smids_1x_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: 1.4112
• Accuracy: 0.8569

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
0.8146	1.0	75	0.7861	0.5524
0.5727	2.0	150	0.4984	0.8103
0.5359	3.0	225	0.4227	0.8286
0.4099	4.0	300	0.4531	0.8270
0.3762	5.0	375	0.4703	0.8236
0.2518	6.0	450	0.5236	0.8453
0.2143	7.0	525	0.5529	0.8353
0.1716	8.0	600	0.6424	0.8519
0.1064	9.0	675	1.0458	0.7987
0.1362	10.0	750	0.7319	0.8336
0.1448	11.0	825	0.9729	0.8236
0.0254	12.0	900	0.9267	0.8436
0.0822	13.0	975	1.0041	0.8336
0.0792	14.0	1050	1.1093	0.8220
0.0861	15.0	1125	1.1399	0.8153
0.049	16.0	1200	1.3759	0.8103
0.0209	17.0	1275	1.1868	0.8303
0.0314	18.0	1350	1.3024	0.8353
0.0371	19.0	1425	1.1958	0.8303
0.0408	20.0	1500	1.0595	0.8469
0.0443	21.0	1575	1.2918	0.8353
0.0161	22.0	1650	1.3270	0.8270
0.002	23.0	1725	1.3561	0.8369
0.0119	24.0	1800	1.3471	0.8353
0.021	25.0	1875	1.3114	0.8403
0.0001	26.0	1950	1.2789	0.8453
0.0215	27.0	2025	1.3801	0.8253
0.0117	28.0	2100	1.3311	0.8353
0.0064	29.0	2175	1.5354	0.8153
0.0497	30.0	2250	1.2007	0.8419
0.0245	31.0	2325	1.2452	0.8586
0.0	32.0	2400	1.2980	0.8586
0.0	33.0	2475	1.3038	0.8586
0.0	34.0	2550	1.3062	0.8552
0.0104	35.0	2625	1.3421	0.8519
0.0001	36.0	2700	1.3682	0.8369
0.0027	37.0	2775	1.4409	0.8419
0.0	38.0	2850	1.3923	0.8519
0.0017	39.0	2925	1.4064	0.8536
0.0	40.0	3000	1.4003	0.8519
0.0027	41.0	3075	1.4111	0.8519
0.0	42.0	3150	1.4021	0.8519
0.0025	43.0	3225	1.4193	0.8519
0.0029	44.0	3300	1.3989	0.8552
0.0	45.0	3375	1.4257	0.8536
0.0	46.0	3450	1.4244	0.8536
0.0027	47.0	3525	1.4185	0.8536
0.0	48.0	3600	1.4177	0.8569
0.0023	49.0	3675	1.4124	0.8569
0.0021	50.0	3750	1.4112	0.8569

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0