908	0.99	0.992	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	1.0	1.0
0.0016	27.0	135	0.2077	0.9661	0.9672	0.9561	0.9815	1.0	1.0	1.0	1.0	0.96	0.9796	0.8333	1.0	0.9091	0.9474	0.9474	0.9474	1.0	1.0	1.0
0.0034	28.0	140	0.1276	0.9831	0.9908	0.99	0.992	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	1.0	1.0
0.0034	29.0	145	0.2054	0.9492	0.9555	0.9741	0.9415	1.0	1.0	1.0	0.9231	0.96	0.9412	1.0	1.0	1.0	0.9474	0.9474	0.9474	1.0	0.8	0.8889
0.0029	30.0	150	0.2007	0.9661	0.9646	0.982	0.952	1.0	1.0	1.0	0.96	0.96	0.96	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	0.8	0.8889
0.0029	31.0	155	0.1982	0.9661	0.9646	0.982	0.952	1.0	1.0	1.0	0.96	0.96	0.96	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	0.8	0.8889
0.0013	32.0	160	0.1960	0.9661	0.9646	0.982	0.952	1.0	1.0	1.0	0.96	0.96	0.96	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	0.8	0.8889
0.0013	33.0	165	0.1889	0.9661	0.9646	0.982	0.952	1.0	1.0	1.0	0.96	0.96	0.96	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	0.8	0.8889
0.0007	34.0	170	0.1778	0.9661	0.9646	0.982	0.952	1.0	1.0	1.0	0.96	0.96	0.96	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	0.8	0.8889
0.0007	35.0	175	0.1699	0.9492	0.9555	0.9741	0.9415	1.0	1.0	1.0	0.9231	0.96	0.9412	1.0	1.0	1.0	0.9474	0.9474	0.9474	1.0	0.8	0.8889
0.0006	36.0	180	0.1594	0.9492	0.9555	0.9741	0.9415	1.0	1.0	1.0	0.9231	0.96	0.9412	1.0	1.0	1.0	0.9474	0.9474	0.9474	1.0	0.8	0.8889
0.0006	37.0	185	0.1479	0.9661	0.9646	0.982	0.952	1.0	1.0	1.0	0.96	0.96	0.96	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	0.8	0.8889
0.0006	38.0	190	0.1393	0.9661	0.9646	0.982	0.952	1.0	1.0	1.0	0.96	0.96	0.96	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	0.8	0.8889
0.0006	39.0	195	0.1343	0.9661	0.9646	0.982	0.952	1.0	1.0	1.0	0.96	0.96	0.96	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	0.8	0.8889
0.0005	40.0	200	0.1322	0.9661	0.9646	0.982	0.952	1.0	1.0	1.0	0.96	0.96	0.96	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	0.8	0.8889
0.0005	41.0	205	0.1317	0.9831	0.9908	0.99	0.992	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	1.0	1.0
0.0005	42.0	210	0.1316	0.9831	0.9908	0.99	0.992	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	1.0	1.0
0.0005	43.0	215	0.1318	0.9831	0.9908	0.99	0.992	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	1.0	1.0
0.0005	44.0	220	0.1318	0.9831	0.9908	0.99	0.992	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	1.0	1.0
0.0005	45.0	225	0.1319	0.9831	0.9908	0.99	0.992	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	1.0	1.0
0.0005	46.0	230	0.1320	0.9831	0.9908	0.99	0.992	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	1.0	1.0
0.0005	47.0	235	0.1321	0.9831	0.9908	0.99	0.992	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	1.0	1.0
0.0005	48.0	240	0.1321	0.9831	0.9908	0.99	0.992	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	1.0	1.0
0.0005	49.0	245	0.1321	0.9831	0.9908	0.99	0.992	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.95	1.0	0.9744	1.0	1.0	1.0
0.0005	50.0	250	0.1321	0.9831	0.9908	0.99	0.992	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.95	1.0	0.9744	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-6

vit-base-patch16-384-finetuned-humid-classes-6

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-6