vit-base-patch32-384-finetuned-humid-classes-nov26-16-52

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: 0.3965
• Accuracy: 0.9310
• F1 Macro: 0.9250
• Precision Macro: 0.9250
• Recall Macro: 0.9250
• 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 Moist: 0.8
• Recall Moist: 0.8
• F1 Moist: 0.8
• Precision Rockies: 0.75
• Recall Rockies: 0.75
• F1 Rockies: 0.75

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 Moist	Recall Moist	F1 Moist	Precision Rockies	Recall Rockies	F1 Rockies
No log	1.0	3	1.8378	0.2069	0.1151	0.2029	0.2000	0.2174	1.0	0.3571	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.2	0.3333	0.0	0.0	0.0	0.0	0.0	0.0
No log	2.0	6	1.6327	0.3448	0.2563	0.2633	0.3333	0.2941	1.0	0.4545	1.0	0.6	0.75	0.0	0.0	0.0	0.0	0.0	0.0	0.2857	0.4	0.3333	0.0	0.0	0.0
No log	3.0	9	1.3258	0.5862	0.5093	0.5597	0.5750	0.625	1.0	0.7692	0.8333	1.0	0.9091	0.5	0.4	0.4444	0.0	0.0	0.0	0.4	0.8	0.5333	1.0	0.25	0.4
1.6213	4.0	12	1.0068	0.6552	0.5195	0.4491	0.6333	0.5556	1.0	0.7143	0.625	1.0	0.7692	0.7143	1.0	0.8333	0.0	0.0	0.0	0.8	0.8	0.8	0.0	0.0	0.0
1.6213	5.0	15	0.7893	0.7931	0.7603	0.7931	0.775	0.625	1.0	0.7692	1.0	1.0	1.0	0.8333	1.0	0.9091	1.0	0.6	0.75	0.8	0.8	0.8	0.5	0.25	0.3333
1.6213	6.0	18	0.5311	0.8966	0.8831	0.8889	0.8833	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.6667	0.8	0.7273	0.6667	0.5	0.5714
0.7335	7.0	21	0.5711	0.7931	0.7974	0.8381	0.7917	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	1.0	0.6	0.75	0.6	0.6	0.6	0.4286	0.75	0.5455
0.7335	8.0	24	0.3965	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.7335	9.0	27	0.3462	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.2638	10.0	30	0.4501	0.8966	0.8944	0.9028	0.8917	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.6667	0.8	0.7273	0.75	0.75	0.75
0.2638	11.0	33	0.3489	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.2638	12.0	36	0.4128	0.8621	0.85	0.8611	0.8583	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.6667	0.4	0.5	0.5	0.75	0.6
0.2638	13.0	39	0.3852	0.8621	0.8494	0.8611	0.85	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091	1.0	0.8	0.8889	0.6667	0.8	0.7273	0.6667	0.5	0.5714
0.0974	14.0	42	0.3861	0.8966	0.8944	0.9028	0.8917	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.6667	0.8	0.7273	0.75	0.75	0.75
0.0974	15.0	45	0.4721	0.8621	0.8593	0.875	0.8583	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.75	0.6	0.6667	0.5	0.75	0.6
0.0974	16.0	48	0.3695	0.8966	0.8831	0.8889	0.8833	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.6667	0.8	0.7273	0.6667	0.5	0.5714
0.0607	17.0	51	0.3712	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0607	18.0	54	0.5031	0.8621	0.85	0.8611	0.8583	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.6667	0.4	0.5	0.5	0.75	0.6
0.0607	19.0	57	0.5480	0.8621	0.85	0.8611	0.8583	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.6667	0.4	0.5	0.5	0.75	0.6
0.1028	20.0	60	0.5037	0.8966	0.8944	0.9028	0.8917	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.6667	0.8	0.7273	0.75	0.75	0.75
0.1028	21.0	63	0.5064	0.8966	0.8944	0.9028	0.8917	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.6667	0.8	0.7273	0.75	0.75	0.75
0.1028	22.0	66	0.4010	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.1028	23.0	69	0.3932	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0237	24.0	72	0.4035	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0237	25.0	75	0.3894	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0237	26.0	78	0.3331	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0687	27.0	81	0.3129	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0687	28.0	84	0.3186	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0687	29.0	87	0.3391	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0249	30.0	90	0.3669	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0249	31.0	93	0.3958	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0249	32.0	96	0.4056	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0249	33.0	99	0.4084	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0139	34.0	102	0.4017	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0139	35.0	105	0.3759	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0139	36.0	108	0.3801	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0091	37.0	111	0.4095	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0091	38.0	114	0.4565	0.8966	0.8889	0.8917	0.8917	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.75	0.6	0.6667	0.6	0.75	0.6667
0.0091	39.0	117	0.4905	0.8621	0.85	0.8611	0.8583	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.6667	0.4	0.5	0.5	0.75	0.6
0.0241	40.0	120	0.4680	0.8966	0.8889	0.8917	0.8917	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.75	0.6	0.6667	0.6	0.75	0.6667
0.0241	41.0	123	0.4604	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0241	42.0	126	0.4651	0.9310	0.9250	0.9250	0.9250	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8	0.8	0.75	0.75	0.75
0.0241	43.0	129	0.4763	0.8966	0.8913	0.8972	0.8917	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091	1.0	0.8	0.8889	0.8	0.8	0.8	0.75	0.75	0.75
0.0174	44.0	132	0.4808	0.8966	0.8913	0.8972	0.8917	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091	1.0	0.8	0.8889	0.8	0.8	0.8	0.75	0.75	0.75
0.0174	45.0	135	0.4797	0.8966	0.8913	0.8972	0.8917	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091	1.0	0.8	0.8889	0.8	0.8	0.8	0.75	0.75	0.75
0.0174	46.0	138	0.4782	0.8966	0.8913	0.8972	0.8917	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091	1.0	0.8	0.8889	0.8	0.8	0.8	0.75	0.75	0.75
0.0212	47.0	141	0.4754	0.8966	0.8913	0.8972	0.8917	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091	1.0	0.8	0.8889	0.8	0.8	0.8	0.75	0.75	0.75
0.0212	48.0	144	0.4731	0.8966	0.8913	0.8972	0.8917	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091	1.0	0.8	0.8889	0.8	0.8	0.8	0.75	0.75	0.75
0.0212	49.0	147	0.4714	0.8966	0.8944	0.9028	0.8917	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.6667	0.8	0.7273	0.75	0.75	0.75
0.0158	50.0	150	0.4708	0.8966	0.8944	0.9028	0.8917	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.8	0.8889	0.6667	0.8	0.7273	0.75	0.75	0.75

Framework versions

• Transformers 4.57.1
• Pytorch 2.9.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.0