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jialicheng/
cifar100_swin-tiny

Name: cifar100_swin-tiny
Availability: InStock

generated_from_trainer

microsoft/swin-tiny-patch4-window7-224

finetune

apache-2.0

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swin-tiny

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the cifar100 dataset. It achieves the following results on the evaluation set:

Loss: 0.5505
Accuracy: 0.8646

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: 1e-05
train_batch_size: 128
eval_batch_size: 256
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 300

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.8188	1.0	333	2.4232	0.4372
2.0411	2.0	666	1.4235	0.6269
1.7069	3.0	999	1.0558	0.7102
1.5722	4.0	1332	0.8657	0.7504
1.346	5.0	1665	0.7774	0.7721
1.303	6.0	1998	0.7138	0.7874
1.2045	7.0	2331	0.6616	0.7986
1.2482	8.0	2664	0.6210	0.8128
1.1202	9.0	2997	0.5925	0.8185
1.0021	10.0	3330	0.5728	0.8235
1.0662	11.0	3663	0.5637	0.829
1.0263	12.0	3996	0.5442	0.8303
1.0581	13.0	4329	0.5319	0.8379
0.9922	14.0	4662	0.5215	0.8388
0.9643	15.0	4995	0.5144	0.8399
0.9687	16.0	5328	0.5103	0.8413
0.9464	17.0	5661	0.5021	0.8422
0.8651	18.0	5994	0.4867	0.8483
0.8122	19.0	6327	0.4865	0.8457
0.7918	20.0	6660	0.4877	0.8486
0.8994	21.0	6993	0.4836	0.8502
0.8661	22.0	7326	0.4736	0.8538
0.869	23.0	7659	0.4703	0.8528
0.8681	24.0	7992	0.4798	0.8513
0.7693	25.0	8325	0.4680	0.8523
0.8693	26.0	8658	0.4646	0.8579
0.8041	27.0	8991	0.4686	0.8555
0.8036	28.0	9324	0.4609	0.8578
0.7571	29.0	9657	0.4597	0.8616
0.7666	30.0	9990	0.4581	0.8606
0.7226	31.0	10323	0.4569	0.8601
0.7179	32.0	10656	0.4573	0.8628
0.6866	33.0	10989	0.4567	0.8606
0.7002	34.0	11322	0.4672	0.8576
0.7499	35.0	11655	0.4624	0.8611
0.7393	36.0	11988	0.4579	0.8604
0.7393	37.0	12321	0.4560	0.8619
0.7599	38.0	12654	0.4503	0.8637
0.6636	39.0	12987	0.4542	0.8636
0.6759	40.0	13320	0.4483	0.8631
0.7266	41.0	13653	0.4484	0.8636
0.6819	42.0	13986	0.4453	0.8647
0.5912	43.0	14319	0.4493	0.864
0.6803	44.0	14652	0.4453	0.8646
0.6898	45.0	14985	0.4458	0.8628
0.6312	46.0	15318	0.4499	0.8636
0.6972	47.0	15651	0.4494	0.8646
0.616	48.0	15984	0.4525	0.8674
0.6911	49.0	16317	0.4506	0.8637
0.6737	50.0	16650	0.4504	0.8648
0.5573	51.0	16983	0.4542	0.8641
0.6296	52.0	17316	0.4573	0.8626
0.6245	53.0	17649	0.4550	0.8647
0.6018	54.0	17982	0.4509	0.8668
0.6068	55.0	18315	0.4561	0.865
0.6368	56.0	18648	0.4533	0.8666
0.5945	57.0	18981	0.4537	0.8646
0.5379	58.0	19314	0.4583	0.8644
0.6031	59.0	19647	0.4574	0.8647
0.5445	60.0	19980	0.4607	0.8629
0.5589	61.0	20313	0.4619	0.8649
0.5777	62.0	20646	0.4740	0.8626
0.5711	63.0	20979	0.4684	0.8659
0.5369	64.0	21312	0.4655	0.8639
