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V4_Image_classification__points_durs__google_vit-base-patch16-224-in21k

Name: V4_Image_classification__points_durs__google_vit-base-patch16-224-in21k
Availability: InStock

generated_from_trainer

apache-2.0

autotrain_compatible

endpoints_compatible

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V4_Image_classification__points_durs__google_vit-base-patch16-224-in21k

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

Loss: 0.2221
Accuracy: 0.9560

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: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6743	1.0	13	0.6315	0.7566
0.6051	2.0	26	0.4384	0.9150
0.4588	3.0	39	0.2402	0.9326
0.1818	4.0	52	0.1702	0.9384
0.1102	5.0	65	0.1409	0.9413
0.0733	6.0	78	0.1516	0.9501
0.0423	7.0	91	0.1613	0.9560
0.0286	8.0	104	0.1843	0.9501
0.0192	9.0	117	0.1672	0.9560
0.0159	10.0	130	0.1703	0.9589
0.0173	11.0	143	0.1729	0.9560
0.0143	12.0	156	0.1786	0.9560
0.0105	13.0	169	0.1821	0.9560
0.0091	14.0	182	0.1827	0.9589
0.0096	15.0	195	0.1859	0.9560
0.0081	16.0	208	0.1989	0.9560
0.0075	17.0	221	0.2012	0.9560
0.0347	18.0	234	0.2507	0.9384
0.0232	19.0	247	0.2271	0.9413
0.0065	20.0	260	0.1950	0.9589
0.0102	21.0	273	0.2378	0.9472
0.0064	22.0	286	0.2265	0.9501
0.0058	23.0	299	0.2033	0.9560
0.0055	24.0	312	0.2402	0.9501
0.005	25.0	325	0.2500	0.9443
0.0054	26.0	338	0.2450	0.9472
0.0048	27.0	351	0.2431	0.9501
0.0047	28.0	364	0.2439	0.9472
0.0046	29.0	377	0.2445	0.9472
0.0044	30.0	390	0.2434	0.9472
0.0042	31.0	403	0.2441	0.9472
0.0042	32.0	416	0.2426	0.9472
0.0042	33.0	429	0.2414	0.9472
0.004	34.0	442	0.2383	0.9472
0.004	35.0	455	0.2349	0.9472
0.0039	36.0	468	0.2340	0.9472
0.0038	37.0	481	0.2325	0.9472
0.0037	38.0	494	0.2311	0.9501
0.0038	39.0	507	0.2280	0.9501
0.0037	40.0	520	0.2263	0.9531
0.0036	41.0	533	0.2248	0.9531
0.0036	42.0	546	0.2242	0.9531
0.0036	43.0	559	0.2236	0.9531
0.0035	44.0	572	0.2231	0.9560
0.0035	45.0	585	0.2224	0.9560
0.0035	46.0	598	0.2223	0.9560
0.0035	47.0	611	0.2220	0.9560
0.0035	48.0	624	0.2221	0.9560
0.0034	49.0	637	0.2221	0.9560
0.0035	50.0	650	0.2221	0.9560

Framework versions

Transformers 4.30.0
Pytorch 2.1.1
Datasets 2.15.0
Tokenizers 0.13.3

Optikan/V4_Image_classification__points_durs__google_vit-base-patch16-224-in21k

V4_Image_classification__points_durs__google_vit-base-patch16-224-in21k

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Optikan/
V4_Image_classification__points_durs__google_vit-base-patch16-224-in21k