smids_5x_deit_tiny_sgd_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: 0.4980
• Accuracy: 0.8003

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
1.1772	1.0	375	1.1487	0.3760
1.0767	2.0	750	1.0817	0.4276
0.9966	3.0	1125	1.0309	0.4742
0.9479	4.0	1500	0.9881	0.5158
0.9443	5.0	1875	0.9501	0.5374
0.9172	6.0	2250	0.9138	0.5574
0.8419	7.0	2625	0.8792	0.5857
0.8745	8.0	3000	0.8452	0.6023
0.8037	9.0	3375	0.8135	0.6273
0.7725	10.0	3750	0.7849	0.6556
0.73	11.0	4125	0.7582	0.6705
0.7472	12.0	4500	0.7325	0.6822
0.6618	13.0	4875	0.7082	0.7105
0.6872	14.0	5250	0.6883	0.7255
0.6348	15.0	5625	0.6702	0.7304
0.5716	16.0	6000	0.6521	0.7388
0.6306	17.0	6375	0.6373	0.7454
0.604	18.0	6750	0.6231	0.7537
0.5325	19.0	7125	0.6109	0.7604
0.602	20.0	7500	0.6001	0.7604
0.5143	21.0	7875	0.5907	0.7604
0.5394	22.0	8250	0.5814	0.7671
0.5319	23.0	8625	0.5730	0.7671
0.5182	24.0	9000	0.5659	0.7704
0.5353	25.0	9375	0.5592	0.7720
0.5323	26.0	9750	0.5527	0.7787
0.5286	27.0	10125	0.5472	0.7787
0.4649	28.0	10500	0.5420	0.7820
0.5396	29.0	10875	0.5373	0.7837
0.4863	30.0	11250	0.5330	0.7837
0.4735	31.0	11625	0.5292	0.7837
0.5595	32.0	12000	0.5255	0.7887
0.4938	33.0	12375	0.5219	0.7920
0.468	34.0	12750	0.5190	0.7970
0.4879	35.0	13125	0.5162	0.7970
0.4592	36.0	13500	0.5136	0.7970
0.4625	37.0	13875	0.5113	0.7953
0.4476	38.0	14250	0.5091	0.7970
0.4681	39.0	14625	0.5073	0.8003
0.4644	40.0	15000	0.5056	0.8003
0.4624	41.0	15375	0.5040	0.8003
0.4244	42.0	15750	0.5026	0.8003
0.457	43.0	16125	0.5015	0.8003
0.4802	44.0	16500	0.5005	0.8003
0.4285	45.0	16875	0.4996	0.8003
0.4665	46.0	17250	0.4990	0.8003
0.4627	47.0	17625	0.4985	0.8003
0.4554	48.0	18000	0.4982	0.8003
0.4888	49.0	18375	0.4980	0.8003
0.4643	50.0	18750	0.4980	0.8003

Framework versions

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