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: 0.0157
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_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 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	1.5040	0.3387	0.2863	0.5885	0.3848	1.0	0.2	0.3333	0.4062	0.9286	0.5652	0.1364	0.6	0.2222	1.0	0.0526	0.1	0.4	0.1429	0.2105
1.4809	2.0	10	1.1305	0.6613	0.4908	0.4555	0.5600	0.8889	0.8	0.8421	0.875	1.0	0.9333	0.0	0.0	0.0	0.5135	1.0	0.6786	0.0	0.0	0.0
1.4809	3.0	15	0.7686	0.7903	0.6533	0.6844	0.6857	0.8333	1.0	0.9091	0.9333	1.0	0.9655	0.0	0.0	0.0	0.6552	1.0	0.7917	1.0	0.4286	0.6
0.6702	4.0	20	0.3308	0.9032	0.7653	0.752	0.7857	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.76	1.0	0.8636	1.0	0.9286	0.9630
0.6702	5.0	25	0.1514	0.9677	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
0.204	6.0	30	0.1756	0.9516	0.9280	0.9727	0.9057	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.6	0.75	0.8636	1.0	0.9268	1.0	0.9286	0.9630
0.204	7.0	35	0.1464	0.9355	0.9235	0.9247	0.9284	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	1.0	0.8421	0.9143	0.8235	1.0	0.9032
0.0809	8.0	40	0.0892	0.9839	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
0.0809	9.0	45	0.1692	0.9355	0.9156	0.9652	0.8914	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.6	0.75	0.8261	1.0	0.9048	1.0	0.8571	0.9231
0.068	10.0	50	0.1271	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.068	11.0	55	0.2046	0.9194	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
0.0383	12.0	60	0.0567	0.9677	0.9621	0.9524	0.9752	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091	1.0	0.9474	0.9730	0.9286	0.9286	0.9286
0.0383	13.0	65	0.2568	0.9032	0.8634	0.9432	0.8409	1.0	1.0	1.0	0.9333	1.0	0.9655	1.0	0.4	0.5714	0.7826	0.9474	0.8571	1.0	0.8571	0.9231
0.0365	14.0	70	0.1315	0.9516	0.9353	0.925	0.9647	1.0	1.0	1.0	1.0	1.0	1.0	0.625	1.0	0.7692	1.0	0.8947	0.9444	1.0	0.9286	0.9630
0.0365	15.0	75	0.1447	0.9677	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
0.027	16.0	80	0.1043	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.027	17.0	85	0.0157	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
0.026	18.0	90	0.0247	0.9839	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
0.026	19.0	95	0.1236	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.008	20.0	100	0.0419	0.9839	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
0.008	21.0	105	0.0141	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
0.0069	22.0	110	0.0880	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.0069	23.0	115	0.0351	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.002	24.0	120	0.0368	0.9839	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
0.002	25.0	125	0.0596	0.9839	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
0.0034	26.0	130	0.0777	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.0034	27.0	135	0.0405	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.0017	28.0	140	0.0516	0.9839	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
0.0017	29.0	145	0.0420	0.9839	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
0.0017	30.0	150	0.0147	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	31.0	155	0.0250	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.0007	32.0	160	0.0346	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.0007	33.0	165	0.0379	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.0007	34.0	170	0.0330	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.0007	35.0	175	0.0284	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.0006	36.0	180	0.0249	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.0006	37.0	185	0.0221	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.0005	38.0	190	0.0198	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.0005	39.0	195	0.0178	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.0005	40.0	200	0.0160	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.0005	41.0	205	0.0147	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.0005	42.0	210	0.0139	0.9839	0.9877	0.9867	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.9474	0.9730	0.9333	1.0	0.9655
0.0005	43.0	215	0.0134	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	44.0	220	0.0129	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	45.0	225	0.0125	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	46.0	230	0.0122	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	47.0	235	0.0120	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	48.0	240	0.0118	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	49.0	245	0.0118	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	50.0	250	0.0117	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 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0

dacunaq/vit-base-patch16-384-finetuned-humid-classes-13

vit-base-patch16-384-finetuned-humid-classes-13

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-13