This model is a fine-tuned version of facebook/convnextv2-base-22k-384 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0484
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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
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	3	0.6896	0.4375	0.4353	0.4833	0.4818	0.6667	0.3636	0.4706	0.3	0.6	0.4
No log	2.0	6	0.3815	0.875	0.8333	0.9231	0.8	0.8462	1.0	0.9167	1.0	0.6	0.75
No log	3.0	9	0.1724	0.9375	0.9227	0.9583	0.9	0.9167	1.0	0.9565	1.0	0.8	0.8889
0.5491	4.0	12	0.0484	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.5491	5.0	15	0.0112	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.5491	6.0	18	0.0027	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0442	7.0	21	0.0008	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0442	8.0	24	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0442	9.0	27	0.0002	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0007	10.0	30	0.0009	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0007	11.0	33	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0007	12.0	36	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0007	13.0	39	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	14.0	42	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	15.0	45	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	16.0	48	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	17.0	51	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	18.0	54	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	19.0	57	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	20.0	60	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	21.0	63	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	22.0	66	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	23.0	69	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	24.0	72	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	25.0	75	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	26.0	78	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	27.0	81	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	28.0	84	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	29.0	87	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	30.0	90	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	31.0	93	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	32.0	96	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	33.0	99	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	34.0	102	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	35.0	105	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	36.0	108	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	37.0	111	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	38.0	114	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	39.0	117	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	40.0	120	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	41.0	123	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	42.0	126	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	43.0	129	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	44.0	132	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	45.0	135	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	46.0	138	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	47.0	141	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	48.0	144	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	49.0	147	0.0000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0	50.0	150	0.0000	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/convnextv2-base-22k-384-finetuned-humid-classes-19

convnextv2-base-22k-384-finetuned-humid-classes-19

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dacunaq/
convnextv2-base-22k-384-finetuned-humid-classes-19