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jialicheng/
cifar100-swin-base

Name: cifar100-swin-base
Availability: InStock

generated_from_trainer

microsoft/swin-base-patch4-window7-224

finetune

apache-2.0

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swin-base

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

Loss: 0.3503
Accuracy: 0.9226

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
1.4475	1.0	333	0.8145	0.8024
1.1153	2.0	666	0.4367	0.8751
0.9219	3.0	999	0.3546	0.8946
0.8802	4.0	1332	0.3216	0.9027
0.7332	5.0	1665	0.3025	0.9069
0.6719	6.0	1998	0.2940	0.9098
0.6996	7.0	2331	0.2785	0.9141
0.7023	8.0	2664	0.2729	0.9165
0.6376	9.0	2997	0.2657	0.9194
0.571	10.0	3330	0.2617	0.9209
0.6006	11.0	3663	0.2636	0.9215
0.5471	12.0	3996	0.2517	0.9239
0.6324	13.0	4329	0.2520	0.9226
0.56	14.0	4662	0.2530	0.924
0.5821	15.0	4995	0.2569	0.9217
0.5203	16.0	5328	0.2476	0.9278
0.5387	17.0	5661	0.2518	0.9264
0.4921	18.0	5994	0.2475	0.9282
0.413	19.0	6327	0.2516	0.9271
0.4612	20.0	6660	0.2538	0.9242
0.4903	21.0	6993	0.2556	0.9262
0.4953	22.0	7326	0.2501	0.9271
0.4922	23.0	7659	0.2486	0.9277
0.4603	24.0	7992	0.2550	0.9234
0.4405	25.0	8325	0.2476	0.9285
0.4867	26.0	8658	0.2482	0.9295
0.4414	27.0	8991	0.2540	0.9267
0.4574	28.0	9324	0.2494	0.9287
0.4109	29.0	9657	0.2533	0.928
0.4433	30.0	9990	0.2571	0.9258
0.4034	31.0	10323	0.2543	0.9265
0.4203	32.0	10656	0.2587	0.9286
0.3942	33.0	10989	0.2555	0.927
0.3991	34.0	11322	0.2564	0.9271
0.4252	35.0	11655	0.2603	0.925
0.4393	36.0	11988	0.2574	0.9288
0.3949	37.0	12321	0.2638	0.9279
0.4458	38.0	12654	0.2582	0.9281
0.3999	39.0	12987	0.2581	0.928
0.3887	40.0	13320	0.2575	0.9301
0.4101	41.0	13653	0.2620	0.9278
0.3987	42.0	13986	0.2627	0.928
0.3514	43.0	14319	0.2660	0.9287
0.3916	44.0	14652	0.2670	0.9287
0.3798	45.0	14985	0.2696	0.9272
0.3352	46.0	15318	0.2726	0.9264
0.3703	47.0	15651	0.2781	0.926
0.3596	48.0	15984	0.2747	0.9274
0.385	49.0	16317	0.2769	0.9257
0.3716	50.0	16650	0.2769	0.9268
0.3305	51.0	16983	0.2756	0.9257
0.354	52.0	17316	0.2759	0.927
0.3543	53.0	17649	0.2825	0.927
0.3333	54.0	17982	0.2766	0.9277
0.3695	55.0	18315	0.2737	0.9293
0.3945	56.0	18648	0.2760	0.9282
0.3315	57.0	18981	0.2749	0.9288
0.3012	58.0	19314	0.2747	0.9283
0.3261	59.0	19647	0.2812	0.9283
0.2817	60.0	19980	0.2754	0.9285
0.3039	61.0	20313	0.2819	0.929
0.306	62.0	20646	0.2816	0.9293
0.3166	63.0	20979	0.2839	0.9265
0.3471	64.0	21312	0.2799	0.9289
0.2996	65.0	21645	0.2848	0.9259
0.3252	66.0	21978	0.2840	0.9278
0.324	67.0	22311	0.2853	0.9273
0.2911	68.0	22644	0.2839	0.9282
0.2852	69.0	22977	0.2892	0.927
