swinModel

This model is a fine-tuned version of facebook/convnext-tiny-224 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.4645
• Accuracy: 0.7823

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: 2e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5524	0.2278	100	0.3380	0.9845
0.4727	0.4556	200	0.3134	0.9439
0.3821	0.6834	300	0.3179	0.8939
0.2765	0.9112	400	0.3308	0.8603
0.1905	1.1390	500	0.4489	0.8069
0.1258	1.3667	600	0.5830	0.7731
0.0846	1.5945	700	0.4515	0.8439
0.064	1.8223	800	0.5274	0.8248
0.0494	2.0501	900	0.6575	0.7969
0.0378	2.2779	1000	0.6267	0.8261
0.0284	2.5057	1100	0.8875	0.7677
0.023	2.7335	1200	1.0218	0.7502
0.0225	2.9613	1300	0.8597	0.7930
0.0158	3.1891	1400	0.9559	0.7875
0.0134	3.4169	1500	0.7133	0.8378
0.0146	3.6446	1600	0.8297	0.8159
0.0116	3.8724	1700	0.9716	0.7930
0.0099	4.1002	1800	0.8118	0.8289
0.009	4.3280	1900	0.8361	0.8305
0.0059	4.5558	2000	0.9536	0.8127
0.009	4.7836	2100	1.0436	0.8003
0.0107	5.0114	2200	1.0988	0.7929
0.0077	5.2392	2300	0.9100	0.8344
0.007	5.4670	2400	0.9920	0.8186
0.0037	5.6948	2500	1.0256	0.8130
0.0073	5.9226	2600	1.5456	0.7387
0.0055	6.1503	2700	1.2020	0.7793
0.0039	6.3781	2800	1.1095	0.8048
0.0022	6.6059	2900	1.2638	0.7887
0.0042	6.8337	3000	1.0389	0.8263
0.005	7.0615	3100	1.3570	0.7763
0.0017	7.2893	3200	1.6866	0.7303
0.0024	7.5171	3300	1.4244	0.7679
0.0036	7.7449	3400	1.4379	0.7609
0.0032	7.9727	3500	1.1855	0.8006
0.0016	8.2005	3600	1.1089	0.8163
0.0023	8.4282	3700	0.9546	0.8441
0.0022	8.6560	3800	1.0083	0.8378
0.002	8.8838	3900	1.6526	0.7368
0.0032	9.1116	4000	1.5307	0.7619
0.0008	9.3394	4100	1.1384	0.8191
0.002	9.5672	4200	1.2104	0.8063
0.0031	9.7950	4300	1.5793	0.7564
0.0024	10.0228	4400	1.3544	0.7857
0.0035	10.2506	4500	1.5046	0.7667
0.0009	10.4784	4600	1.8010	0.7306
0.0007	10.7062	4700	1.2062	0.8115
0.0025	10.9339	4800	1.2110	0.8127
0.0016	11.1617	4900	1.3772	0.7875
0.001	11.3895	5000	1.3586	0.7947
0.0024	11.6173	5100	1.2359	0.8094
0.0012	11.8451	5200	0.8793	0.8679
0.0011	12.0729	5300	1.5563	0.7648
0.0021	12.3007	5400	1.3154	0.8003
0.0018	12.5285	5500	1.2115	0.8168
0.001	12.7563	5600	1.4905	0.7773
0.0012	12.9841	5700	1.4290	0.7868
0.0022	13.2118	5800	1.1928	0.8214
0.0023	13.4396	5900	1.2761	0.8077
0.0014	13.6674	6000	1.1804	0.8211
0.0021	13.8952	6100	1.3523	0.7965
0.0007	14.1230	6200	1.2330	0.8128
0.0008	14.3508	6300	1.3563	0.7955
0.0004	14.5786	6400	1.3969	0.7903
0.0011	14.8064	6500	1.4645	0.7823

Framework versions

• Transformers 4.42.0.dev0
• Pytorch 2.1.1
• Datasets 2.19.2
• Tokenizers 0.19.1