smids_10x_deit_tiny_sgd_0001_fold4

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: 0.4161
• Accuracy: 0.8417

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
1.0688	1.0	750	1.0895	0.3983
0.9867	2.0	1500	0.9975	0.4833
0.9278	3.0	2250	0.9175	0.54
0.8528	4.0	3000	0.8474	0.5783
0.714	5.0	3750	0.7808	0.635
0.6866	6.0	4500	0.7206	0.6917
0.6101	7.0	5250	0.6718	0.7167
0.6452	8.0	6000	0.6299	0.7333
0.6113	9.0	6750	0.5989	0.7467
0.5055	10.0	7500	0.5743	0.7633
0.4983	11.0	8250	0.5553	0.7717
0.5538	12.0	9000	0.5377	0.7733
0.4959	13.0	9750	0.5236	0.7833
0.4737	14.0	10500	0.5129	0.7867
0.4376	15.0	11250	0.5024	0.7967
0.3926	16.0	12000	0.4941	0.8033
0.397	17.0	12750	0.4866	0.805
0.4304	18.0	13500	0.4793	0.8067
0.4526	19.0	14250	0.4737	0.81
0.4267	20.0	15000	0.4680	0.81
0.3746	21.0	15750	0.4626	0.8183
0.4237	22.0	16500	0.4581	0.815
0.4022	23.0	17250	0.4540	0.82
0.465	24.0	18000	0.4503	0.8233
0.3585	25.0	18750	0.4464	0.8267
0.3671	26.0	19500	0.4431	0.8267
0.3889	27.0	20250	0.4400	0.8283
0.3836	28.0	21000	0.4372	0.83
0.3751	29.0	21750	0.4351	0.83
0.3772	30.0	22500	0.4334	0.8333
0.3959	31.0	23250	0.4312	0.8333
0.3701	32.0	24000	0.4290	0.8317
0.3441	33.0	24750	0.4274	0.8317
0.371	34.0	25500	0.4262	0.8333
0.327	35.0	26250	0.4246	0.8333
0.3799	36.0	27000	0.4233	0.8367
0.3186	37.0	27750	0.4226	0.835
0.3955	38.0	28500	0.4215	0.835
0.4171	39.0	29250	0.4206	0.835
0.4116	40.0	30000	0.4196	0.8367
0.369	41.0	30750	0.4189	0.8383
0.3461	42.0	31500	0.4184	0.8383
0.3837	43.0	32250	0.4178	0.8417
0.3565	44.0	33000	0.4174	0.8417
0.3745	45.0	33750	0.4170	0.8417
0.3413	46.0	34500	0.4167	0.8417
0.301	47.0	35250	0.4164	0.8417
0.3105	48.0	36000	0.4162	0.8417
0.3511	49.0	36750	0.4161	0.8417
0.3221	50.0	37500	0.4161	0.8417

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2