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: 0.0494
Accuracy: 0.9846
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	6	1.4939	0.3231	0.1739	0.2071	0.2136	0.25	0.0909	0.1333	0.5	0.24	0.3243	0.0	0.0	0.0	0.2857	0.7368	0.4118	0.0	0.0	0.0
1.5504	2.0	12	1.0417	0.6308	0.395	0.4374	0.4092	1.0	0.4545	0.625	0.6154	0.96	0.75	0.0	0.0	0.0	0.5714	0.6316	0.6	0.0	0.0	0.0
1.5504	3.0	18	0.5960	0.8154	0.5956	0.7003	0.616	0.8462	1.0	0.9167	1.0	0.88	0.9362	0.0	0.0	0.0	0.6552	1.0	0.7917	1.0	0.2	0.3333
0.8022	4.0	24	0.3348	0.9077	0.8592	0.9232	0.8535	1.0	1.0	1.0	1.0	0.92	0.9583	1.0	0.4	0.5714	0.7826	0.9474	0.8571	0.8333	1.0	0.9091
0.3123	5.0	30	0.4147	0.8615	0.8373	0.8863	0.8463	1.0	1.0	1.0	0.8065	1.0	0.8929	1.0	0.6	0.75	1.0	0.6316	0.7742	0.625	1.0	0.7692
0.3123	6.0	36	0.3146	0.8769	0.7736	0.9407	0.744	1.0	1.0	1.0	1.0	0.92	0.9583	1.0	0.2	0.3333	0.7037	1.0	0.8261	1.0	0.6	0.75
0.2923	7.0	42	0.3068	0.9077	0.8759	0.8702	0.9074	1.0	1.0	1.0	0.9259	1.0	0.9615	0.8	0.8	0.8	1.0	0.7368	0.8485	0.625	1.0	0.7692
0.2923	8.0	48	0.3899	0.8769	0.8167	0.9407	0.776	1.0	1.0	1.0	1.0	0.88	0.9362	1.0	0.4	0.5714	0.7037	1.0	0.8261	1.0	0.6	0.75
0.1625	9.0	54	0.1591	0.9538	0.9426	0.9256	0.9684	1.0	1.0	1.0	0.9615	1.0	0.9804	0.8333	1.0	0.9091	1.0	0.8421	0.9143	0.8333	1.0	0.9091
0.1154	10.0	60	0.1564	0.9692	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
0.1154	11.0	66	0.2429	0.9077	0.8740	0.9183	0.8560	1.0	1.0	1.0	1.0	0.88	0.9362	0.8	0.8	0.8	0.7917	1.0	0.8837	1.0	0.6	0.75
0.0623	12.0	72	0.0956	0.9692	0.9672	0.9561	0.9815	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.9474	0.9474	0.9474	0.8333	1.0	0.9091
0.0623	13.0	78	0.1097	0.9538	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
0.0139	14.0	84	0.1859	0.9385	0.8892	0.9145	0.9015	1.0	1.0	1.0	1.0	0.96	0.9796	0.625	1.0	0.7692	0.9474	0.9474	0.9474	1.0	0.6	0.75
0.0258	15.0	90	0.0990	0.9692	0.9672	0.9561	0.9815	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.9474	0.9474	0.9474	0.8333	1.0	0.9091
0.0258	16.0	96	0.1266	0.9538	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
0.0059	17.0	102	0.2095	0.9385	0.9226	0.9652	0.9040	1.0	1.0	1.0	1.0	0.92	0.9583	1.0	0.6	0.75	0.8261	1.0	0.9048	1.0	1.0	1.0
0.0059	18.0	108	0.0494	0.9846	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
0.0042	19.0	114	0.1558	0.9692	0.9672	0.9561	0.9815	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.9474	0.9474	0.9474	0.8333	1.0	0.9091
0.0069	20.0	120	0.0556	0.9692	0.9672	0.9561	0.9815	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.9474	0.9474	0.9474	0.8333	1.0	0.9091
0.0069	21.0	126	0.0792	0.9692	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
0.0026	22.0	132	0.2397	0.9385	0.9636	0.9652	0.968	1.0	1.0	1.0	1.0	0.84	0.9130	1.0	1.0	1.0	0.8261	1.0	0.9048	1.0	1.0	1.0
0.0026	23.0	138	0.1618	0.9385	0.9068	0.9379	0.8989	1.0	1.0	1.0	0.9615	1.0	0.9804	1.0	0.6	0.75	0.8947	0.8947	0.8947	0.8333	1.0	0.9091
0.0413	24.0	144	0.1227	0.9692	0.9672	0.9561	0.9815	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.9474	0.9474	0.9474	0.8333	1.0	0.9091
0.0075	25.0	150	0.2146	0.9385	0.9460	0.9652	0.9360	1.0	1.0	1.0	1.0	0.88	0.9362	1.0	0.8	0.8889	0.8261	1.0	0.9048	1.0	1.0	1.0
0.0075	26.0	156	0.1914	0.9538	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
0.0022	27.0	162	0.1673	0.9538	0.9272	0.9161	0.9415	1.0	1.0	1.0	1.0	0.96	0.9796	0.8333	1.0	0.9091	0.9474	0.9474	0.9474	0.8	0.8	0.8
0.0022	28.0	168	0.1216	0.9692	0.9672	0.9561	0.9815	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.9474	0.9474	0.9474	0.8333	1.0	0.9091
0.0025	29.0	174	0.3480	0.9385	0.8904	0.9637	0.8695	1.0	1.0	1.0	0.9615	1.0	0.9804	1.0	0.4	0.5714	0.8571	0.9474	0.9	1.0	1.0	1.0
0.0038	30.0	180	0.1135	0.9538	0.9576	0.9476	0.9709	1.0	1.0	1.0	0.96	0.96	0.96	1.0	1.0	1.0	0.9444	0.8947	0.9189	0.8333	1.0	0.9091
0.0038	31.0	186	0.1418	0.9692	0.9672	0.9561	0.9815	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.9474	0.9474	0.9474	0.8333	1.0	0.9091
0.0016	32.0	192	0.0926	0.9692	0.9672	0.9561	0.9815	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.9474	0.9474	0.9474	0.8333	1.0	0.9091
0.0016	33.0	198	0.0796	0.9692	0.9672	0.9561	0.9815	1.0	1.0	1.0	1.0	0.96	0.9796	1.0	1.0	1.0	0.9474	0.9474	0.9474	0.8333	1.0	0.9091
0.0007	34.0	204	0.0847	0.9692	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
0.0006	35.0	210	0.0917	0.9692	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
0.0006	36.0	216	0.0956	0.9692	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
0.0005	37.0	222	0.0972	0.9692	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
0.0005	38.0	228	0.0982	0.9692	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
0.0005	39.0	234	0.0989	0.9692	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
0.0005	40.0	240	0.0995	0.9692	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
0.0005	41.0	246	0.1003	0.9692	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
0.0005	42.0	252	0.1008	0.9692	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
0.0005	43.0	258	0.1011	0.9692	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
0.0005	44.0	264	0.1013	0.9692	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
0.0004	45.0	270	0.1015	0.9846	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
0.0004	46.0	276	0.1016	0.9846	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
0.0004	47.0	282	0.1017	0.9846	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
0.0004	48.0	288	0.1018	0.9846	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
0.0004	49.0	294	0.1018	0.9846	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
0.0004	50.0	300	0.1018	0.9846	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-8

vit-base-patch16-384-finetuned-humid-classes-8

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-8