vit-large-artifacts

KyriaAnnwyn/vit-large-artifacts

vit-large-artifacts is a image classification model from KyriaAnnwyn. Use it when you need a label for an image. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image Classification
Library: transformers
Model type: vit
Access: Public
Created: Jul 7, 2023
Updated: Jul 13, 2023
SHA: 6f4f8e9d

Model card

From the Hugging Face model README

vit-large-artifacts

This model is a fine-tuned version of kakaobrain/vit-large-patch16-512 on the KyriaAnnwyn/artifacts_ds dataset. It achieves the following results on the evaluation set:

Loss: 0.5995
Accuracy: 0.6705

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: 0.0002
train_batch_size: 4
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7001	0.01	100	0.6414	0.6559
0.6288	0.01	200	0.6666	0.6559
0.7237	0.02	300	0.7087	0.6559
0.8741	0.03	400	0.6739	0.6257
0.6093	0.04	500	0.6462	0.6559
0.5801	0.04	600	0.6822	0.6559
0.594	0.05	700	1.9948	0.6395
0.7724	0.06	800	0.6566	0.6553
0.6976	0.07	900	0.6774	0.6325
0.6583	0.07	1000	0.7175	0.3517
0.6779	0.08	1100	0.7012	0.6559
0.6478	0.09	1200	0.6336	0.6559
0.7405	0.1	1300	0.6577	0.6559
0.7362	0.1	1400	0.6630	0.6142
0.535	0.11	1500	0.7445	0.6559
0.7338	0.12	1600	0.7046	0.4718
0.6519	0.13	1700	0.6601	0.6426
0.5969	0.13	1800	0.6518	0.6559
0.5992	0.14	1900	0.6544	0.6559
0.5762	0.15	2000	0.6608	0.6559
0.6483	0.16	2100	0.6436	0.6331
0.7594	0.16	2200	0.7562	0.5213
0.6423	0.17	2300	0.6326	0.6433
0.7006	0.18	2400	0.6669	0.6108
0.833	0.19	2500	0.7043	0.6559
0.6133	0.19	2600	0.6356	0.6532
0.5285	0.2	2700	0.6619	0.6606
0.7209	0.21	2800	0.7306	0.4196
0.682	0.22	2900	0.6400	0.6539
0.7148	0.22	3000	0.6421	0.6559
0.6288	0.23	3100	0.7416	0.6559
0.666	0.24	3200	0.6368	0.6293
0.772	0.25	3300	0.6973	0.4985
0.6778	0.25	3400	0.6288	0.6604
0.5939	0.26	3500	0.6566	0.6559
0.6246	0.27	3600	0.6347	0.6618
0.649	0.28	3700	0.6353	0.6277
0.7122	0.28	3800	0.6407	0.6559
0.6292	0.29	3900	0.6776	0.6560
0.6079	0.3	4000	0.6220	0.6609
0.6971	0.31	4100	0.6258	0.6394
0.7131	0.31	4200	0.7202	0.6556
0.5346	0.32	4300	0.6394	0.6571
0.5801	0.33	4400	0.6960	0.6664
0.6806	0.34	4500	0.6339	0.6348
0.6245	0.34	4600	0.6226	0.6477
0.6905	0.35	4700	0.6203	0.6533
0.741	0.36	4800	0.6464	0.6680
0.5712	0.37	4900	0.6162	0.6640
0.5566	0.37	5000	0.6182	0.6507
0.6443	0.38	5100	0.6457	0.6664
0.6107	0.39	5200	0.6092	0.6617