0.5454	65.0	21645	0.4574	0.867
0.5471	66.0	21978	0.4579	0.8655
0.5816	67.0	22311	0.4610	0.8662
0.5262	68.0	22644	0.4631	0.8646
0.5163	69.0	22977	0.4532	0.8677
0.5231	70.0	23310	0.4635	0.867
0.5672	71.0	23643	0.4626	0.8668
0.501	72.0	23976	0.4601	0.8677
0.527	73.0	24309	0.4661	0.8644
0.5618	74.0	24642	0.4677	0.8664
0.5161	75.0	24975	0.4630	0.8691
0.5158	76.0	25308	0.4691	0.8671
0.54	77.0	25641	0.4645	0.8696
0.5352	78.0	25974	0.4805	0.8649
0.5433	79.0	26307	0.4696	0.867
0.5555	80.0	26640	0.4745	0.8657
0.5248	81.0	26973	0.4767	0.8655
0.4648	82.0	27306	0.4730	0.8681
0.5853	83.0	27639	0.4781	0.8656
0.5298	84.0	27972	0.4729	0.869
0.4484	85.0	28305	0.4741	0.869
0.4765	86.0	28638	0.4877	0.8633
0.5409	87.0	28971	0.4807	0.8664
0.4778	88.0	29304	0.4753	0.8677
0.508	89.0	29637	0.4750	0.867
0.4567	90.0	29970	0.4816	0.8681
0.4828	91.0	30303	0.4806	0.8659
0.4357	92.0	30636	0.4770	0.8676
0.5117	93.0	30969	0.4741	0.8714
0.4756	94.0	31302	0.4860	0.8639
0.4575	95.0	31635	0.4855	0.8652
0.4657	96.0	31968	0.4828	0.8677
0.4746	97.0	32301	0.4850	0.8676
0.5466	98.0	32634	0.4890	0.8662
0.49	99.0	32967	0.4879	0.8663
0.4886	100.0	33300	0.4859	0.869
0.4763	101.0	33633	0.4840	0.868
0.5143	102.0	33966	0.4940	0.8673
0.4732	103.0	34299	0.4827	0.8699
0.481	104.0	34632	0.4891	0.8686
0.5015	105.0	34965	0.5004	0.8651
0.4596	106.0	35298	0.4950	0.8669
0.4201	107.0	35631	0.4920	0.866
0.4358	108.0	35964	0.4954	0.8643
0.4588	109.0	36297	0.4923	0.8649
0.4681	110.0	36630	0.4948	0.8654
0.4602	111.0	36963	0.4961	0.8677
0.4871	112.0	37296	0.5005	0.8634
0.4144	113.0	37629	0.4988	0.8657
0.4735	114.0	37962	0.4976	0.8654
0.4621	115.0	38295	0.4937	0.867
0.467	116.0	38628	0.4961	0.8671
0.4328	117.0	38961	0.4987	0.8662
0.3697	118.0	39294	0.4968	0.8667
0.4668	119.0	39627	0.5020	0.8655
0.4095	120.0	39960	0.4992	0.8674
0.4019	121.0	40293	0.5088	0.864
0.3627	122.0	40626	0.5078	0.8658
0.3875	123.0	40959	0.5079	0.8656
0.4696	124.0	41292	0.5006	0.8653
0.4071	125.0	41625	0.5089	0.8638
0.4485	126.0	41958	0.5067	0.8636
0.4565	127.0	42291	0.5060	0.8644
0.4633	128.0	42624	0.5130	0.8637
0.4259	129.0	42957	0.5053	0.867
0.4668	130.0	43290	0.5131	0.8647
0.4916	131.0	43623	0.5055	0.8656
0.4068	132.0	43956	0.5117	0.8669
0.4187	133.0	44289	0.5151	0.8639
0.4197	134.0	44622	0.5068	0.8685
0.3916	135.0	44955	0.5028	0.8684
0.4084	136.0	45288	0.5097	0.8662
0.405	137.0	45621	0.5061	0.8664
0.3752	138.0	45954	0.5129	0.8656
0.4338	139.0	46287	0.5147	0.8654
0.4865	140.0	46620	0.5171	0.8638
0.4771	141.0	46953	0.5166	0.865
0.4824	142.0	47286	0.5209	0.8645
0.4026	143.0	47619	0.5266	0.8649
0.436	144.0	47952	0.5200	0.8658
0.3487	145.0	48285	0.5185	0.8644
0.3615	146.0	48618	0.5202	0.8638
