vit-base-patch32-384-finetuned-humid-classes-16

dacunaq/vit-base-patch32-384-finetuned-humid-classes-16

vit-base-patch32-384-finetuned-humid-classes-16 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: vit
Access: Public
Created: Nov 4, 2025
Updated: Nov 4, 2025
SHA: 4e6ebf49

Base models

google/vit-base-patch32-384

Datasets

imagefolder

Model card

From the Hugging Face model README

vit-base-patch32-384-finetuned-humid-classes-16

This model is a fine-tuned version of google/vit-base-patch32-384 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0276
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: 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 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	5	1.5025	0.3065	0.2746	0.3647	0.2907	0.8571	0.6	0.7059	0.5	0.0714	0.125	0.0	0.0	0.0	0.25	0.2105	0.2286	0.2162	0.5714	0.3137
1.5391	2.0	10	1.2375	0.6452	0.4853	0.4473	0.5552	1.0	0.9	0.9474	0.7222	0.9286	0.8125	0.0	0.0	0.0	0.5143	0.9474	0.6667	0.0	0.0	0.0
1.5391	3.0	15	0.7925	0.7903	0.6672	0.6933	0.6895	1.0	1.0	1.0	0.8235	1.0	0.9032	0.0	0.0	0.0	0.6429	0.9474	0.7660	1.0	0.5	0.6667
0.7047	4.0	20	0.4438	0.9032	0.8527	0.8917	0.8371	1.0	1.0	1.0	1.0	1.0	1.0	0.6667	0.4	0.5	0.7917	1.0	0.8837	1.0	0.7857	0.88
0.7047	5.0	25	0.2924	0.9032	0.8634	0.9432	0.8409	1.0	1.0	1.0	0.9333	1.0	0.9655	1.0	0.4	0.5714	0.7826	0.9474	0.8571	1.0	0.8571	0.9231
0.223	6.0	30	0.1850	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
0.223	7.0	35	0.2624	0.9032	0.8875	0.8909	0.9368	1.0	1.0	1.0	1.0	1.0	1.0	0.4545	1.0	0.625	1.0	0.6842	0.8125	1.0	1.0	1.0
0.1237	8.0	40	0.2641	0.9355	0.9156	0.9652	0.8914	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.6	0.75	0.8261	1.0	0.9048	1.0	0.8571	0.9231
0.1237	9.0	45	0.1824	0.9194	0.9057	0.8978	0.9474	1.0	1.0	1.0	0.9333	1.0	0.9655	0.5556	1.0	0.7143	1.0	0.7368	0.8485	1.0	1.0	1.0
0.0554	10.0	50	0.0800	0.9516	0.9280	0.9727	0.9057	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.6	0.75	0.8636	1.0	0.9268	1.0	0.9286	0.9630
0.0554	11.0	55	0.0765	0.9516	0.9408	0.9323	0.9609	1.0	1.0	1.0	1.0	1.0	1.0	0.7143	1.0	0.8333	0.9474	0.9474	0.9474	1.0	0.8571	0.9231
0.0123	12.0	60	0.0622	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
0.0123	13.0	65	0.0775	0.9677	0.9400	0.9810	0.9200	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.6	0.75	0.9048	1.0	0.95	1.0	1.0	1.0
0.0051	14.0	70	0.0534	0.9677	0.9638	0.9533	0.9789	1.0	1.0	1.0	0.9333	1.0	0.9655	0.8333	1.0	0.9091	1.0	0.8947	0.9444	1.0	1.0	1.0
0.0051	15.0	75	0.0446	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
0.0023	16.0	80	0.1137	0.9516	0.9280	0.9727	0.9057	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.6	0.75	0.8636	1.0	0.9268	1.0	0.9286	0.9630
0.0023	17.0	85	0.0707	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
0.0154	18.0	90	0.1571	0.9677	0.9400	0.9810	0.9200	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.6	0.75	0.9048	1.0	0.95	1.0	1.0	1.0
0.0154	19.0	95	0.1066	0.9677	0.9556	0.9429	0.9789	1.0	1.0	1.0	1.0	1.0	1.0	0.7143	1.0	0.8333	1.0	0.8947	0.9444	1.0	1.0	1.0
0.0069	20.0	100	0.0959	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
0.0069	21.0	105	0.1878	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
0.0049	22.0	110	0.0524	0.9677	0.9556	0.9429	0.9789	1.0	1.0	1.0	1.0	1.0	1.0	0.7143	1.0	0.8333	1.0	0.8947	0.9444	1.0	1.0	1.0
0.0049	23.0	115	0.1435	0.9677	0.9556	0.9429	0.9789	1.0	1.0	1.0	1.0	1.0	1.0	0.7143	1.0	0.8333	1.0	0.8947	0.9444	1.0	1.0	1.0
0.0016	24.0	120	0.0629	0.9677	0.9556	0.9429	0.9789	1.0	1.0	1.0	1.0	1.0	1.0	0.7143	1.0	0.8333	1.0	0.8947	0.9444	1.0	1.0	1.0
0.0016	25.0	125	0.0654	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
0.0008	26.0	130	0.1026	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
0.0008	27.0	135	0.1043	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
0.0009	28.0	140	0.0898	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
0.0009	29.0	145	0.0589	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
0.0008	30.0	150	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
0.0008	31.0	155	0.0276	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
0.0006	32.0	160	0.0252	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
0.0006	33.0	165	0.0246	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
0.0006	34.0	170	0.0245	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
0.0006	35.0	175	0.0245	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
0.0005	36.0	180	0.0244	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
0.0005	37.0	185	0.0241	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
0.0005	38.0	190	0.0241	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
0.0005	39.0	195	0.0241	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
0.0005	40.0	200	0.0240	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
0.0005	41.0	205	0.0240	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
0.0005	42.0	210	0.0240	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
0.0005	43.0	215	0.0239	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
0.0005	44.0	220	0.0239	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
0.0005	45.0	225	0.0238	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
0.0005	46.0	230	0.0238	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
0.0005	47.0	235	0.0238	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
0.0005	48.0	240	0.0238	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
0.0005	49.0	245	0.0238	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
0.0005	50.0	250	0.0238	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

Framework versions

Transformers 4.56.1
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Nov 4, 2025
Updated: Nov 4, 2025