0.3154	70.0	23310	0.2902	0.928
0.3402	71.0	23643	0.2874	0.9294
0.3122	72.0	23976	0.2909	0.9258
0.3276	73.0	24309	0.2856	0.9281
0.291	74.0	24642	0.2895	0.9257
0.2939	75.0	24975	0.2922	0.927
0.2853	76.0	25308	0.2956	0.9245
0.2863	77.0	25641	0.2900	0.9281
0.3417	78.0	25974	0.2902	0.927
0.3053	79.0	26307	0.2925	0.9261
0.2954	80.0	26640	0.2942	0.9257
0.2539	81.0	26973	0.3004	0.9252
0.2728	82.0	27306	0.2943	0.9252
0.3537	83.0	27639	0.3002	0.9243
0.2917	84.0	27972	0.2907	0.9259
0.26	85.0	28305	0.2947	0.9265
0.2604	86.0	28638	0.3004	0.9261
0.3287	87.0	28971	0.3003	0.9266
0.3101	88.0	29304	0.3001	0.9277
0.3029	89.0	29637	0.2987	0.9275
0.2824	90.0	29970	0.3001	0.9266
0.2771	91.0	30303	0.3026	0.9261
0.2428	92.0	30636	0.3052	0.9256
0.2784	93.0	30969	0.3012	0.9273
0.2397	94.0	31302	0.2990	0.9275
0.2789	95.0	31635	0.3009	0.9257
0.3029	96.0	31968	0.3023	0.9257
0.2966	97.0	32301	0.3007	0.9273
0.3114	98.0	32634	0.2945	0.9277
0.2892	99.0	32967	0.3028	0.9257
0.248	100.0	33300	0.2971	0.9284
0.3176	101.0	33633	0.3050	0.9264
0.3074	102.0	33966	0.3066	0.929
0.2901	103.0	34299	0.3037	0.9252
0.3027	104.0	34632	0.3039	0.9245
0.3048	105.0	34965	0.2983	0.9287
0.2573	106.0	35298	0.3004	0.928
0.2739	107.0	35631	0.3039	0.9263
0.2491	108.0	35964	0.3016	0.9277
0.2491	109.0	36297	0.3025	0.9272
0.291	110.0	36630	0.3018	0.9257
0.264	111.0	36963	0.3096	0.9255
0.2931	112.0	37296	0.3093	0.9282
0.2407	113.0	37629	0.3106	0.927
0.2583	114.0	37962	0.3116	0.9252
0.2628	115.0	38295	0.3068	0.9254
0.2806	116.0	38628	0.3114	0.9257
0.2441	117.0	38961	0.3072	0.9251
0.2204	118.0	39294	0.3125	0.9258
0.2819	119.0	39627	0.3178	0.9237
0.2466	120.0	39960	0.3189	0.9248
0.2284	121.0	40293	0.3107	0.9267
0.217	122.0	40626	0.3138	0.9263
0.2405	123.0	40959	0.3088	0.9275
0.2972	124.0	41292	0.3067	0.9255
0.246	125.0	41625	0.3120	0.9252
0.273	126.0	41958	0.3165	0.9247
0.2837	127.0	42291	0.3159	0.925
0.2741	128.0	42624	0.3169	0.9256
0.26	129.0	42957	0.3156	0.9251
0.2739	130.0	43290	0.3166	0.9257
0.3104	131.0	43623	0.3238	0.9237
0.264	132.0	43956	0.3164	0.9257
0.2485	133.0	44289	0.3236	0.9232
0.2637	134.0	44622	0.3232	0.9249
0.2211	135.0	44955	0.3191	0.9256
0.2498	136.0	45288	0.3190	0.9251
0.2331	137.0	45621	0.3226	0.9245
0.2247	138.0	45954	0.3241	0.9228
0.2555	139.0	46287	0.3269	0.9224
0.2255	140.0	46620	0.3229	0.9247
0.2909	141.0	46953	0.3212	0.9256
0.2902	142.0	47286	0.3215	0.9231
0.2384	143.0	47619	0.3296	0.9233
0.2538	144.0	47952	0.3270	0.9255
0.2174	145.0	48285	0.3265	0.9249
0.2274	146.0	48618	0.3302	0.9235
0.2354	147.0	48951	0.3300	0.9254
0.2555	148.0	49284	0.3265	0.9236
0.2389	149.0	49617	0.3329	0.9235
