This model is a fine-tuned version of google/vit-base-patch32-384 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0356
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 Moist: 1.0
Recall Moist: 1.0
F1 Moist: 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 Moist	Recall Moist	F1 Moist	Precision Rockies	Recall Rockies	F1 Rockies
No log	1.0	3	1.6038	0.3846	0.1820	0.1458	0.2706	0.0	0.0	0.0	0.6667	0.9091	0.7692	0.2083	0.7143	0.3226	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
No log	2.0	6	1.3463	0.4872	0.3096	0.4074	0.3790	1.0	0.1667	0.2857	0.6111	1.0	0.7586	0.3333	0.8571	0.48	0.0	0.0	0.0	0.5	0.25	0.3333	0.0	0.0	0.0
No log	3.0	9	1.0343	0.8205	0.7987	0.8868	0.7667	0.8333	0.8333	0.8333	0.6875	1.0	0.8148	1.0	1.0	1.0	0.8	0.6667	0.7273	1.0	0.5	0.6667	1.0	0.6	0.75
1.4549	4.0	12	0.7474	0.8974	0.8782	0.8764	0.8821	0.8333	0.8333	0.8333	1.0	0.9091	0.9524	0.875	1.0	0.9333	1.0	1.0	1.0	0.75	0.75	0.75	0.8	0.8	0.8
1.4549	5.0	15	0.5873	0.8718	0.8266	0.8839	0.8306	1.0	0.8333	0.9091	1.0	1.0	1.0	0.875	1.0	0.9333	1.0	1.0	1.0	0.4286	0.75	0.5455	1.0	0.4	0.5714
1.4549	6.0	18	0.3575	0.9231	0.9093	0.9190	0.9098	0.8571	1.0	0.9231	1.0	0.9091	0.9524	1.0	1.0	1.0	0.8571	1.0	0.9231	1.0	0.75	0.8571	0.8	0.8	0.8
0.5145	7.0	21	0.2852	0.8974	0.8771	0.9083	0.8694	1.0	1.0	1.0	1.0	1.0	1.0	0.7	1.0	0.8235	1.0	0.6667	0.8	0.75	0.75	0.75	1.0	0.8	0.8889
0.5145	8.0	24	0.1317	0.9744	0.9769	0.9722	0.9848	1.0	1.0	1.0	1.0	0.9091	0.9524	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091
0.5145	9.0	27	0.1270	0.9744	0.9630	0.9667	0.9667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	1.0	0.8889	1.0	0.8	0.8889
0.098	10.0	30	0.0767	0.9744	0.9651	0.9792	0.9583	1.0	1.0	1.0	1.0	1.0	1.0	0.875	1.0	0.9333	1.0	1.0	1.0	1.0	0.75	0.8571	1.0	1.0	1.0
0.098	11.0	33	0.0577	0.9744	0.9769	0.9722	0.9848	1.0	1.0	1.0	1.0	0.9091	0.9524	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091
0.098	12.0	36	0.0356	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.098	13.0	39	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	1.0	1.0	1.0
0.0144	14.0	42	0.0139	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0144	15.0	45	0.0318	0.9744	0.9651	0.9792	0.9583	1.0	1.0	1.0	1.0	1.0	1.0	0.875	1.0	0.9333	1.0	1.0	1.0	1.0	0.75	0.8571	1.0	1.0	1.0
0.0144	16.0	48	0.0643	0.9744	0.9651	0.9792	0.9583	1.0	1.0	1.0	1.0	1.0	1.0	0.875	1.0	0.9333	1.0	1.0	1.0	1.0	0.75	0.8571	1.0	1.0	1.0
0.0046	17.0	51	0.0619	0.9744	0.9737	0.9792	0.9722	1.0	1.0	1.0	1.0	1.0	1.0	0.875	1.0	0.9333	1.0	0.8333	0.9091	1.0	1.0	1.0	1.0	1.0	1.0
0.0046	18.0	54	0.0421	0.9744	0.9737	0.9792	0.9722	1.0	1.0	1.0	1.0	1.0	1.0	0.875	1.0	0.9333	1.0	0.8333	0.9091	1.0	1.0	1.0	1.0	1.0	1.0
0.0046	19.0	57	0.0272	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.002	20.0	60	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	1.0	1.0	1.0
0.002	21.0	63	0.0137	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.002	22.0	66	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	1.0	1.0	1.0
0.002	23.0	69	0.0091	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0014	24.0	72	0.0082	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0014	25.0	75	0.0076	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0014	26.0	78	0.0073	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0011	27.0	81	0.0071	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0011	28.0	84	0.0071	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0011	29.0	87	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	1.0	1.0	1.0
0.001	30.0	90	0.0071	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	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	31.0	93	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	1.0	1.0	1.0
0.001	32.0	96	0.0071	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	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	33.0	99	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	1.0	1.0	1.0
0.0009	34.0	102	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	1.0	1.0	1.0
0.0009	35.0	105	0.0071	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0009	36.0	108	0.0071	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0008	37.0	111	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	1.0	1.0	1.0
0.0008	38.0	114	0.0069	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0008	39.0	117	0.0068	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0008	40.0	120	0.0068	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0008	41.0	123	0.0067	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0008	42.0	126	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	1.0	1.0	1.0
0.0008	43.0	129	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	1.0	1.0	1.0
0.0008	44.0	132	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	1.0	1.0	1.0
0.0008	45.0	135	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	1.0	1.0	1.0
0.0008	46.0	138	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	1.0	1.0	1.0
0.0007	47.0	141	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	1.0	1.0	1.0
0.0007	48.0	144	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	1.0	1.0	1.0
0.0007	49.0	147	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	1.0	1.0	1.0
0.0007	50.0	150	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	1.0	1.0	1.0

Framework versions

Transformers 4.57.1
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0

dacunaq/vit-base-patch32-384-finetuned-humid-classes-33

vit-base-patch32-384-finetuned-humid-classes-33

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dacunaq/
vit-base-patch32-384-finetuned-humid-classes-33