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: 0.5194
Accuracy: 0.8667
F1 Macro: 0.8611
Precision Macro: 0.8730
Recall Macro: 0.8667
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: 0.5714
Recall Moist: 0.8
F1 Moist: 0.6667
Precision Rockies: 0.6667
Recall Rockies: 0.4
F1 Rockies: 0.5

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	1.8423	0.1333	0.0513	0.0317	0.1333	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1905	0.8	0.3077
No log	2.0	6	1.6345	0.4	0.3954	0.5444	0.4000	0.2667	0.8	0.4	1.0	0.6	0.75	1.0	0.8	0.8889	1.0	0.2	0.3333	0.0	0.0	0.0	0.0	0.0	0.0
No log	3.0	9	1.4049	0.5667	0.4726	0.4317	0.5667	0.3571	1.0	0.5263	0.8333	1.0	0.9091	1.0	1.0	1.0	0.0	0.0	0.0	0.4	0.4	0.4	0.0	0.0	0.0
1.7012	4.0	12	1.1559	0.6	0.4884	0.4274	0.6	0.4167	1.0	0.5882	0.7143	1.0	0.8333	0.8333	1.0	0.9091	0.0	0.0	0.0	0.6	0.6	0.6	0.0	0.0	0.0
1.7012	5.0	15	0.9138	0.7333	0.7071	0.7593	0.7333	1.0	0.8	0.8889	0.8333	1.0	0.9091	0.8333	1.0	0.9091	1.0	0.4	0.5714	0.5556	1.0	0.7143	0.3333	0.2	0.25
1.7012	6.0	18	0.7135	0.7333	0.7428	0.7778	0.7333	1.0	0.8	0.8889	0.8333	1.0	0.9091	1.0	1.0	1.0	1.0	0.6	0.75	0.5	0.6	0.5455	0.3333	0.4	0.3636
0.7754	7.0	21	0.7240	0.8333	0.8148	0.8426	0.8333	1.0	0.8	0.8889	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5556	1.0	0.7143	0.5	0.2	0.2857
0.7754	8.0	24	0.5194	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.7754	9.0	27	0.4991	0.8333	0.8333	0.8452	0.8333	1.0	0.8	0.8889	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.5	0.4	0.4444
0.3095	10.0	30	0.5101	0.8333	0.8333	0.8452	0.8333	1.0	0.8	0.8889	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.5	0.4	0.4444
0.3095	11.0	33	0.4442	0.8	0.7917	0.7937	0.8000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.3333	0.2	0.25	0.4286	0.6	0.5
0.3095	12.0	36	0.5977	0.8	0.7924	0.8056	0.8000	1.0	0.8	0.8889	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5	0.8	0.6154	0.3333	0.2	0.25
0.3095	13.0	39	0.5819	0.7667	0.7667	0.7847	0.7667	1.0	0.8	0.8889	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.3333	0.2	0.25	0.375	0.6	0.4615
0.1088	14.0	42	0.5983	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.1088	15.0	45	0.7568	0.8667	0.8413	0.9259	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5556	1.0	0.7143	1.0	0.2	0.3333
0.1088	16.0	48	0.6159	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.114	17.0	51	0.7233	0.7667	0.7667	0.7847	0.7667	1.0	0.8	0.8889	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.3333	0.2	0.25	0.375	0.6	0.4615
0.114	18.0	54	0.7060	0.8333	0.8333	0.8452	0.8333	1.0	0.8	0.8889	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.5	0.4	0.4444
0.114	19.0	57	0.6106	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0569	20.0	60	0.5845	0.8667	0.8667	0.8667	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.6	0.6	0.6	0.6	0.6	0.6
0.0569	21.0	63	0.6314	0.8333	0.8316	0.8333	0.8333	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5	0.4	0.4444	0.5	0.6	0.5455
0.0569	22.0	66	0.6409	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0569	23.0	69	0.6653	0.8	0.8057	0.8167	0.8000	1.0	0.8	0.8889	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5	0.6	0.5455	0.4	0.4	0.4
0.024	24.0	72	0.7143	0.8333	0.8333	0.8452	0.8333	1.0	0.8	0.8889	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.5	0.4	0.4444
0.024	25.0	75	0.7270	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.024	26.0	78	0.7195	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.025	27.0	81	0.6491	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.025	28.0	84	0.6040	0.8333	0.8316	0.8333	0.8333	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5	0.6	0.5455	0.5	0.4	0.4444
0.025	29.0	87	0.6655	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0163	30.0	90	0.7635	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0163	31.0	93	0.7816	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0163	32.0	96	0.7384	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0163	33.0	99	0.7122	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0289	34.0	102	0.6932	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0289	35.0	105	0.6609	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0289	36.0	108	0.6479	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0218	37.0	111	0.6768	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0218	38.0	114	0.7241	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0218	39.0	117	0.7362	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0203	40.0	120	0.7305	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0203	41.0	123	0.7395	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0203	42.0	126	0.7528	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0203	43.0	129	0.7695	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0108	44.0	132	0.7858	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0108	45.0	135	0.7961	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0108	46.0	138	0.8051	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0087	47.0	141	0.8116	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0087	48.0	144	0.8151	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.0087	49.0	147	0.8179	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5
0.008	50.0	150	0.8181	0.8667	0.8611	0.8730	0.8667	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.5714	0.8	0.6667	0.6667	0.4	0.5

Framework versions

Transformers 4.57.1
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0

dacunaq/vit-base-patch32-384-finetuned-humid-classes-nov26-14-44

vit-base-patch32-384-finetuned-humid-classes-nov26-14-44

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dacunaq/
vit-base-patch32-384-finetuned-humid-classes-nov26-14-44