vit-base-patch32-384-finetuned-humid-classes-22

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: 0.0888
• Accuracy: 0.9787
• F1 Macro: 0.9801
• Precision Macro: 0.9722
• Recall Macro: 0.9907
• 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: 0.8333
• Recall Humid: 1.0
• F1 Humid: 0.9091
• Precision Lump: 1.0
• Recall Lump: 0.9444
• F1 Lump: 0.9714
• Precision Moist: 1.0
• Recall Moist: 1.0
• F1 Moist: 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 Moist	Recall Moist	F1 Moist	Precision Rockies	Recall Rockies	F1 Rockies
No log	1.0	3	1.7369	0.3191	0.1724	0.1845	0.1921	0.0	0.0	0.0	0.4783	0.7857	0.5946	0.2	0.2	0.2	0.4286	0.1667	0.24	0.0	0.0	0.0	0.0	0.0	0.0
No log	2.0	6	1.4332	0.5319	0.2157	0.1883	0.2685	0.0	0.0	0.0	0.5185	1.0	0.6829	0.0	0.0	0.0	0.6111	0.6111	0.6111	0.0	0.0	0.0	0.0	0.0	0.0
No log	3.0	9	1.1422	0.6809	0.3207	0.3911	0.3574	0.0	0.0	0.0	0.6667	1.0	0.8	1.0	0.2	0.3333	0.68	0.9444	0.7907	0.0	0.0	0.0	0.0	0.0	0.0
1.651	4.0	12	0.8563	0.7872	0.6695	0.8816	0.6176	1.0	0.3333	0.5	0.7778	1.0	0.875	1.0	0.4	0.5714	0.7619	0.8889	0.8205	0.75	0.75	0.75	1.0	0.3333	0.5
1.651	5.0	15	0.6674	0.8511	0.8102	0.9042	0.7843	0.75	1.0	0.8571	0.875	1.0	0.9333	1.0	0.4	0.5714	0.8	0.8889	0.8421	1.0	0.75	0.8571	1.0	0.6667	0.8
1.651	6.0	18	0.4374	0.9149	0.8639	0.9429	0.8444	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.4	0.5714	0.8571	1.0	0.9231	0.8	1.0	0.8889	1.0	0.6667	0.8
0.7174	7.0	21	0.3417	0.9149	0.8586	0.9345	0.8444	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.4	0.5714	0.8571	1.0	0.9231	1.0	1.0	1.0	1.0	0.6667	0.8
0.7174	8.0	24	0.2149	0.9149	0.8547	0.9	0.8361	0.75	1.0	0.8571	1.0	1.0	1.0	0.75	0.6	0.6667	0.9	1.0	0.9474	1.0	0.75	0.8571	1.0	0.6667	0.8
0.7174	9.0	27	0.2210	0.9149	0.8586	0.9345	0.8444	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.4	0.5714	0.8571	1.0	0.9231	1.0	1.0	1.0	1.0	0.6667	0.8
0.149	10.0	30	0.1764	0.9362	0.8812	0.9162	0.8694	0.75	1.0	0.8571	1.0	1.0	1.0	0.8	0.8	0.8	0.9474	1.0	0.9730	1.0	0.75	0.8571	1.0	0.6667	0.8
0.149	11.0	33	0.2211	0.9149	0.8586	0.9345	0.8444	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.4	0.5714	0.8571	1.0	0.9231	1.0	1.0	1.0	1.0	0.6667	0.8
0.149	12.0	36	0.0888	0.9787	0.9801	0.9722	0.9907	1.0	1.0	1.0	1.0	1.0	1.0	0.8333	1.0	0.9091	1.0	0.9444	0.9714	1.0	1.0	1.0	1.0	1.0	1.0
0.149	13.0	39	0.2047	0.9362	0.8924	0.9417	0.8778	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.6	0.75	0.9	1.0	0.9474	1.0	1.0	1.0	1.0	0.6667	0.8
0.023	14.0	42	0.1937	0.9574	0.9198	0.9496	0.9111	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.8	0.8889	0.9474	1.0	0.9730	1.0	1.0	1.0	1.0	0.6667	0.8
0.023	15.0	45	0.1983	0.9574	0.9198	0.9496	0.9111	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.8	0.8889	0.9474	1.0	0.9730	1.0	1.0	1.0	1.0	0.6667	0.8
