Swin-dmae-DA3-N-Colab

This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 1.6604
• Accuracy: 0.7826

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: 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
1.4177	0.98	22	1.3455	0.4348
1.4043	2.0	45	1.3475	0.4565
1.3628	2.98	67	1.3397	0.4565
1.21	4.0	90	1.2593	0.4565
1.0504	4.98	112	1.1194	0.4130
0.9129	6.0	135	1.0522	0.4130
0.7811	6.98	157	1.1184	0.4348
0.6572	8.0	180	0.9951	0.5870
0.5207	8.98	202	0.9055	0.6522
0.6234	10.0	225	0.9277	0.6087
0.4721	10.98	247	0.8458	0.6739
0.3944	12.0	270	0.8837	0.6522
0.3572	12.98	292	0.8719	0.7174
0.2911	14.0	315	1.0367	0.6304
0.3224	14.98	337	0.9641	0.6087
0.2663	16.0	360	1.3670	0.5870
0.2132	16.98	382	1.3090	0.6304
0.266	18.0	405	1.1247	0.7174
0.1929	18.98	427	1.1458	0.6087
0.1831	20.0	450	1.0267	0.7391
0.2298	20.98	472	1.1863	0.6304
0.1825	22.0	495	1.0458	0.6957
0.1701	22.98	517	1.3520	0.6087
0.1964	24.0	540	1.3927	0.6522
0.1731	24.98	562	1.4361	0.6522
0.1565	26.0	585	1.0449	0.6957
0.1844	26.98	607	1.3166	0.6087
0.1187	28.0	630	1.7950	0.6304
0.129	28.98	652	1.2753	0.6957
0.1269	30.0	675	1.4244	0.6739
0.1522	30.98	697	1.4873	0.6522
0.1497	32.0	720	1.3693	0.6739
0.1215	32.98	742	1.8168	0.6739
0.1049	34.0	765	1.2749	0.7609
0.1013	34.98	787	1.5098	0.7609
0.1499	36.0	810	1.6464	0.6304
0.0823	36.98	832	1.7892	0.6957
0.092	38.0	855	1.6448	0.6739
0.1076	38.98	877	1.6955	0.6304
0.1163	40.0	900	1.5780	0.6739
0.0952	40.98	922	1.8121	0.6522
0.0833	42.0	945	1.4459	0.7174
0.1045	42.98	967	1.7307	0.6739
0.094	44.0	990	1.4970	0.7391
0.092	44.98	1012	1.5766	0.7174
0.0863	46.0	1035	1.7600	0.6522
0.101	46.98	1057	1.4763	0.6957
0.0995	48.0	1080	2.0018	0.6522
0.0893	48.98	1102	1.4872	0.7391
0.0965	50.0	1125	1.6165	0.7391
0.0595	50.98	1147	1.6608	0.7391
0.0606	52.0	1170	1.6604	0.7826
0.0794	52.98	1192	1.9967	0.7174
0.0919	54.0	1215	1.7728	0.6957
0.0666	54.98	1237	1.7364	0.7391
0.0842	56.0	1260	1.6661	0.7609
0.0781	56.98	1282	1.9340	0.7174
0.0565	58.0	1305	1.7399	0.7391
0.0939	58.98	1327	1.6644	0.7609
0.0666	60.0	1350	1.6804	0.6957
0.0577	60.98	1372	1.8968	0.6957
0.0534	62.0	1395	1.8967	0.7391
0.0592	62.98	1417	2.0113	0.7174
0.0732	64.0	1440	1.9213	0.6522
0.0768	64.98	1462	1.8912	0.7391
0.0415	66.0	1485	1.7955	0.7391
0.036	66.98	1507	1.6584	0.7391
0.0617	68.0	1530	1.9461	0.7391
0.0622	68.98	1552	1.7302	0.7826
0.0362	70.0	1575	1.7996	0.7609
0.0526	70.98	1597	1.6479	0.7391
0.0493	72.0	1620	1.7251	0.6739
0.0703	72.98	1642	2.0378	0.7391
0.0692	74.0	1665	2.0999	0.6522
0.0396	74.98	1687	2.0074	0.6739
0.0505	76.0	1710	1.7463	0.7174
0.0512	76.98	1732	1.6401	0.7609
0.0653	78.0	1755	1.7836	0.6957
0.0764	78.98	1777	1.7904	0.7609
0.0598	80.0	1800	1.8720	0.7609
0.0523	80.98	1822	1.7356	0.7174
0.0499	82.0	1845	1.9223	0.7609
0.0841	82.98	1867	1.9060	0.7609
0.0597	84.0	1890	1.9092	0.7174
0.0696	84.98	1912	1.9534	0.6957
0.0566	86.0	1935	1.9356	0.7609
0.0435	86.98	1957	2.0566	0.7391
0.024	88.0	1980	1.8717	0.7174
0.0137	88.98	2002	2.0280	0.7174
0.0663	90.0	2025	1.8829	0.7174
0.035	90.98	2047	1.9688	0.7174
0.0504	92.0	2070	2.0456	0.7391
0.025	92.98	2092	2.1568	0.6304
0.0405	94.0	2115	2.0628	0.6304
0.0247	94.98	2137	2.0686	0.6957
0.0429	96.0	2160	2.1125	0.7174
0.0408	96.98	2182	2.1003	0.7391
0.0385	98.0	2205	2.2997	0.6739
0.0364	98.98	2227	2.1442	0.7174
0.0415	100.0	2250	2.1043	0.7174
0.0175	100.98	2272	2.1847	0.6739
0.0281	102.0	2295	2.3262	0.6522
0.0268	102.98	2317	2.2843	0.6957
0.022	104.0	2340	2.3522	0.6957
0.0279	104.98	2362	2.4827	0.6739
0.0188	106.0	2385	2.5688	0.6522
0.0303	106.98	2407	2.4357	0.6522
0.0439	108.0	2430	2.4359	0.6522
0.0422	108.98	2452	2.4725	0.6739
0.032	110.0	2475	2.2899	0.6522
0.0414	110.98	2497	2.2685	0.6957
0.03	112.0	2520	2.3063	0.6739
0.0293	112.98	2542	2.3524	0.6739
0.0514	114.0	2565	2.3612	0.6739
0.0234	114.98	2587	2.3711	0.6522
0.0476	116.0	2610	2.3548	0.6739
0.0307	116.98	2632	2.3432	0.6739
0.028	117.33	2640	2.3432	0.6739

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.2+cu118
• Datasets 2.16.1
• Tokenizers 0.15.0