0.5824	0.4	5300	0.6383	0.6571
0.4775	0.4	5400	0.6606	0.6621
0.7114	0.41	5500	0.6179	0.6619
0.7701	0.42	5600	0.7982	0.4217
0.6974	0.42	5700	0.6223	0.6540
0.6669	0.43	5800	0.6249	0.6559
0.6982	0.44	5900	0.6287	0.6564
0.5811	0.45	6000	0.6104	0.6506
0.4347	0.45	6100	1.0475	0.6559
0.5885	0.46	6200	0.6125	0.6552
0.6867	0.47	6300	0.6435	0.6468
0.6088	0.48	6400	0.6047	0.6623
0.8194	0.48	6500	0.6972	0.6589
0.8182	0.49	6600	0.6053	0.6644
0.6104	0.5	6700	0.7375	0.6571
0.5552	0.51	6800	0.6231	0.6402
0.6451	0.51	6900	0.6452	0.6561
0.7849	0.52	7000	0.6177	0.6612
0.64	0.53	7100	0.6307	0.6234
0.6393	0.54	7200	0.6130	0.6554
0.8326	0.54	7300	0.7210	0.6421
0.6579	0.55	7400	0.6227	0.6544
0.5195	0.56	7500	0.6619	0.6557
0.6197	0.57	7600	0.6354	0.6498
0.8507	0.57	7700	0.6820	0.6550
0.7163	0.58	7800	0.6720	0.5328
0.6896	0.59	7900	0.6530	0.6386
0.62	0.6	8000	0.6296	0.6559
0.8254	0.6	8100	0.6752	0.6200
0.7653	0.61	8200	0.7118	0.6558
0.7742	0.62	8300	0.6262	0.6497
0.6861	0.63	8400	0.6799	0.5566
0.5652	0.63	8500	0.6708	0.6559
0.7486	0.64	8600	0.6319	0.6559
0.6204	0.65	8700	0.6407	0.6530
0.673	0.66	8800	0.7154	0.4672
0.7272	0.66	8900	0.6323	0.6528
0.7364	0.67	9000	0.6436	0.6188
0.71	0.68	9100	0.6507	0.5924
0.6767	0.69	9200	0.6347	0.6575
0.7046	0.69	9300	0.6723	0.6127
0.7486	0.7	9400	0.6328	0.6485
0.7646	0.71	9500	0.6244	0.6550
0.5971	0.72	9600	0.6610	0.6558
0.6195	0.72	9700	0.6219	0.6515
0.6891	0.73	9800	0.6300	0.6619
0.6829	0.74	9900	0.6312	0.6568
0.4786	0.75	10000	0.7160	0.6573
0.6093	0.75	10100	0.6245	0.6503
0.672	0.76	10200	0.6248	0.6577
0.6734	0.77	10300	0.6541	0.6600
0.7826	0.78	10400	0.6413	0.6559
0.6851	0.78	10500	0.6478	0.6006
0.6776	0.79	10600	0.6453	0.6175
0.7322	0.8	10700	0.6188	0.6353
0.5144	0.81	10800	0.6762	0.6571
0.6977	0.81	10900	0.6559	0.6544
0.5681	0.82	11000	0.7225	0.6559
0.6449	0.83	11100	0.6372	0.6576
0.6067	0.83	11200	0.6207	0.6391
0.5921	0.84	11300	0.6178	0.6538
0.5373	0.85	11400	0.7370	0.6559
0.6926	0.86	11500	0.6346	0.6372
0.6634	0.86	11600	0.6274	0.6489
0.61	0.87	11700	0.6309	0.6427
0.6214	0.88	11800	0.6273	0.6480
0.6202	0.89	11900	0.6255	0.6559
0.6153	0.89	12000	0.6348	0.6459
0.7062	0.9	12100	0.6283	0.6512
0.6977	0.91	12200	0.6159	0.6515
0.6041	0.92	12300	0.6251	0.6504
0.6609	0.92	12400	0.6633	0.5870
0.7565	0.93	12500	0.6200	0.6562
0.6133	0.94	12600	0.6193	0.6527
0.7066	0.95	12700	0.6279	0.6180
