vit-gabor-detection-v2

nicolasdupuisroy/vit-gabor-detection-v2

vit-gabor-detection-v2 is a image classification model from nicolasdupuisroy. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.

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

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At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: vit
Access: Public
Created: Dec 19, 2023
Updated: Dec 19, 2023
SHA: 4fe24a2f

Base models

google/vit-base-patch16-224-in21k

Datasets

imagefolder

Model card

From the Hugging Face model README

vit-gabor-detection-v2

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: 0.0186
Accuracy: 1.0

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: 200
eval_batch_size: 200
seed: 1337
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 120.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	1	0.5751	1.0
No log	2.0	2	0.5081	1.0
No log	3.0	3	0.4654	1.0
No log	4.0	4	0.4014	1.0
No log	5.0	5	0.3692	1.0
No log	6.0	6	0.3327	1.0
No log	7.0	7	0.2937	1.0
No log	8.0	8	0.2775	1.0
No log	9.0	9	0.2335	1.0
0.4432	10.0	10	0.2092	1.0
0.4432	11.0	11	0.2007	1.0
0.4432	12.0	12	0.1674	1.0
0.4432	13.0	13	0.1546	1.0
0.4432	14.0	14	0.1393	1.0
0.4432	15.0	15	0.1297	1.0
0.4432	16.0	16	0.1219	1.0
0.4432	17.0	17	0.1090	1.0
0.4432	18.0	18	0.1012	1.0
0.4432	19.0	19	0.0981	1.0
0.1696	20.0	20	0.0874	1.0
0.1696	21.0	21	0.0812	1.0
0.1696	22.0	22	0.0750	1.0
0.1696	23.0	23	0.0754	1.0
0.1696	24.0	24	0.0693	1.0
0.1696	25.0	25	0.0642	1.0
0.1696	26.0	26	0.0610	1.0
0.1696	27.0	27	0.0586	1.0
0.1696	28.0	28	0.0569	1.0
0.1696	29.0	29	0.0532	1.0
0.0792	30.0	30	0.0506	1.0
0.0792	31.0	31	0.0495	1.0
0.0792	32.0	32	0.0476	1.0
0.0792	33.0	33	0.0457	1.0
0.0792	34.0	34	0.0442	1.0
0.0792	35.0	35	0.0419	1.0
0.0792	36.0	36	0.0404	1.0
0.0792	37.0	37	0.0396	1.0
0.0792	38.0	38	0.0384	1.0
0.0792	39.0	39	0.0377	1.0
0.049	40.0	40	0.0366	1.0
0.049	41.0	41	0.0370	1.0
0.049	42.0	42	0.0339	1.0
0.049	43.0	43	0.0330	1.0
0.049	44.0	44	0.0344	1.0
0.049	45.0	45	0.0324	1.0
0.049	46.0	46	0.0323	1.0
0.049	47.0	47	0.0311	1.0
0.049	48.0	48	0.0308	1.0
0.049	49.0	49	0.0294	1.0
0.0359	50.0	50	0.0297	1.0
0.0359	51.0	51	0.0289	1.0
0.0359	52.0	52	0.0285	1.0
0.0359	53.0	53	0.0280	1.0
0.0359	54.0	54	0.0270	1.0
0.0359	55.0	55	0.0265	1.0
0.0359	56.0	56	0.0266	1.0
0.0359	57.0	57	0.0261	1.0
0.0359	58.0	58	0.0268	1.0
0.0359	59.0	59	0.0255	1.0
0.0293	60.0	60	0.0255	1.0
0.0293	61.0	61	0.0246	1.0
0.0293	62.0	62	0.0256	1.0
0.0293	63.0	63	0.0247	1.0
0.0293	64.0	64	0.0241	1.0
0.0293	65.0	65	0.0241	1.0
0.0293	66.0	66	0.0234	1.0
0.0293	67.0	67	0.0236	1.0
0.0293	68.0	68	0.0228	1.0
0.0293	69.0	69	0.0233	1.0
0.0256	70.0	70	0.0227	1.0
0.0256	71.0	71	0.0227	1.0
0.0256	72.0	72	0.0230	1.0
0.0256	73.0	73	0.0222	1.0
0.0256	74.0	74	0.0220	1.0
0.0256	75.0	75	0.0221	1.0
0.0256	76.0	76	0.0219	1.0
0.0256	77.0	77	0.0215	1.0
0.0256	78.0	78	0.0210	1.0
0.0256	79.0	79	0.0209	1.0
0.0234	80.0	80	0.0212	1.0
0.0234	81.0	81	0.0212	1.0
0.0234	82.0	82	0.0206	1.0
0.0234	83.0	83	0.0210	1.0
0.0234	84.0	84	0.0204	1.0
0.0234	85.0	85	0.0205	1.0
0.0234	86.0	86	0.0204	1.0
0.0234	87.0	87	0.0203	1.0
0.0234	88.0	88	0.0200	1.0
0.0234	89.0	89	0.0203	1.0
0.0218	90.0	90	0.0196	1.0
0.0218	91.0	91	0.0199	1.0
0.0218	92.0	92	0.0198	1.0
0.0218	93.0	93	0.0196	1.0
0.0218	94.0	94	0.0195	1.0
0.0218	95.0	95	0.0198	1.0
0.0218	96.0	96	0.0197	1.0
0.0218	97.0	97	0.0193	1.0
0.0218	98.0	98	0.0195	1.0
0.0218	99.0	99	0.0194	1.0
0.0208	100.0	100	0.0192	1.0
0.0208	101.0	101	0.0190	1.0
0.0208	102.0	102	0.0188	1.0
0.0208	103.0	103	0.0191	1.0
0.0208	104.0	104	0.0193	1.0
0.0208	105.0	105	0.0193	1.0
0.0208	106.0	106	0.0190	1.0
0.0208	107.0	107	0.0191	1.0
0.0208	108.0	108	0.0186	1.0
0.0208	109.0	109	0.0188	1.0
0.0202	110.0	110	0.0187	1.0
0.0202	111.0	111	0.0191	1.0
0.0202	112.0	112	0.0188	1.0
0.0202	113.0	113	0.0185	1.0
0.0202	114.0	114	0.0188	1.0
0.0202	115.0	115	0.0183	1.0
0.0202	116.0	116	0.0187	1.0
0.0202	117.0	117	0.0185	1.0
0.0202	118.0	118	0.0184	1.0
0.0202	119.0	119	0.0188	1.0
0.0197	120.0	120	0.0185	1.0

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Dec 19, 2023
Updated: Dec 19, 2023