convnextv2-tiny-1k-224-finetuned-fullwear

This model is a fine-tuned version of facebook/convnextv2-tiny-1k-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.5203
• Accuracy: 0.8403

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: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 120

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.4871	0.9756	10	2.4771	0.0694
2.4464	1.9512	20	2.4333	0.1528
2.3911	2.9268	30	2.3670	0.2778
2.3204	4.0	41	2.2617	0.3681
2.206	4.9756	51	2.1445	0.3958
2.0869	5.9512	61	2.0146	0.4444
1.9756	6.9268	71	1.8763	0.5139
1.8124	8.0	82	1.7422	0.5486
1.6624	8.9756	92	1.6629	0.5903
1.587	9.9512	102	1.5474	0.6111
1.4746	10.9268	112	1.4577	0.625
1.359	12.0	123	1.3055	0.6736
1.2412	12.9756	133	1.2241	0.6736
1.1374	13.9512	143	1.2003	0.6736
1.0194	14.9268	153	1.0233	0.7569
0.9705	16.0	164	0.9492	0.7847
0.8949	16.9756	174	0.9246	0.75
0.7959	17.9512	184	0.8148	0.7639
0.7491	18.9268	194	0.7858	0.7569
0.6783	20.0	205	0.8010	0.7569
0.6257	20.9756	215	0.7295	0.7847
0.5999	21.9512	225	0.6219	0.8333
0.5701	22.9268	235	0.5932	0.8403
0.4926	24.0	246	0.5970	0.8056
0.4692	24.9756	256	0.6298	0.8194
0.4393	25.9512	266	0.5857	0.8056
0.419	26.9268	276	0.5203	0.8542
0.3454	28.0	287	0.6084	0.8264
0.36	28.9756	297	0.5928	0.8264
0.3265	29.9512	307	0.5303	0.8403
0.3278	30.9268	317	0.6049	0.8194
0.2766	32.0	328	0.5656	0.8264
0.2805	32.9756	338	0.5003	0.8681
0.2505	33.9512	348	0.5412	0.8403
0.2464	34.9268	358	0.5410	0.8333
0.2166	36.0	369	0.5000	0.8472
0.2	36.9756	379	0.5053	0.8056
0.1914	37.9512	389	0.5161	0.8403
0.186	38.9268	399	0.4242	0.8681
0.1592	40.0	410	0.5059	0.8472
0.1598	40.9756	420	0.5143	0.8264
0.1565	41.9512	430	0.4703	0.8542
0.1598	42.9268	440	0.4384	0.8542
0.139	44.0	451	0.4850	0.8403
0.1137	44.9756	461	0.4405	0.8542
0.1158	45.9512	471	0.5250	0.8333
0.1192	46.9268	481	0.5843	0.8194
0.1271	48.0	492	0.4498	0.8611
0.0914	48.9756	502	0.5167	0.8264
0.1079	49.9512	512	0.4648	0.8681
0.091	50.9268	522	0.5321	0.8194
0.1053	52.0	533	0.4402	0.8611
0.0842	52.9756	543	0.4776	0.8542
0.0961	53.9512	553	0.4762	0.8681
0.0896	54.9268	563	0.4477	0.8681
0.0876	56.0	574	0.4951	0.8472
0.0855	56.9756	584	0.5653	0.8125
0.073	57.9512	594	0.5315	0.8472
0.0804	58.9268	604	0.5064	0.8681
0.0765	60.0	615	0.6316	0.8264
0.0782	60.9756	625	0.5733	0.8056
0.069	61.9512	635	0.6994	0.8056
0.0809	62.9268	645	0.4898	0.8611
0.0829	64.0	656	0.6042	0.8194
0.0735	64.9756	666	0.4758	0.8611
0.0763	65.9512	676	0.4921	0.8542
0.0565	66.9268	686	0.4700	0.8681
0.062	68.0	697	0.4944	0.8819
0.0644	68.9756	707	0.4733	0.8681
0.0659	69.9512	717	0.4703	0.8819
0.0625	70.9268	727	0.5075	0.8542
0.042	72.0	738	0.5464	0.8264
0.056	72.9756	748	0.5186	0.8333
0.0858	73.9512	758	0.5403	0.8264
0.0616	74.9268	768	0.5104	0.8472
0.0777	76.0	779	0.5516	0.8403
0.0668	76.9756	789	0.4918	0.8611
0.0585	77.9512	799	0.5692	0.8403
0.0562	78.9268	809	0.5734	0.8403
0.0653	80.0	820	0.5403	0.8264
0.0434	80.9756	830	0.5108	0.8333
0.0483	81.9512	840	0.5699	0.8125
0.0329	82.9268	850	0.6028	0.8056
0.0431	84.0	861	0.5230	0.8333
0.042	84.9756	871	0.5875	0.8194
0.0449	85.9512	881	0.5180	0.8611
0.0512	86.9268	891	0.5425	0.8194
0.0545	88.0	902	0.5690	0.8264
0.0496	88.9756	912	0.5619	0.8611
0.0449	89.9512	922	0.5626	0.8333
0.0405	90.9268	932	0.5267	0.8403
0.0344	92.0	943	0.5617	0.8403
0.0421	92.9756	953	0.5400	0.8611
0.0341	93.9512	963	0.5729	0.8333
0.0492	94.9268	973	0.5855	0.8056
0.0374	96.0	984	0.6113	0.8125
0.0375	96.9756	994	0.5511	0.8403
0.0373	97.9512	1004	0.4942	0.8542
0.0447	98.9268	1014	0.5031	0.8542
0.0519	100.0	1025	0.5349	0.8542
0.0387	100.9756	1035	0.5511	0.8542
0.0256	101.9512	1045	0.5319	0.8403
0.043	102.9268	1055	0.5605	0.8264
0.029	104.0	1066	0.5776	0.8403
0.0379	104.9756	1076	0.5697	0.8472
0.0445	105.9512	1086	0.5133	0.8681
0.0267	106.9268	1096	0.5076	0.8681
0.044	108.0	1107	0.5260	0.8403
0.0263	108.9756	1117	0.5101	0.8542
0.0247	109.9512	1127	0.4972	0.8542
0.0441	110.9268	1137	0.5094	0.8472
0.0263	112.0	1148	0.5259	0.8333
0.0247	112.9756	1158	0.5323	0.8403
0.0356	113.9512	1168	0.5275	0.8403
0.0297	114.9268	1178	0.5240	0.8333
0.044	116.0	1189	0.5201	0.8472
0.031	116.9756	1199	0.5203	0.8403
0.0369	117.0732	1200	0.5203	0.8403

Framework versions

• Transformers 4.44.0
• Pytorch 2.4.0
• Datasets 2.21.0
• Tokenizers 0.19.1