This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 5.6236
Accuracy: 0.4528

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: 200

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.1452	1.0	268	2.2034	0.2247
0.9905	2.0	536	0.9729	0.7126
0.5262	3.0	804	0.5282	0.8314
0.36	4.0	1072	0.5618	0.8337
0.305	5.0	1340	0.9210	0.6535
0.2669	6.0	1608	1.1776	0.6317
0.2663	7.0	1876	1.2129	0.6290
0.2207	8.0	2144	2.2039	0.4068
0.2178	9.0	2412	1.9747	0.4740
0.1822	10.0	2680	1.4390	0.5526
0.1691	11.0	2948	2.1020	0.3814
0.1731	12.0	3216	2.0999	0.4251
0.1705	13.0	3484	2.4643	0.3700
0.1286	14.0	3752	2.7902	0.4345
0.1511	15.0	4020	2.5151	0.4165
0.1403	16.0	4288	4.4323	0.3099
0.1562	17.0	4556	2.0293	0.5096
0.1233	18.0	4824	2.5863	0.4236
0.1293	19.0	5092	2.6533	0.4506
0.1268	20.0	5360	2.1429	0.4998
0.1464	21.0	5628	2.3014	0.5470
0.1507	22.0	5896	2.3857	0.4911
0.1285	23.0	6164	1.4228	0.6406
0.1364	24.0	6432	3.6147	0.4842
0.1209	25.0	6700	2.4210	0.4896
0.1321	26.0	6968	2.7809	0.5344
0.0944	27.0	7236	3.5598	0.4226
0.1013	28.0	7504	4.0793	0.3905
0.1243	29.0	7772	4.5733	0.3443
0.0962	30.0	8040	2.9494	0.4199
0.0974	31.0	8308	3.1012	0.4496
0.113	32.0	8576	3.9522	0.3764
0.1067	33.0	8844	1.9792	0.6053
0.095	34.0	9112	2.8795	0.5302
0.1015	35.0	9380	5.9943	0.2941
0.0912	36.0	9648	2.9536	0.5242
0.1193	37.0	9916	3.5187	0.4226
0.0906	38.0	10184	3.0049	0.5114
0.1109	39.0	10452	2.6823	0.5675
0.0903	40.0	10720	4.7151	0.3109
0.0846	41.0	10988	3.1118	0.3880
0.0986	42.0	11256	3.9827	0.4792
0.1244	43.0	11524	4.7544	0.2860
0.1039	44.0	11792	4.4297	0.3178
0.077	45.0	12060	5.8973	0.3524
0.0718	46.0	12328	6.0338	0.3033
0.0838	47.0	12596	6.3524	0.3507
0.0935	48.0	12864	3.8675	0.4194
0.0922	49.0	13132	4.7731	0.3129
0.0903	50.0	13400	3.5435	0.4115
0.0927	51.0	13668	4.7606	0.4234
0.0757	52.0	13936	3.4110	0.4436
0.0738	53.0	14204	6.3143	0.3648
0.076	54.0	14472	4.9524	0.3604
0.0951	55.0	14740	5.5633	0.3680
0.1078	56.0	15008	5.9219	0.3082
0.0991	57.0	15276	4.9457	0.3344
0.0968	58.0	15544	4.0270	0.4271
0.0883	59.0	15812	5.3006	0.3574
0.0728	60.0	16080	6.9527	0.3119
0.0803	61.0	16348	2.9117	0.5
0.1022	62.0	16616	5.1631	0.3487
0.1155	63.0	16884	5.2602	0.3453
0.0737	64.0	17152	6.5281	0.3129
0.0735	65.0	17420	4.9847	0.3945
0.0948	66.0	17688	3.6684	0.4330
0.0765	67.0	17956	4.2188	0.4076
0.0597	68.0	18224	3.0067	0.5208
0.0866	69.0	18492	3.8993	0.4412
0.0825	70.0	18760	3.9058	0.3945
0.0897	71.0	19028	4.5870	0.3932
0.0687	72.0	19296	4.2837	0.3744
0.0774	73.0	19564	4.9028	0.3596
0.0755	74.0	19832	5.1321	0.3356
0.0728	75.0	20100	4.5533	0.3851
0.0753	76.0	20368	4.9765	0.3898
0.0582	77.0	20636	5.1959	0.3777
0.0714	78.0	20904	4.6735	0.3707
0.0928	79.0	21172	3.6359	0.4639
0.0593	80.0	21440	5.1507	0.3841
0.0972	81.0	21708	5.3122	0.3356
0.0903	82.0	21976	3.5833	0.4310
0.074	83.0	22244	2.3014	0.6349
0.0651	84.0	22512	3.8229	0.4387
0.0682	85.0	22780	3.5292	0.4627
0.0543	86.0	23048	4.0542	0.4266
0.0776	87.0	23316	3.8799	0.5240
0.0868	88.0	23584	4.1896	0.4750
0.0711	89.0	23852	3.1013	0.5381
0.077	90.0	24120	2.9132	0.5650
0.0672	91.0	24388	4.4834	0.3806
0.0737	92.0	24656	4.0161	0.5116
0.0868	93.0	24924	2.9386	0.4956
0.0778	94.0	25192	4.4806	0.4478
0.0586	95.0	25460	5.0668	0.4313
0.0713	96.0	25728	6.4632	0.3043
0.0897	97.0	25996	5.0227	0.4674
0.073	98.0	26264	3.6177	0.4854
0.0775	99.0	26532	5.5003	0.3702
0.0709	100.0	26800	5.6101	0.3863
0.078	101.0	27068	4.3187	0.4338
0.0702	102.0	27336	4.8467	0.4545
0.0498	103.0	27604	3.9094	0.4511
0.0785	104.0	27872	4.0952	0.3836
0.0767	105.0	28140	3.2816	0.4909
