hushem_40x_deit_tiny_rms_001_fold2

This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 8.4415
• Accuracy: 0.4

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: 0.001
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• 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
1.2259	1.0	215	1.1893	0.3778
0.7887	2.0	430	1.7800	0.3778
0.6967	3.0	645	1.8757	0.4667
0.6862	4.0	860	1.9414	0.4444
0.4924	5.0	1075	1.8919	0.4444
0.5712	6.0	1290	1.8316	0.5111
0.4554	7.0	1505	2.6959	0.5111
0.3791	8.0	1720	3.2703	0.4889
0.2815	9.0	1935	2.8206	0.4222
0.3229	10.0	2150	2.1796	0.3778
0.2732	11.0	2365	2.6937	0.4222
0.2161	12.0	2580	2.7085	0.4
0.2247	13.0	2795	2.7907	0.5556
0.1656	14.0	3010	3.5588	0.5111
0.2252	15.0	3225	3.4710	0.4667
0.1912	16.0	3440	4.0799	0.4667
0.2296	17.0	3655	3.3917	0.5778
0.0717	18.0	3870	5.2253	0.4222
0.0776	19.0	4085	4.4474	0.4667
0.0565	20.0	4300	5.1424	0.4222
0.0848	21.0	4515	4.9397	0.4667
0.0607	22.0	4730	4.6748	0.4667
0.0626	23.0	4945	5.0805	0.4889
0.0796	24.0	5160	5.0210	0.4444
0.0786	25.0	5375	5.7741	0.4667
0.011	26.0	5590	4.7102	0.5333
0.0247	27.0	5805	5.9220	0.4444
0.0051	28.0	6020	6.4658	0.4222
0.0246	29.0	6235	5.3041	0.5111
0.025	30.0	6450	5.4166	0.5333
0.0321	31.0	6665	5.7245	0.4444
0.0467	32.0	6880	5.9082	0.5111
0.0354	33.0	7095	5.7199	0.4667
0.0267	34.0	7310	6.9737	0.4444
0.0012	35.0	7525	6.7506	0.4222
0.0014	36.0	7740	7.0113	0.4222
0.0151	37.0	7955	6.8314	0.4444
0.0325	38.0	8170	6.8690	0.4444
0.0	39.0	8385	6.9350	0.4667
0.0006	40.0	8600	7.6894	0.4444
0.0001	41.0	8815	7.8369	0.4222
0.0001	42.0	9030	7.3604	0.4
0.0	43.0	9245	7.8724	0.3778
0.0	44.0	9460	7.8044	0.3333
0.0	45.0	9675	8.3094	0.4
0.001	46.0	9890	8.3688	0.4
0.0018	47.0	10105	8.4135	0.4
0.0	48.0	10320	8.3955	0.4
0.0	49.0	10535	8.4293	0.4
0.0	50.0	10750	8.4415	0.4

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.1+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2