0.4317	147.0	48951	0.5198	0.8658
0.4171	148.0	49284	0.5236	0.8648
0.3833	149.0	49617	0.5228	0.865
0.3934	150.0	49950	0.5223	0.8649
0.4226	151.0	50283	0.5184	0.8657
0.4112	152.0	50616	0.5187	0.8644
0.4202	153.0	50949	0.5191	0.8648
0.4026	154.0	51282	0.5165	0.8669
0.4322	155.0	51615	0.5215	0.8649
0.3763	156.0	51948	0.5235	0.8659
0.4191	157.0	52281	0.5213	0.866
0.3864	158.0	52614	0.5225	0.8662
0.3974	159.0	52947	0.5248	0.8653
0.355	160.0	53280	0.5265	0.8626
0.3511	161.0	53613	0.5227	0.8665
0.3945	162.0	53946	0.5201	0.8662
0.3869	163.0	54279	0.5280	0.8633
0.4148	164.0	54612	0.5258	0.8649
0.3829	165.0	54945	0.5282	0.8652
0.3415	166.0	55278	0.5249	0.8654
0.3599	167.0	55611	0.5252	0.8648
0.3705	168.0	55944	0.5301	0.8645
0.4122	169.0	56277	0.5358	0.8636
0.3473	170.0	56610	0.5298	0.8646
0.3825	171.0	56943	0.5256	0.8643
0.3841	172.0	57276	0.5229	0.8668
0.3543	173.0	57609	0.5270	0.8646
0.4086	174.0	57942	0.5240	0.8656
0.3832	175.0	58275	0.5280	0.8631
0.3515	176.0	58608	0.5302	0.8645
0.3749	177.0	58941	0.5316	0.8645
0.3298	178.0	59274	0.5290	0.8647
0.3758	179.0	59607	0.5272	0.8668
0.31	180.0	59940	0.5314	0.864
0.3521	181.0	60273	0.5259	0.8648
0.3922	182.0	60606	0.5316	0.8638
0.3391	183.0	60939	0.5316	0.8648
0.3646	184.0	61272	0.5329	0.8637
0.4033	185.0	61605	0.5357	0.8662
0.395	186.0	61938	0.5376	0.8634
0.3253	187.0	62271	0.5346	0.8647
0.416	188.0	62604	0.5357	0.8621
0.3494	189.0	62937	0.5332	0.864
0.4009	190.0	63270	0.5364	0.8639
0.3935	191.0	63603	0.5329	0.8668
0.3666	192.0	63936	0.5337	0.8641
0.3474	193.0	64269	0.5321	0.866
0.3873	194.0	64602	0.5336	0.8635
0.3722	195.0	64935	0.5319	0.8645
0.3525	196.0	65268	0.5347	0.8636
0.3561	197.0	65601	0.5407	0.8629
0.3946	198.0	65934	0.5361	0.8643
0.3768	199.0	66267	0.5387	0.8639
0.3328	200.0	66600	0.5325	0.8656
0.3418	201.0	66933	0.5306	0.8676
0.3542	202.0	67266	0.5321	0.8648
0.3688	203.0	67599	0.5430	0.8598
0.3685	204.0	67932	0.5405	0.8629
0.3252	205.0	68265	0.5411	0.8628
0.358	206.0	68598	0.5403	0.8621
0.3086	207.0	68931	0.5399	0.8626
0.3774	208.0	69264	0.5390	0.8628
0.3449	209.0	69597	0.5388	0.865
0.3268	210.0	69930	0.5363	0.8645
0.3549	211.0	70263	0.5437	0.8634
0.3296	212.0	70596	0.5486	0.8627
0.3461	213.0	70929	0.5414	0.8638
0.3292	214.0	71262	0.5445	0.864
0.3622	215.0	71595	0.5438	0.8626
0.3724	216.0	71928	0.5359	0.8665
0.3352	217.0	72261	0.5410	0.8658
0.3484	218.0	72594	0.5407	0.8638
0.3109	219.0	72927	0.5404	0.8653
0.3703	220.0	73260	0.5471	0.8641
0.3318	221.0	73593	0.5432	0.8638
0.3573	222.0	73926	0.5473	0.8631
0.3308	223.0	74259	0.5448	0.8663
0.3329	224.0	74592	0.5445	0.8635
0.3429	225.0	74925	0.5445	0.8631
0.3494	226.0	75258	0.5433	0.8632