0.0611	106.0	28408	5.2239	0.4221
0.0753	107.0	28676	4.2586	0.4493
0.0758	108.0	28944	3.6094	0.4938
0.0951	109.0	29212	6.1982	0.3453
0.086	110.0	29480	6.4891	0.3191
0.0701	111.0	29748	5.8145	0.3235
0.0772	112.0	30016	3.7809	0.5133
0.0705	113.0	30284	4.9590	0.4372
0.0602	114.0	30552	5.5669	0.3959
0.0671	115.0	30820	4.4897	0.4429
0.0692	116.0	31088	5.1358	0.3319
0.0675	117.0	31356	5.0169	0.4226
0.0626	118.0	31624	5.6420	0.4170
0.0537	119.0	31892	5.1601	0.3683
0.0543	120.0	32160	5.4460	0.3663
0.0601	121.0	32428	7.2877	0.2981
0.0743	122.0	32696	6.5134	0.3337
0.0558	123.0	32964	4.4690	0.4469
0.0396	124.0	33232	4.4964	0.4212
0.0704	125.0	33500	4.5766	0.4011
0.0547	126.0	33768	4.0679	0.4538
0.0643	127.0	34036	3.3335	0.4545
0.0709	128.0	34304	3.6568	0.4750
0.0932	129.0	34572	4.7978	0.4614
0.0522	130.0	34840	6.1548	0.3366
0.0592	131.0	35108	5.0728	0.4409
0.0528	132.0	35376	5.5127	0.4088
0.087	133.0	35644	4.5838	0.3900
0.0566	134.0	35912	4.8733	0.3683
0.0474	135.0	36180	3.4370	0.4348
0.0517	136.0	36448	4.5547	0.3908
0.0627	137.0	36716	4.7011	0.4048
0.0693	138.0	36984	4.8039	0.4419
0.0753	139.0	37252	4.6905	0.4674
0.0542	140.0	37520	4.4103	0.4278
0.0629	141.0	37788	4.5332	0.4402
0.0636	142.0	38056	4.4822	0.4288
0.0551	143.0	38324	5.3970	0.3885
0.0677	144.0	38592	4.9337	0.3811
0.037	145.0	38860	4.7588	0.3979
0.0426	146.0	39128	4.5055	0.4110
0.0624	147.0	39396	4.9575	0.3722
0.0799	148.0	39664	3.9235	0.4350
0.0643	149.0	39932	3.2063	0.5297
0.0687	150.0	40200	3.1733	0.5692
0.0652	151.0	40468	3.8738	0.5178
0.078	152.0	40736	2.7892	0.5319
0.0644	153.0	41004	3.4909	0.5185
0.0639	154.0	41272	3.7233	0.5005
0.0517	155.0	41540	4.9475	0.4152
0.0546	156.0	41808	5.0784	0.4251
0.0704	157.0	42076	5.3511	0.3987
0.0753	158.0	42344	5.0345	0.4538
0.0504	159.0	42612	4.1655	0.4701
0.0645	160.0	42880	3.9242	0.4936
0.0543	161.0	43148	4.8499	0.4533
0.0592	162.0	43416	5.1871	0.4345
0.0716	163.0	43684	5.4487	0.4325
0.0613	164.0	43952	4.3626	0.4711
0.0616	165.0	44220	4.8649	0.4807
0.0506	166.0	44488	4.1038	0.5133
0.0802	167.0	44756	5.0038	0.4889
0.0672	168.0	45024	6.3643	0.4009
0.0562	169.0	45292	6.1359	0.4372
0.0367	170.0	45560	5.6726	0.4340
0.0687	171.0	45828	5.2015	0.4254
0.061	172.0	46096	5.0398	0.4491
0.0444	173.0	46364	5.8819	0.4414
0.0685	174.0	46632	6.0729	0.4263
0.0548	175.0	46900	5.6388	0.4298
0.084	176.0	47168	6.3042	0.4090
0.0575	177.0	47436	6.3381	0.4019
0.0678	178.0	47704	6.3679	0.4100
0.0445	179.0	47972	6.3634	0.4152
0.081	180.0	48240	6.4057	0.4051
0.0643	181.0	48508	6.6593	0.3648
0.0497	182.0	48776	6.7469	0.3799
0.0568	183.0	49044	5.9056	0.4221
0.0513	184.0	49312	6.4656	0.4046
0.0496	185.0	49580	6.1444	0.4140
0.0524	186.0	49848	5.9295	0.4357
0.0746	187.0	50116	5.6245	0.4612
0.0489	188.0	50384	5.6278	0.4476
0.0589	189.0	50652	5.6629	0.4595
0.0365	190.0	50920	5.9882	0.4392
0.0456	191.0	51188	6.0186	0.4496
0.0486	192.0	51456	5.6916	0.4427
0.0658	193.0	51724	5.7638	0.4461
0.0599	194.0	51992	5.7886	0.4387
0.0522	195.0	52260	5.7112	0.4464
0.0556	196.0	52528	5.7411	0.4419
0.0681	197.0	52796	5.6449	0.4516
0.0649	198.0	53064	5.6714	0.4508
0.0582	199.0	53332	5.6241	0.4521
0.0727	200.0	53600	5.6236	0.4528

Framework versions

Transformers 4.20.1
Pytorch 1.12.1+cu102
Datasets 2.3.2
Tokenizers 0.12.1

ezzouhri/swin-tiny-patch4-window7-224-seg-swin-amal-finetuned-eurosat

swin-tiny-patch4-window7-224-seg-swin-amal-finetuned-eurosat

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ezzouhri/
swin-tiny-patch4-window7-224-seg-swin-amal-finetuned-eurosat