This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.1827
Accuracy: 0.9516

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: 8
eval_batch_size: 8
seed: 1337
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 150.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2639	1.0	2575	0.2192	0.9461
0.2457	2.0	5150	0.2065	0.9464
0.3157	3.0	7725	0.1827	0.9516
0.3149	4.0	10300	0.1855	0.9488
0.1212	5.0	12875	0.2079	0.9480
0.078	6.0	15450	0.2008	0.9516
0.3493	7.0	18025	0.2038	0.9497
0.131	8.0	20600	0.2059	0.9510
0.2658	9.0	23175	0.2089	0.9510
0.0762	10.0	25750	0.2068	0.9541
0.127	11.0	28325	0.1986	0.9543
0.181	12.0	30900	0.2227	0.9513
0.1072	13.0	33475	0.2303	0.9502
0.0179	14.0	36050	0.2240	0.9483
0.1447	15.0	38625	0.2364	0.9505
0.0933	16.0	41200	0.2372	0.9532
0.17	17.0	43775	0.2166	0.9557
0.0463	18.0	46350	0.2852	0.9461
0.1207	19.0	48925	0.2653	0.9508
0.1761	20.0	51500	0.2443	0.9521
0.1441	21.0	54075	0.2464	0.9535
0.1279	22.0	56650	0.2681	0.9499
0.1811	23.0	59225	0.2626	0.9538
0.1737	24.0	61800	0.2604	0.9541
0.0275	25.0	64375	0.2625	0.9510
0.1757	26.0	66950	0.2819	0.9488
0.1257	27.0	69525	0.2708	0.9521
0.1097	28.0	72100	0.2801	0.9519
0.0772	29.0	74675	0.2870	0.9499
0.132	30.0	77250	0.2824	0.9497
0.0652	31.0	79825	0.2628	0.9538
0.0324	32.0	82400	0.3223	0.9453
0.1774	33.0	84975	0.2749	0.9549
0.1178	34.0	87550	0.2905	0.9513
0.0804	35.0	90125	0.3100	0.9480
0.0617	36.0	92700	0.3131	0.9475
0.0348	37.0	95275	0.3341	0.9486
0.0057	38.0	97850	0.3225	0.9466
0.0409	39.0	100425	0.3206	0.9483
0.1052	40.0	103000	0.3212	0.9494
0.0943	41.0	105575	0.3075	0.9508
0.0018	42.0	108150	0.3062	0.9519
0.0287	43.0	110725	0.3224	0.9469
0.0384	44.0	113300	0.3086	0.9488
0.1214	45.0	115875	0.3145	0.9494
0.1735	46.0	118450	0.3191	0.9494
0.0477	47.0	121025	0.3004	0.9521
0.0221	48.0	123600	0.3205	0.9480
0.0939	49.0	126175	0.3431	0.9486
0.0599	50.0	128750	0.3167	0.9516
0.1785	51.0	131325	0.3274	0.9513
0.1039	52.0	133900	0.3114	0.9519
0.0527	53.0	136475	0.3252	0.9477
0.0584	54.0	139050	0.3200	0.9510
0.1022	55.0	141625	0.3284	0.9491
0.013	56.0	144200	0.3386	0.9475
0.0488	57.0	146775	0.3290	0.9505
0.0514	58.0	149350	0.3126	0.9535
0.0184	59.0	151925	0.3196	0.9532
0.1233	60.0	154500	0.3270	0.9516
0.1667	61.0	157075	0.3250	0.9502
0.0497	62.0	159650	0.3375	0.9466
0.0445	63.0	162225	0.3493	0.9502
0.114	64.0	164800	0.3368	0.9488
0.048	65.0	167375	0.3358	0.9510
0.2337	66.0	169950	0.3330	0.9510
0.0705	67.0	172525	0.3480	0.9510
0.094	68.0	175100	0.3508	0.9497
0.0498	69.0	177675	0.3328	0.9508
0.0535	70.0	180250	0.3558	0.9499
0.0217	71.0	182825	0.3583	0.9488
0.0264	72.0	185400	0.3600	0.9477
0.0108	73.0	187975	0.3629	0.9491
0.0446	74.0	190550	0.3570	0.9508
0.0702	75.0	193125	0.3600	0.9502
0.141	76.0	195700	0.3428	0.9527
0.0226	77.0	198275	0.3594	0.9502
