convnextv2-base-22k-384-finetuned-humid-classes-18

dacunaq/convnextv2-base-22k-384-finetuned-humid-classes-18

convnextv2-base-22k-384-finetuned-humid-classes-18 is a image classification model from dacunaq. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: convnextv2
Access: Public
Created: Nov 5, 2025
Updated: Nov 5, 2025
SHA: b13606db

Base models

facebook/convnextv2-base-22k-384

Datasets

imagefolder

Model card

From the Hugging Face model README

convnextv2-base-22k-384-finetuned-humid-classes-18

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: 0.0091
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 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 Firm	Recall Firm	F1 Firm	Precision Humid	Recall Humid	F1 Humid	Precision Lump	Recall Lump	F1 Lump	Precision Rockies	Recall Rockies	F1 Rockies
1.6674	1.0	10	1.4023	0.5161	0.3624	0.4162	0.4284	0.5	0.1	0.1667	0.6364	1.0	0.7778	0.5	0.2	0.2857	0.4444	0.8421	0.5818	0.0	0.0	0.0
1.0373	2.0	20	0.8036	0.7742	0.6447	0.6785	0.6752	1.0	1.0	1.0	0.7	1.0	0.8235	0.0	0.0	0.0	0.6923	0.9474	0.8	1.0	0.4286	0.6
0.4943	3.0	30	0.6225	0.7903	0.6549	0.6881	0.6857	1.0	1.0	1.0	0.7368	1.0	0.8485	0.0	0.0	0.0	0.7037	1.0	0.8261	1.0	0.4286	0.6
0.2539	4.0	40	0.2507	0.8871	0.8366	0.8695	0.8266	1.0	1.0	1.0	0.8235	1.0	0.9032	0.6667	0.4	0.5	0.8571	0.9474	0.9	1.0	0.7857	0.88
0.1104	5.0	50	0.5704	0.8710	0.7412	0.7407	0.7571	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.7037	1.0	0.8261	1.0	0.7857	0.88
0.2396	6.0	60	0.3681	0.8548	0.7294	0.7167	0.7504	1.0	1.0	1.0	0.875	1.0	0.9333	0.0	0.0	0.0	0.7083	0.8947	0.7907	1.0	0.8571	0.9231
0.2072	7.0	70	0.2441	0.9194	0.9216	0.9248	0.9323	1.0	1.0	1.0	0.9333	1.0	0.9655	0.8333	1.0	0.9091	0.8571	0.9474	0.9	1.0	0.7143	0.8333
0.0998	8.0	80	0.1762	0.9194	0.8656	0.8986	0.8514	1.0	1.0	1.0	1.0	1.0	1.0	0.6667	0.4	0.5	0.8261	1.0	0.9048	1.0	0.8571	0.9231
0.06	9.0	90	0.4499	0.8387	0.8129	0.8979	0.7950	1.0	1.0	1.0	0.7	1.0	0.8235	1.0	0.4	0.5714	0.7895	0.7895	0.7895	1.0	0.7857	0.88
0.0991	10.0	100	0.2209	0.9516	0.8997	0.9727	0.8800	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.4	0.5714	0.8636	1.0	0.9268	1.0	1.0	1.0
0.0548	11.0	110	0.2286	0.9194	0.8756	0.9583	0.8514	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.4	0.5714	0.7917	1.0	0.8837	1.0	0.8571	0.9231
0.0214	12.0	120	0.7384	0.8871	0.8498	0.9462	0.8229	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.4	0.5714	0.7308	1.0	0.8444	1.0	0.7143	0.8333
0.0505	13.0	130	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
0.0013	14.0	140	0.1499	0.9516	0.9426	0.9295	0.9684	1.0	1.0	1.0	1.0	1.0	1.0	0.7143	1.0	0.8333	1.0	0.8421	0.9143	0.9333	1.0	0.9655
0.0228	15.0	150	0.1125	0.9516	0.9614	0.9727	0.9571	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8636	1.0	0.9268	1.0	0.7857	0.88
0.008	16.0	160	0.3561	0.9032	0.8630	0.952	0.8371	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.4	0.5714	0.76	1.0	0.8636	1.0	0.7857	0.88
0.0004	17.0	170	0.1422	0.9516	0.9249	0.9233	0.9314	1.0	1.0	1.0	1.0	1.0	1.0	0.6667	0.8	0.7273	0.95	1.0	0.9744	1.0	0.8571	0.9231
0.0013	18.0	180	0.0573	0.9839	0.9764	0.9667	0.9895	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091	1.0	0.9474	0.9730	1.0	1.0	1.0
0.0175	19.0	190	0.3037	0.9516	0.9478	0.9476	0.9571	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091	0.9048	1.0	0.95	1.0	0.7857	0.88
0.0008	20.0	200	0.0023	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	21.0	210	0.1787	0.9516	0.8997	0.9727	0.8800	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.4	0.5714	0.8636	1.0	0.9268	1.0	1.0	1.0
0.0001	22.0	220	0.4554	0.9355	0.8878	0.9652	0.8657	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.4	0.5714	0.8261	1.0	0.9048	1.0	0.9286	0.9630
0.0001	23.0	230	0.2331	0.9355	0.8878	0.9652	0.8657	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.4	0.5714	0.8261	1.0	0.9048	1.0	0.9286	0.9630
0.0001	24.0	240	0.1128	0.9677	0.9604	0.9810	0.9457	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.9048	1.0	0.95	1.0	0.9286	0.9630
0.0001	25.0	250	0.0764	0.9677	0.9604	0.9810	0.9457	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.9048	1.0	0.95	1.0	0.9286	0.9630
0.0	26.0	260	0.0616	0.9677	0.9604	0.9810	0.9457	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.9048	1.0	0.95	1.0	0.9286	0.9630
0.0	27.0	270	0.0546	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	28.0	280	0.0533	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0001	29.0	290	0.0214	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	30.0	300	0.0165	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	31.0	310	0.0175	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	32.0	320	0.0197	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	33.0	330	0.0217	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	34.0	340	0.0253	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	35.0	350	0.0273	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	36.0	360	0.0307	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	37.0	370	0.0345	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	38.0	380	0.0361	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	39.0	390	0.0369	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	40.0	400	0.0375	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	41.0	410	0.0384	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	42.0	420	0.0390	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	43.0	430	0.0378	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	44.0	440	0.0374	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	45.0	450	0.0377	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	46.0	460	0.0378	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	47.0	470	0.0378	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	48.0	480	0.0380	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	49.0	490	0.0382	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	1.0	1.0	1.0
0.0	50.0	500	0.0381	0.9839	0.9726	0.99	0.96	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.95	1.0	0.9744	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

Task: Image Classification
Library: transformers
Type: convnextv2
License: apache-2.0
Created: Nov 5, 2025
Updated: Nov 5, 2025