0.2441	150.0	49950	0.3222	0.924
0.2419	151.0	50283	0.3287	0.9235
0.2293	152.0	50616	0.3310	0.9221
0.2432	153.0	50949	0.3293	0.9237
0.2255	154.0	51282	0.3338	0.9257
0.2418	155.0	51615	0.3313	0.924
0.2254	156.0	51948	0.3326	0.9245
0.2549	157.0	52281	0.3365	0.924
0.213	158.0	52614	0.3250	0.9257
0.2178	159.0	52947	0.3259	0.9252
0.2127	160.0	53280	0.3294	0.9241
0.2063	161.0	53613	0.3301	0.9239
0.2401	162.0	53946	0.3306	0.9247
0.2198	163.0	54279	0.3323	0.9226
0.2642	164.0	54612	0.3324	0.9239
0.2477	165.0	54945	0.3314	0.9238
0.1936	166.0	55278	0.3324	0.9233
0.2151	167.0	55611	0.3356	0.9229
0.2049	168.0	55944	0.3355	0.9214
0.2356	169.0	56277	0.3369	0.9226
0.2092	170.0	56610	0.3306	0.923
0.2239	171.0	56943	0.3387	0.9237
0.2157	172.0	57276	0.3347	0.9222
0.1877	173.0	57609	0.3423	0.9224
0.2532	174.0	57942	0.3342	0.9231
0.2306	175.0	58275	0.3364	0.9223
0.2247	176.0	58608	0.3376	0.9219
0.2548	177.0	58941	0.3390	0.9217
0.1797	178.0	59274	0.3433	0.9214
0.235	179.0	59607	0.3388	0.9206
0.1707	180.0	59940	0.3376	0.9209
0.195	181.0	60273	0.3384	0.9224
0.2526	182.0	60606	0.3418	0.9215
0.2041	183.0	60939	0.3373	0.921
0.2251	184.0	61272	0.3409	0.922
0.2562	185.0	61605	0.3356	0.9239
0.2225	186.0	61938	0.3390	0.9228
0.1772	187.0	62271	0.3390	0.9219
0.2343	188.0	62604	0.3419	0.9219
0.2086	189.0	62937	0.3400	0.9222
0.3153	190.0	63270	0.3436	0.9203
0.2632	191.0	63603	0.3436	0.9226
0.2191	192.0	63936	0.3463	0.9218
0.1892	193.0	64269	0.3455	0.9226
0.2246	194.0	64602	0.3454	0.9215
0.2485	195.0	64935	0.3412	0.9224
0.2055	196.0	65268	0.3426	0.9209
0.2087	197.0	65601	0.3456	0.92
0.235	198.0	65934	0.3437	0.9218
0.2093	199.0	66267	0.3425	0.9231
0.1899	200.0	66600	0.3433	0.9229
0.231	201.0	66933	0.3435	0.9218
0.2002	202.0	67266	0.3430	0.9219
0.2062	203.0	67599	0.3435	0.921
0.2138	204.0	67932	0.3486	0.9214
0.1742	205.0	68265	0.3454	0.9224
0.2116	206.0	68598	0.3459	0.9212
0.1906	207.0	68931	0.3475	0.9202
0.2253	208.0	69264	0.3447	0.9227
0.1944	209.0	69597	0.3476	0.9209
0.2114	210.0	69930	0.3473	0.9211
0.1959	211.0	70263	0.3463	0.9213
0.1967	212.0	70596	0.3454	0.9235
0.221	213.0	70929	0.3500	0.9239
0.194	214.0	71262	0.3468	0.9231
0.2055	215.0	71595	0.3455	0.9221
0.216	216.0	71928	0.3482	0.9215
0.1887	217.0	72261	0.3495	0.9218
0.2043	218.0	72594	0.3458	0.9237
0.2022	219.0	72927	0.3428	0.9224
0.1834	220.0	73260	0.3419	0.9222
0.1835	221.0	73593	0.3447	0.9231
0.2194	222.0	73926	0.3472	0.9218
0.1775	223.0	74259	0.3466	0.922
0.1781	224.0	74592	0.3505	0.9233
0.2001	225.0	74925	0.3477	0.9226
0.185	226.0	75258	0.3469	0.9229
0.2079	227.0	75591	0.3465	0.9228
0.1709	228.0	75924	0.3485	0.9217
