vit-base-patch16-384-finetuned-humid-classes-9

This model is a fine-tuned version of google/vit-base-patch16-384 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.0156
• 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 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	1.3884	0.4815	0.2533	0.2143	0.3263	0.0	0.0	0.0	0.5	1.0	0.6667	0.0	0.0	0.0	0.5714	0.6316	0.6	0.0	0.0	0.0
1.352	2.0	10	1.0193	0.7963	0.5380	0.4968	0.5895	0.9167	1.0	0.9565	0.875	1.0	0.9333	0.0	0.0	0.0	0.6923	0.9474	0.8	0.0	0.0	0.0
1.352	3.0	15	0.6250	0.8148	0.5583	0.5310	0.6	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.6552	1.0	0.7917	0.0	0.0	0.0
0.6341	4.0	20	0.3860	0.8704	0.7383	0.7367	0.7495	1.0	1.0	1.0	0.9333	1.0	0.9655	0.0	0.0	0.0	0.75	0.9474	0.8372	1.0	0.8	0.8889
0.6341	5.0	25	0.2527	0.9259	0.9140	0.9179	0.9284	1.0	1.0	1.0	0.875	1.0	0.9333	1.0	0.8	0.8889	1.0	0.8421	0.9143	0.7143	1.0	0.8333
0.2197	6.0	30	0.1329	0.9630	0.9604	0.9761	0.9495	1.0	1.0	1.0	0.9333	1.0	0.9655	1.0	0.8	0.8889	0.9474	0.9474	0.9474	1.0	1.0	1.0
0.2197	7.0	35	0.1464	0.9444	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
0.1142	8.0	40	0.0711	0.9815	0.9877	0.9867	0.9895	1.0	1.0	1.0	0.9333	1.0	0.9655	1.0	1.0	1.0	1.0	0.9474	0.9730	1.0	1.0	1.0
0.1142	9.0	45	0.1523	0.9259	0.8888	0.9167	0.8695	1.0	1.0	1.0	0.9333	1.0	0.9655	0.75	0.6	0.6667	0.9	0.9474	0.9231	1.0	0.8	0.8889
0.0468	10.0	50	0.0735	0.9815	0.9596	0.9667	0.96	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091	1.0	1.0	1.0	1.0	0.8	0.8889
0.0468	11.0	55	0.1200	0.9630	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
0.0119	12.0	60	0.1084	0.9444	0.9205	0.925	0.9389	1.0	1.0	1.0	1.0	1.0	1.0	0.625	1.0	0.7692	1.0	0.8947	0.9444	1.0	0.8	0.8889
0.0119	13.0	65	0.0984	0.9630	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
0.0105	14.0	70	0.0510	0.9815	0.9877	0.9867	0.9895	1.0	1.0	1.0	0.9333	1.0	0.9655	1.0	1.0	1.0	1.0	0.9474	0.9730	1.0	1.0	1.0
0.0105	15.0	75	0.0485	0.9815	0.9877	0.9867	0.9895	1.0	1.0	1.0	0.9333	1.0	0.9655	1.0	1.0	1.0	1.0	0.9474	0.9730	1.0	1.0	1.0
0.0031	16.0	80	0.1964	0.9259	0.9234	0.9117	0.9579	1.0	1.0	1.0	0.9333	1.0	0.9655	0.625	1.0	0.7692	1.0	0.7895	0.8824	1.0	1.0	1.0
0.0031	17.0	85	0.0908	0.9815	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
0.0032	18.0	90	0.0334	0.9815	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
0.0032	19.0	95	0.0156	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	20.0	100	0.0115	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	21.0	105	0.0105	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
0.001	22.0	110	0.0129	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
0.001	23.0	115	0.0160	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
0.0009	24.0	120	0.0188	0.9815	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
0.0009	25.0	125	0.0202	0.9815	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
0.0007	26.0	130	0.0196	0.9815	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
0.0007	27.0	135	0.0183	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	28.0	140	0.0172	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	29.0	145	0.0162	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
0.0007	30.0	150	0.0077	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
0.0007	31.0	155	0.0070	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	0.0072	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	0.0083	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	34.0	170	0.0106	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	35.0	175	0.0130	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	36.0	180	0.0149	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	37.0	185	0.0165	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	38.0	190	0.0172	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	39.0	195	0.0175	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	0.0173	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	0.0170	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	0.0168	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	0.0165	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
0.0004	44.0	220	0.0162	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
0.0004	45.0	225	0.0160	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
0.0004	46.0	230	0.0160	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
0.0004	47.0	235	0.0160	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
0.0004	48.0	240	0.0159	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
0.0004	49.0	245	0.0159	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
0.0004	50.0	250	0.0159	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 2.5.1+cu124
• Datasets 4.0.0
• Tokenizers 0.22.0