0.0055	78.0	200850	0.3653	0.9508
0.1442	79.0	203425	0.3437	0.9530
0.0834	80.0	206000	0.3431	0.9524
0.0388	81.0	208575	0.3426	0.9521
0.0321	82.0	211150	0.3555	0.9497
0.051	83.0	213725	0.3730	0.9505
0.0049	84.0	216300	0.3549	0.9527
0.043	85.0	218875	0.3592	0.9524
0.0284	86.0	221450	0.3749	0.9499
0.0923	87.0	224025	0.3527	0.9513
0.1188	88.0	226600	0.3725	0.9486
0.1493	89.0	229175	0.3560	0.9521
0.0164	90.0	231750	0.3573	0.9508
0.0477	91.0	234325	0.3679	0.9502
0.0827	92.0	236900	0.3683	0.9486
0.0799	93.0	239475	0.3667	0.9510
0.0413	94.0	242050	0.3604	0.9516
0.071	95.0	244625	0.3725	0.9483
0.2079	96.0	247200	0.3688	0.9483
0.0665	97.0	249775	0.3576	0.9521
0.0673	98.0	252350	0.3636	0.9513
0.062	99.0	254925	0.3688	0.9513
0.1217	100.0	257500	0.3742	0.9508
0.0951	101.0	260075	0.3718	0.9491
0.0118	102.0	262650	0.3849	0.9491
0.0307	103.0	265225	0.3644	0.9535
0.0157	104.0	267800	0.3647	0.9524
0.0125	105.0	270375	0.3994	0.9486
0.0213	106.0	272950	0.3775	0.9499
0.1249	107.0	275525	0.3902	0.9491
0.0333	108.0	278100	0.3637	0.9516
0.0545	109.0	280675	0.3663	0.9521
0.1136	110.0	283250	0.3847	0.9502
0.0751	111.0	285825	0.3818	0.9513
0.001	112.0	288400	0.3811	0.9521
0.0282	113.0	290975	0.3843	0.9510
0.1117	114.0	293550	0.3790	0.9521
0.0022	115.0	296125	0.3717	0.9521
0.0203	116.0	298700	0.3794	0.9530
0.0437	117.0	301275	0.3807	0.9527
0.0045	118.0	303850	0.3821	0.9530
0.0015	119.0	306425	0.3867	0.9527
0.1152	120.0	309000	0.3842	0.9521
0.0748	121.0	311575	0.3839	0.9527
0.0955	122.0	314150	0.3805	0.9516
0.0043	123.0	316725	0.3833	0.9521
0.0249	124.0	319300	0.3745	0.9497
0.0002	125.0	321875	0.3744	0.9519
0.0169	126.0	324450	0.3808	0.9510
0.0277	127.0	327025	0.3735	0.9524
0.0082	128.0	329600	0.3831	0.9527
0.0737	129.0	332175	0.3891	0.9524
0.0517	130.0	334750	0.3839	0.9530
0.0218	131.0	337325	0.3863	0.9527
0.0228	132.0	339900	0.3913	0.9519
0.0094	133.0	342475	0.3968	0.9513
0.0784	134.0	345050	0.3871	0.9532
0.0116	135.0	347625	0.3890	0.9538
0.015	136.0	350200	0.3846	0.9530
0.0307	137.0	352775	0.3850	0.9530
0.0081	138.0	355350	0.3852	0.9532
0.0705	139.0	357925	0.3859	0.9527
0.0442	140.0	360500	0.3871	0.9524
0.0888	141.0	363075	0.3851	0.9535
0.0169	142.0	365650	0.3908	0.9527
0.0132	143.0	368225	0.3923	0.9527
0.0349	144.0	370800	0.3880	0.9527
0.0014	145.0	373375	0.3875	0.9535
0.0495	146.0	375950	0.3898	0.9535
0.0006	147.0	378525	0.3908	0.9530
0.0226	148.0	381100	0.3899	0.9527
0.0927	149.0	383675	0.3895	0.9527
0.081	150.0	386250	0.3896	0.9527

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.1+cu121
Datasets 2.15.0
Tokenizers 0.15.0

bortle/astrophotography-object-classifier-alpha5

astrophotography-object-classifier-alpha5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

bortle/
astrophotography-object-classifier-alpha5