0.023	16.0	48	0.2206	0.9362	0.8924	0.9417	0.8778	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.6	0.75	0.9	1.0	0.9474	1.0	1.0	1.0	1.0	0.6667	0.8
0.0069	17.0	51	0.1683	0.9362	0.9003	0.9157	0.9019	0.75	1.0	0.8571	1.0	1.0	1.0	0.8	0.8	0.8	0.9444	0.9444	0.9444	1.0	1.0	1.0	1.0	0.6667	0.8
0.0069	18.0	54	0.1379	0.9362	0.9003	0.9157	0.9019	0.75	1.0	0.8571	1.0	1.0	1.0	0.8	0.8	0.8	0.9444	0.9444	0.9444	1.0	1.0	1.0	1.0	0.6667	0.8
0.0069	19.0	57	0.1356	0.9362	0.9003	0.9157	0.9019	0.75	1.0	0.8571	1.0	1.0	1.0	0.8	0.8	0.8	0.9444	0.9444	0.9444	1.0	1.0	1.0	1.0	0.6667	0.8
0.0033	20.0	60	0.1543	0.9362	0.9003	0.9157	0.9019	0.75	1.0	0.8571	1.0	1.0	1.0	0.8	0.8	0.8	0.9444	0.9444	0.9444	1.0	1.0	1.0	1.0	0.6667	0.8
0.0033	21.0	63	0.1923	0.9574	0.9198	0.9496	0.9111	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.8	0.8889	0.9474	1.0	0.9730	1.0	1.0	1.0	1.0	0.6667	0.8
0.0033	22.0	66	0.2434	0.9362	0.8924	0.9417	0.8778	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.6	0.75	0.9	1.0	0.9474	1.0	1.0	1.0	1.0	0.6667	0.8
0.0033	23.0	69	0.2865	0.9149	0.8586	0.9345	0.8444	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.4	0.5714	0.8571	1.0	0.9231	1.0	1.0	1.0	1.0	0.6667	0.8
0.0017	24.0	72	0.3160	0.9149	0.8586	0.9345	0.8444	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.4	0.5714	0.8571	1.0	0.9231	1.0	1.0	1.0	1.0	0.6667	0.8
0.0017	25.0	75	0.3190	0.9149	0.8586	0.9345	0.8444	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.4	0.5714	0.8571	1.0	0.9231	1.0	1.0	1.0	1.0	0.6667	0.8
0.0017	26.0	78	0.3077	0.9149	0.8586	0.9345	0.8444	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.4	0.5714	0.8571	1.0	0.9231	1.0	1.0	1.0	1.0	0.6667	0.8
0.0014	27.0	81	0.2909	0.9149	0.8547	0.9	0.8361	0.75	1.0	0.8571	1.0	1.0	1.0	0.75	0.6	0.6667	0.9	1.0	0.9474	1.0	0.75	0.8571	1.0	0.6667	0.8
0.0014	28.0	84	0.2722	0.9149	0.8547	0.9	0.8361	0.75	1.0	0.8571	1.0	1.0	1.0	0.75	0.6	0.6667	0.9	1.0	0.9474	1.0	0.75	0.8571	1.0	0.6667	0.8
0.0014	29.0	87	0.2533	0.9149	0.8547	0.9	0.8361	0.75	1.0	0.8571	1.0	1.0	1.0	0.75	0.6	0.6667	0.9	1.0	0.9474	1.0	0.75	0.8571	1.0	0.6667	0.8
0.0012	30.0	90	0.2378	0.9149	0.8547	0.9	0.8361	0.75	1.0	0.8571	1.0	1.0	1.0	0.75	0.6	0.6667	0.9	1.0	0.9474	1.0	0.75	0.8571	1.0	0.6667	0.8
0.0012	31.0	93	0.2284	0.9362	0.8812	0.9162	0.8694	0.75	1.0	0.8571	1.0	1.0	1.0	0.8	0.8	0.8	0.9474	1.0	0.9730	1.0	0.75	0.8571	1.0	0.6667	0.8
0.0012	32.0	96	0.2225	0.9362	0.8812	0.9162	0.8694	0.75	1.0	0.8571	1.0	1.0	1.0	0.8	0.8	0.8	0.9474	1.0	0.9730	1.0	0.75	0.8571	1.0	0.6667	0.8
0.0012	33.0	99	0.2183	0.9574	0.9198	0.9496	0.9111	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.8	0.8889	0.9474	1.0	0.9730	1.0	1.0	1.0	1.0	0.6667	0.8
