hushem_40x_deit_tiny_rms_0001_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: 4.3313
• Accuracy: 0.6667

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: 0.0001
• 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
0.1374	1.0	215	1.3718	0.7111
0.0248	2.0	430	1.5033	0.7778
0.0726	3.0	645	1.7295	0.7556
0.0372	4.0	860	1.5869	0.7778
0.0411	5.0	1075	1.1809	0.7778
0.0235	6.0	1290	2.1699	0.6889
0.0004	7.0	1505	1.8564	0.7333
0.0351	8.0	1720	2.6913	0.5556
0.0436	9.0	1935	1.7899	0.6889
0.0311	10.0	2150	2.2763	0.7333
0.0318	11.0	2365	2.1440	0.7111
0.0601	12.0	2580	1.3738	0.8
0.0036	13.0	2795	1.9492	0.7556
0.0024	14.0	3010	2.0010	0.7778
0.0119	15.0	3225	2.9477	0.7111
0.0001	16.0	3440	2.0050	0.8222
0.0	17.0	3655	2.2043	0.7778
0.0045	18.0	3870	2.9253	0.6889
0.0002	19.0	4085	2.4235	0.7333
0.0	20.0	4300	3.4852	0.6
0.0276	21.0	4515	3.0762	0.6667
0.0098	22.0	4730	3.3340	0.6222
0.0328	23.0	4945	1.8687	0.8
0.0	24.0	5160	1.6806	0.8
0.0	25.0	5375	2.3408	0.7333
0.0208	26.0	5590	2.3251	0.7778
0.0	27.0	5805	2.8347	0.7111
0.0	28.0	6020	2.2742	0.7333
0.0	29.0	6235	2.4267	0.7111
0.0	30.0	6450	2.5951	0.7111
0.0	31.0	6665	2.7772	0.6889
0.0	32.0	6880	2.9769	0.6889
0.0	33.0	7095	3.1694	0.6889
0.0	34.0	7310	3.3770	0.6889
0.0	35.0	7525	3.5369	0.6889
0.0	36.0	7740	3.6892	0.7111
0.0	37.0	7955	3.8241	0.6889
0.0	38.0	8170	3.9473	0.6889
0.0	39.0	8385	4.0424	0.6889
0.0	40.0	8600	4.1157	0.6889
0.0	41.0	8815	4.1738	0.6667
0.0	42.0	9030	4.2155	0.6667
0.0	43.0	9245	4.2470	0.6667
0.0	44.0	9460	4.2729	0.6667
0.0	45.0	9675	4.2929	0.6667
0.0	46.0	9890	4.3080	0.6667
0.0	47.0	10105	4.3190	0.6667
0.0	48.0	10320	4.3263	0.6667
0.0	49.0	10535	4.3304	0.6667
0.0	50.0	10750	4.3313	0.6667

Framework versions

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