smids_5x_deit_tiny_sgd_001_fold2

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

• Loss: 0.3358
• Accuracy: 0.8686

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
0.7754	1.0	375	0.7240	0.7271
0.5298	2.0	750	0.5482	0.7837
0.4453	3.0	1125	0.4761	0.8186
0.3233	4.0	1500	0.4354	0.8286
0.3301	5.0	1875	0.4115	0.8386
0.3179	6.0	2250	0.3924	0.8469
0.3101	7.0	2625	0.3803	0.8502
0.3266	8.0	3000	0.3685	0.8586
0.2663	9.0	3375	0.3605	0.8552
0.2805	10.0	3750	0.3550	0.8536
0.2677	11.0	4125	0.3495	0.8619
0.3046	12.0	4500	0.3461	0.8686
0.2173	13.0	4875	0.3409	0.8602
0.2384	14.0	5250	0.3398	0.8636
0.2681	15.0	5625	0.3343	0.8652
0.1901	16.0	6000	0.3336	0.8735
0.2623	17.0	6375	0.3353	0.8735
0.1865	18.0	6750	0.3314	0.8735
0.2003	19.0	7125	0.3309	0.8735
0.2713	20.0	7500	0.3280	0.8752
0.2017	21.0	7875	0.3298	0.8702
0.1863	22.0	8250	0.3281	0.8769
0.227	23.0	8625	0.3271	0.8769
0.1889	24.0	9000	0.3290	0.8752
0.1561	25.0	9375	0.3282	0.8752
0.2339	26.0	9750	0.3258	0.8752
0.2006	27.0	10125	0.3286	0.8802
0.1745	28.0	10500	0.3294	0.8719
0.1852	29.0	10875	0.3284	0.8719
0.1931	30.0	11250	0.3301	0.8702
0.1811	31.0	11625	0.3297	0.8735
0.1783	32.0	12000	0.3325	0.8702
0.1809	33.0	12375	0.3288	0.8769
0.1274	34.0	12750	0.3315	0.8652
0.1957	35.0	13125	0.3314	0.8702
0.1704	36.0	13500	0.3319	0.8686
0.1796	37.0	13875	0.3309	0.8686
0.1565	38.0	14250	0.3327	0.8702
0.1735	39.0	14625	0.3325	0.8686
0.1525	40.0	15000	0.3345	0.8669
0.1548	41.0	15375	0.3344	0.8735
0.1677	42.0	15750	0.3353	0.8669
0.1708	43.0	16125	0.3357	0.8669
0.1467	44.0	16500	0.3356	0.8669
0.1338	45.0	16875	0.3358	0.8686
0.2032	46.0	17250	0.3360	0.8669
0.1609	47.0	17625	0.3359	0.8686
0.155	48.0	18000	0.3359	0.8686
0.2258	49.0	18375	0.3359	0.8669
0.1319	50.0	18750	0.3358	0.8686

Framework versions

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