0.0011	34.0	102	0.2165	0.9574	0.9198	0.9496	0.9111	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.8	0.8889	0.9474	1.0	0.9730	1.0	1.0	1.0	1.0	0.6667	0.8
0.0011	35.0	105	0.2166	0.9574	0.9198	0.9496	0.9111	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.8	0.8889	0.9474	1.0	0.9730	1.0	1.0	1.0	1.0	0.6667	0.8
0.0011	36.0	108	0.2170	0.9574	0.9198	0.9496	0.9111	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.8	0.8889	0.9474	1.0	0.9730	1.0	1.0	1.0	1.0	0.6667	0.8
0.001	37.0	111	0.2198	0.9574	0.9198	0.9496	0.9111	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.8	0.8889	0.9474	1.0	0.9730	1.0	1.0	1.0	1.0	0.6667	0.8
0.001	38.0	114	0.2230	0.9362	0.8924	0.9417	0.8778	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.6	0.75	0.9	1.0	0.9474	1.0	1.0	1.0	1.0	0.6667	0.8
0.001	39.0	117	0.2266	0.9362	0.8924	0.9417	0.8778	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.6	0.75	0.9	1.0	0.9474	1.0	1.0	1.0	1.0	0.6667	0.8
0.0009	40.0	120	0.2291	0.9362	0.8924	0.9417	0.8778	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.6	0.75	0.9	1.0	0.9474	1.0	1.0	1.0	1.0	0.6667	0.8
0.0009	41.0	123	0.2307	0.9362	0.8924	0.9417	0.8778	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.6	0.75	0.9	1.0	0.9474	1.0	1.0	1.0	1.0	0.6667	0.8
0.0009	42.0	126	0.2322	0.9362	0.8924	0.9417	0.8778	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.6	0.75	0.9	1.0	0.9474	1.0	1.0	1.0	1.0	0.6667	0.8
0.0009	43.0	129	0.2340	0.9362	0.8924	0.9417	0.8778	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.6	0.75	0.9	1.0	0.9474	1.0	1.0	1.0	1.0	0.6667	0.8
0.0009	44.0	132	0.2353	0.9362	0.8924	0.9417	0.8778	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.6	0.75	0.9	1.0	0.9474	1.0	1.0	1.0	1.0	0.6667	0.8
0.0009	45.0	135	0.2367	0.9362	0.8924	0.9417	0.8778	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.6	0.75	0.9	1.0	0.9474	1.0	1.0	1.0	1.0	0.6667	0.8
0.0009	46.0	138	0.2381	0.9362	0.8924	0.9417	0.8778	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.6	0.75	0.9	1.0	0.9474	1.0	1.0	1.0	1.0	0.6667	0.8
0.0009	47.0	141	0.2393	0.9362	0.8924	0.9417	0.8778	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.6	0.75	0.9	1.0	0.9474	1.0	1.0	1.0	1.0	0.6667	0.8
0.0009	48.0	144	0.2400	0.9362	0.8924	0.9417	0.8778	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.6	0.75	0.9	1.0	0.9474	1.0	1.0	1.0	1.0	0.6667	0.8
0.0009	49.0	147	0.2404	0.9362	0.8924	0.9417	0.8778	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.6	0.75	0.9	1.0	0.9474	1.0	1.0	1.0	1.0	0.6667	0.8
0.0008	50.0	150	0.2405	0.9362	0.8924	0.9417	0.8778	0.75	1.0	0.8571	1.0	1.0	1.0	1.0	0.6	0.75	0.9	1.0	0.9474	1.0	1.0	1.0	1.0	0.6667	0.8

Framework versions

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