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: 0.1847
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 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	2	0.8464	0.375	0.3651	0.4872	0.4909	0.6667	0.1818	0.2857	0.3077	0.8	0.4444
No log	2.0	4	0.5715	0.8125	0.7922	0.7833	0.8091	0.9	0.8182	0.8571	0.6667	0.8	0.7273
No log	3.0	6	0.3832	0.75	0.5897	0.8667	0.6	0.7333	1.0	0.8462	1.0	0.2	0.3333
No log	4.0	8	0.1847	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.5069	5.0	10	0.0564	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.5069	6.0	12	0.0196	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.5069	7.0	14	0.0073	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.5069	8.0	16	0.0026	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.5069	9.0	18	0.0011	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0148	10.0	20	0.0006	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0148	11.0	22	0.0004	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0148	12.0	24	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0148	13.0	26	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0148	14.0	28	0.0002	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	15.0	30	0.0002	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	16.0	32	0.0002	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	17.0	34	0.0002	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	18.0	36	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	19.0	38	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	20.0	40	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	21.0	42	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	22.0	44	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	23.0	46	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	24.0	48	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	25.0	50	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	26.0	52	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	27.0	54	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	28.0	56	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	29.0	58	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	30.0	60	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	31.0	62	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	32.0	64	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	33.0	66	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	34.0	68	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	35.0	70	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	36.0	72	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	37.0	74	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	38.0	76	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	39.0	78	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	40.0	80	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	41.0	82	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	42.0	84	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	43.0	86	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	44.0	88	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	45.0	90	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	46.0	92	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	47.0	94	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	48.0	96	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	49.0	98	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	50.0	100	0.0001	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 2.5.1+cu124
Datasets 4.0.0
Tokenizers 0.22.0

dacunaq/vit-base-patch16-384-finetuned-dry-rockies-1

vit-base-patch16-384-finetuned-dry-rockies-1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dacunaq/
vit-base-patch16-384-finetuned-dry-rockies-1