0.327	227.0	75591	0.5457	0.8639
0.313	228.0	75924	0.5457	0.8651
0.3344	229.0	76257	0.5421	0.8649
0.2893	230.0	76590	0.5472	0.8645
0.3225	231.0	76923	0.5436	0.8651
0.3662	232.0	77256	0.5428	0.8654
0.3281	233.0	77589	0.5453	0.8654
0.3354	234.0	77922	0.5468	0.8648
0.3238	235.0	78255	0.5501	0.8638
0.292	236.0	78588	0.5419	0.8658
0.3863	237.0	78921	0.5445	0.8637
0.3368	238.0	79254	0.5451	0.8643
0.3011	239.0	79587	0.5459	0.8651
0.2977	240.0	79920	0.5476	0.8651
0.3695	241.0	80253	0.5412	0.8649
0.3683	242.0	80586	0.5449	0.865
0.2971	243.0	80919	0.5490	0.8658
0.3532	244.0	81252	0.5449	0.8666
0.3014	245.0	81585	0.5448	0.8657
0.4048	246.0	81918	0.5468	0.8654
0.3279	247.0	82251	0.5501	0.8661
0.3494	248.0	82584	0.5498	0.8661
0.3543	249.0	82917	0.5513	0.8652
0.3142	250.0	83250	0.5506	0.8645
0.3534	251.0	83583	0.5471	0.8657
0.3703	252.0	83916	0.5475	0.866
0.3331	253.0	84249	0.5462	0.8662
0.3349	254.0	84582	0.5467	0.8649
0.3737	255.0	84915	0.5496	0.8635
0.394	256.0	85248	0.5489	0.8643
0.3394	257.0	85581	0.5514	0.8649
0.2963	258.0	85914	0.5478	0.865
0.3298	259.0	86247	0.5483	0.8639
0.4112	260.0	86580	0.5491	0.8637
0.3627	261.0	86913	0.5482	0.8652
0.2939	262.0	87246	0.5480	0.8649
0.2827	263.0	87579	0.5501	0.8648
0.3001	264.0	87912	0.5491	0.8659
0.3106	265.0	88245	0.5486	0.8662
0.3416	266.0	88578	0.5509	0.8641
0.3277	267.0	88911	0.5526	0.8643
0.3304	268.0	89244	0.5483	0.8645
0.2967	269.0	89577	0.5485	0.8642
0.2956	270.0	89910	0.5506	0.8638
0.335	271.0	90243	0.5497	0.8636
0.3032	272.0	90576	0.5494	0.8642
0.2757	273.0	90909	0.5511	0.8635
0.372	274.0	91242	0.5519	0.8633
0.3262	275.0	91575	0.5502	0.8641
0.2771	276.0	91908	0.5511	0.8629
0.2907	277.0	92241	0.5512	0.8627
0.3239	278.0	92574	0.5513	0.8633
0.2725	279.0	92907	0.5492	0.8638
0.3243	280.0	93240	0.5505	0.8639
0.3407	281.0	93573	0.5508	0.8636
0.3188	282.0	93906	0.5515	0.8644
0.3627	283.0	94239	0.5528	0.8639
0.2879	284.0	94572	0.5518	0.8645
0.288	285.0	94905	0.5524	0.8633
0.3104	286.0	95238	0.5516	0.8645
0.2963	287.0	95571	0.5509	0.8647
0.348	288.0	95904	0.5511	0.8647
0.3232	289.0	96237	0.5504	0.8642
0.286	290.0	96570	0.5499	0.8644
0.2953	291.0	96903	0.5504	0.8638
0.3371	292.0	97236	0.5512	0.8645
0.3631	293.0	97569	0.5504	0.8646
0.3439	294.0	97902	0.5496	0.8648
0.3277	295.0	98235	0.5495	0.8648
0.3115	296.0	98568	0.5503	0.8648
0.3103	297.0	98901	0.5504	0.8643
0.2616	298.0	99234	0.5508	0.8648
0.3072	299.0	99567	0.5505	0.8646
0.3333	300.0	99900	0.5505	0.8646

Framework versions

Transformers 4.39.3
Pytorch 2.2.2+cu118
Datasets 2.18.0
Tokenizers 0.15.2

jialicheng/cifar100_swin-tiny

swin-tiny

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

jialicheng/
cifar100_swin-tiny