0.5706	0.95	12800	0.6128	0.6575
0.6992	0.96	12900	0.6334	0.6449
0.6834	0.97	13000	0.6258	0.6591
0.6069	0.98	13100	0.6290	0.6620
0.743	0.98	13200	0.6110	0.6562
0.5226	0.99	13300	0.6165	0.6557
0.7359	1.0	13400	0.6207	0.6376
0.5812	1.01	13500	0.6192	0.6559
0.666	1.01	13600	0.6347	0.6602
0.5489	1.02	13700	0.6107	0.6459
0.701	1.03	13800	0.6172	0.6518
0.4873	1.04	13900	0.6786	0.6559
0.5807	1.04	14000	0.6636	0.6433
0.6824	1.05	14100	0.6176	0.6315
0.6012	1.06	14200	0.6097	0.6617
0.4865	1.07	14300	0.6103	0.6623
0.5612	1.07	14400	0.6947	0.6559
0.5968	1.08	14500	0.6559	0.5981
0.5657	1.09	14600	0.6076	0.6509
0.4778	1.1	14700	0.6808	0.6535
0.6047	1.1	14800	0.6131	0.6480
0.5999	1.11	14900	0.6120	0.6559
0.5852	1.12	15000	0.6356	0.6553
0.7033	1.13	15100	0.6578	0.6647
0.5925	1.13	15200	0.6153	0.6633
0.5959	1.14	15300	0.6306	0.6211
0.5929	1.15	15400	0.6246	0.6655
0.5621	1.16	15500	0.6126	0.6424
0.5508	1.16	15600	0.6844	0.6559
0.6276	1.17	15700	0.6066	0.6531
1.0359	1.18	15800	0.6271	0.6617
0.6191	1.19	15900	0.6166	0.6480
0.7095	1.19	16000	0.6228	0.6462
0.6567	1.2	16100	0.6066	0.6653
0.5653	1.21	16200	0.6022	0.6605
0.6894	1.21	16300	0.6216	0.6568
0.608	1.22	16400	0.6041	0.6559
0.665	1.23	16500	0.6111	0.6564
0.6753	1.24	16600	0.6138	0.6581
0.6213	1.24	16700	0.6121	0.6380
0.6983	1.25	16800	0.6166	0.6661
0.8521	1.26	16900	0.6202	0.6461
0.4927	1.27	17000	0.6313	0.6547
0.6414	1.27	17100	0.6011	0.6667
0.539	1.28	17200	0.6451	0.6664
0.5118	1.29	17300	0.6243	0.6641
0.7512	1.3	17400	0.6257	0.6586
0.5943	1.3	17500	0.6186	0.6423
0.5861	1.31	17600	0.6435	0.6638
0.7065	1.32	17700	0.6197	0.6279
0.5973	1.33	17800	0.6081	0.6535
0.5997	1.33	17900	0.6053	0.6608
0.7091	1.34	18000	0.6013	0.6644
0.691	1.35	18100	0.6103	0.6654
0.5559	1.36	18200	0.6110	0.6658
0.6309	1.36	18300	0.6067	0.6664
0.6262	1.37	18400	0.6027	0.6616
0.5551	1.38	18500	0.6106	0.6671
0.6703	1.39	18600	0.6043	0.6576
0.6849	1.39	18700	0.6018	0.6616
0.6136	1.4	18800	0.6324	0.6629
0.7075	1.41	18900	0.6057	0.6561
0.6036	1.42	19000	0.6081	0.6559
0.6549	1.42	19100	0.6352	0.6655
0.5168	1.43	19200	0.6042	0.6632
0.5864	1.44	19300	0.6111	0.6639
0.5961	1.45	19400	0.6003	0.6644
0.6077	1.45	19500	0.6125	0.6566
0.6215	1.46	19600	0.6128	0.6582
0.4005	1.47	19700	0.6348	0.6642
0.5689	1.48	19800	0.6355	0.6647
0.6026	1.48	19900	0.6127	0.6444
0.4982	1.49	20000	0.6034	0.6654
0.6189	1.5	20100	0.6202	0.6609
