V3_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: 0.0411
• Accuracy: 0.9927

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
0.6667	1.0	15	0.5893	0.9121
0.4394	2.0	30	0.3294	0.9487
0.2685	3.0	45	0.1365	0.9707
0.0936	4.0	60	0.0752	0.9853
0.0517	5.0	75	0.0553	0.9890
0.0436	6.0	90	0.0556	0.9890
0.018	7.0	105	0.0557	0.9890
0.0189	8.0	120	0.0457	0.9890
0.013	9.0	135	0.0343	0.9927
0.0115	10.0	150	0.0270	0.9963
0.0101	11.0	165	0.0355	0.9927
0.0085	12.0	180	0.0356	0.9927
0.0079	13.0	195	0.0259	0.9963
0.0069	14.0	210	0.0345	0.9927
0.0066	15.0	225	0.0360	0.9927
0.0061	16.0	240	0.0359	0.9927
0.0059	17.0	255	0.0360	0.9927
0.0055	18.0	270	0.0368	0.9927
0.0054	19.0	285	0.0375	0.9927
0.0051	20.0	300	0.0375	0.9927
0.0049	21.0	315	0.0380	0.9927
0.0047	22.0	330	0.0380	0.9927
0.0046	23.0	345	0.0383	0.9927
0.0044	24.0	360	0.0386	0.9927
0.0043	25.0	375	0.0388	0.9927
0.0041	26.0	390	0.0388	0.9927
0.0041	27.0	405	0.0391	0.9927
0.0039	28.0	420	0.0392	0.9927
0.0038	29.0	435	0.0396	0.9927
0.0037	30.0	450	0.0397	0.9927
0.0037	31.0	465	0.0397	0.9927
0.0036	32.0	480	0.0399	0.9927
0.0035	33.0	495	0.0401	0.9927
0.0034	34.0	510	0.0402	0.9927
0.0034	35.0	525	0.0403	0.9927
0.0033	36.0	540	0.0403	0.9927
0.0033	37.0	555	0.0405	0.9927
0.0032	38.0	570	0.0406	0.9927
0.0032	39.0	585	0.0406	0.9927
0.0031	40.0	600	0.0407	0.9927
0.0031	41.0	615	0.0408	0.9927
0.0031	42.0	630	0.0408	0.9927
0.003	43.0	645	0.0409	0.9927
0.003	44.0	660	0.0410	0.9927
0.003	45.0	675	0.0410	0.9927
0.003	46.0	690	0.0410	0.9927
0.003	47.0	705	0.0410	0.9927
0.0029	48.0	720	0.0411	0.9927
0.0029	49.0	735	0.0411	0.9927
0.0029	50.0	750	0.0411	0.9927

Framework versions

• Transformers 4.30.0
• Pytorch 2.1.1
• Datasets 2.15.0
• Tokenizers 0.13.3