0.2041	229.0	76257	0.3476	0.9213
0.1793	230.0	76590	0.3505	0.9222
0.193	231.0	76923	0.3483	0.9234
0.2192	232.0	77256	0.3490	0.9213
0.2017	233.0	77589	0.3481	0.9219
0.1883	234.0	77922	0.3479	0.9218
0.1682	235.0	78255	0.3476	0.9214
0.1702	236.0	78588	0.3474	0.922
0.2109	237.0	78921	0.3499	0.9221
0.1768	238.0	79254	0.3459	0.9211
0.1731	239.0	79587	0.3480	0.9222
0.1834	240.0	79920	0.3479	0.9216
0.2182	241.0	80253	0.3484	0.9218
0.2084	242.0	80586	0.3515	0.9222
0.2006	243.0	80919	0.3499	0.9223
0.221	244.0	81252	0.3502	0.9223
0.1835	245.0	81585	0.3526	0.9212
0.2469	246.0	81918	0.3473	0.9215
0.1844	247.0	82251	0.3473	0.9228
0.1972	248.0	82584	0.3493	0.9213
0.1821	249.0	82917	0.3503	0.9212
0.2	250.0	83250	0.3518	0.9213
0.1888	251.0	83583	0.3509	0.9219
0.2034	252.0	83916	0.3488	0.9207
0.2062	253.0	84249	0.3464	0.9217
0.1906	254.0	84582	0.3480	0.9224
0.1996	255.0	84915	0.3481	0.9219
0.2447	256.0	85248	0.3485	0.9217
0.1975	257.0	85581	0.3509	0.9215
0.1787	258.0	85914	0.3497	0.9203
0.1599	259.0	86247	0.3530	0.9208
0.2455	260.0	86580	0.3507	0.9206
0.2159	261.0	86913	0.3510	0.9224
0.2032	262.0	87246	0.3502	0.9215
0.1453	263.0	87579	0.3501	0.9225
0.1922	264.0	87912	0.3494	0.9235
0.2038	265.0	88245	0.3481	0.9229
0.1897	266.0	88578	0.3492	0.923
0.1941	267.0	88911	0.3504	0.9237
0.197	268.0	89244	0.3504	0.923
0.1933	269.0	89577	0.3485	0.9227
0.1585	270.0	89910	0.3488	0.9237
0.1994	271.0	90243	0.3488	0.9223
0.1562	272.0	90576	0.3482	0.922
0.1804	273.0	90909	0.3487	0.9214
0.2202	274.0	91242	0.3509	0.9215
0.1804	275.0	91575	0.3502	0.9227
0.1542	276.0	91908	0.3496	0.9229
0.1744	277.0	92241	0.3486	0.9226
0.1779	278.0	92574	0.3483	0.9228
0.1396	279.0	92907	0.3495	0.9228
0.1501	280.0	93240	0.3484	0.9232
0.1808	281.0	93573	0.3503	0.9227
0.1749	282.0	93906	0.3492	0.9218
0.2295	283.0	94239	0.3493	0.9216
0.1695	284.0	94572	0.3491	0.9219
0.1859	285.0	94905	0.3502	0.9222
0.1891	286.0	95238	0.3505	0.9226
0.1681	287.0	95571	0.3513	0.9222
0.1837	288.0	95904	0.3512	0.9217
0.2181	289.0	96237	0.3512	0.9224
0.1637	290.0	96570	0.3514	0.9219
0.1808	291.0	96903	0.3511	0.922
0.1935	292.0	97236	0.3511	0.9226
0.2022	293.0	97569	0.3508	0.9229
0.1708	294.0	97902	0.3505	0.923
0.1924	295.0	98235	0.3508	0.9228
0.1775	296.0	98568	0.3511	0.9224
0.1681	297.0	98901	0.3509	0.9224
0.1528	298.0	99234	0.3504	0.9225
0.1978	299.0	99567	0.3503	0.9226
0.1826	300.0	99900	0.3503	0.9226

Framework versions

Transformers 4.39.3
Pytorch 2.2.2+cu118
Datasets 2.18.0
Tokenizers 0.15.2

jialicheng/cifar100-swin-base

swin-base

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

jialicheng/
cifar100-swin-base