0.5502	1.51	20200	0.6044	0.6621
0.5924	1.51	20300	0.6107	0.6445
0.744	1.52	20400	0.6164	0.6559
0.5582	1.53	20500	0.6166	0.6559
0.6994	1.54	20600	0.6109	0.6664
0.5396	1.54	20700	0.6189	0.6670
0.7232	1.55	20800	0.6104	0.6610
0.9802	1.56	20900	0.6232	0.6642
0.6487	1.57	21000	0.6056	0.6505
0.5932	1.57	21100	0.5980	0.6702
0.7897	1.58	21200	0.6012	0.6638
0.6006	1.59	21300	0.6232	0.6672
0.4481	1.6	21400	0.6124	0.6676
0.6078	1.6	21500	0.6495	0.6664
0.595	1.61	21600	0.7122	0.6675
0.6388	1.62	21700	0.6227	0.6671
0.5731	1.62	21800	0.6252	0.6682
0.8603	1.63	21900	0.6026	0.6653
0.6316	1.64	22000	0.6494	0.6669
0.6712	1.65	22100	0.6097	0.6676
0.6102	1.65	22200	0.6221	0.6585
0.7099	1.66	22300	0.6006	0.6658
0.621	1.67	22400	0.6026	0.6626
0.478	1.68	22500	0.6062	0.6624
0.6106	1.68	22600	0.5990	0.6669
0.5793	1.69	22700	0.5980	0.6681
0.5804	1.7	22800	0.6014	0.6626
0.6304	1.71	22900	0.6107	0.6380
0.7427	1.71	23000	0.6051	0.6682
0.5794	1.72	23100	0.6105	0.6611
0.5084	1.73	23200	0.6643	0.6673
0.6518	1.74	23300	0.6366	0.6687
0.5129	1.74	23400	0.6053	0.6682
0.7593	1.75	23500	0.5977	0.6662
0.6645	1.76	23600	0.5988	0.6683
0.6144	1.77	23700	0.6130	0.6673
0.6855	1.77	23800	0.6192	0.6596
0.559	1.78	23900	0.6208	0.6574
0.4202	1.79	24000	0.6125	0.6690
0.6604	1.8	24100	0.6052	0.6685
0.5487	1.8	24200	0.6086	0.6685
0.6816	1.81	24300	0.5997	0.6620
0.6057	1.82	24400	0.6128	0.6530
0.4335	1.83	24500	0.6121	0.6676
0.6147	1.83	24600	0.6225	0.6670
0.7414	1.84	24700	0.6248	0.6718
0.622	1.85	24800	0.6084	0.6722
0.5356	1.86	24900	0.6003	0.6611
0.7994	1.86	25000	0.6098	0.6657
0.5389	1.87	25100	0.6052	0.6633
0.6985	1.88	25200	0.6073	0.6694
0.652	1.89	25300	0.6040	0.6709
0.5409	1.89	25400	0.6065	0.6709
0.6356	1.9	25500	0.6062	0.6699
0.7588	1.91	25600	0.6025	0.6711
0.5109	1.92	25700	0.5992	0.6693
0.6766	1.92	25800	0.6004	0.6693
0.6517	1.93	25900	0.6020	0.6701
0.6561	1.94	26000	0.5995	0.6705
0.6224	1.95	26100	0.6008	0.6717
0.6054	1.95	26200	0.6005	0.6714
0.5152	1.96	26300	0.6023	0.6709
0.5503	1.97	26400	0.6032	0.6706
0.5101	1.98	26500	0.6067	0.6709
0.5229	1.98	26600	0.6079	0.6702
0.8387	1.99	26700	0.6079	0.6700
0.608	2.0	26800	0.6069	0.6699

Framework versions

Transformers 4.30.2
Pytorch 1.13.1+cu116
Datasets 2.13.1
Tokenizers 0.13.3

Task: Image Classification
Library: transformers
Type: vit
Created: Jul 7, 2023
Updated: Jul 13, 2023