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: 0.3395
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 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 Rockies	Recall Rockies	F1 Rockies
No log	1.0	1	0.7950	0.5	0.3333	0.25	0.5	0.0	0.0	0.0	0.5	1.0	0.6667
No log	2.0	2	0.6379	0.5	0.3333	0.25	0.5	0.0	0.0	0.0	0.5	1.0	0.6667
No log	3.0	3	0.5717	0.6667	0.625	0.8	0.6667	0.6	1.0	0.75	1.0	0.3333	0.5
No log	4.0	4	0.4795	0.8333	0.8286	0.875	0.8333	0.75	1.0	0.8571	1.0	0.6667	0.8
No log	5.0	5	0.3395	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
No log	6.0	6	0.2178	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
No log	7.0	7	0.1301	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
No log	8.0	8	0.1475	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
No log	9.0	9	0.0490	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.4165	10.0	10	0.0210	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.4165	11.0	11	0.0145	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.4165	12.0	12	0.0089	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.4165	13.0	13	0.0070	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.4165	14.0	14	0.0065	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.4165	15.0	15	0.0060	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.4165	16.0	16	0.0042	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.4165	17.0	17	0.0025	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.4165	18.0	18	0.0017	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.4165	19.0	19	0.0013	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0056	20.0	20	0.0010	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0056	21.0	21	0.0008	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0056	22.0	22	0.0007	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0056	23.0	23	0.0007	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0056	24.0	24	0.0006	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0056	25.0	25	0.0005	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0056	26.0	26	0.0005	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0056	27.0	27	0.0005	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0056	28.0	28	0.0004	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0056	29.0	29	0.0004	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	30.0	30	0.0004	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	31.0	31	0.0004	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	32.0	32	0.0004	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	33.0	33	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	34.0	34	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	35.0	35	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	36.0	36	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	37.0	37	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	38.0	38	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	39.0	39	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	40.0	40	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	41.0	41	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	42.0	42	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	43.0	43	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	44.0	44	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	45.0	45	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	46.0	46	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	47.0	47	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	48.0	48	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	49.0	49	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	50.0	50	0.0003	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-patch16-384-finetuned-humid-classes-20

vit-base-patch16-384-finetuned-humid-classes-20

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-20