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

Loss: 0.7292
Accuracy: 0.7273

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: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	1	1.3530	0.2727
No log	2.0	2	1.1775	0.2727
No log	3.0	3	0.9128	0.5455
No log	4.0	4	0.7292	0.7273
No log	5.0	5	0.7753	0.7273
No log	6.0	6	0.9952	0.7273
No log	7.0	7	1.1799	0.7273
No log	8.0	8	1.2699	0.7273
No log	9.0	9	1.2729	0.7273
0.38	10.0	10	1.2451	0.7273
0.38	11.0	11	1.2341	0.7273
0.38	12.0	12	1.2153	0.7273
0.38	13.0	13	1.1847	0.7273
0.38	14.0	14	1.1903	0.7273
0.38	15.0	15	1.2128	0.7273
0.38	16.0	16	1.1724	0.7273
0.38	17.0	17	1.0705	0.7273
0.38	18.0	18	1.0106	0.7273
0.38	19.0	19	1.0238	0.7273
0.1729	20.0	20	1.0594	0.7273
0.1729	21.0	21	1.1695	0.7273
0.1729	22.0	22	1.2466	0.7273
0.1729	23.0	23	1.2861	0.7273
0.1729	24.0	24	1.2790	0.7273
0.1729	25.0	25	1.3177	0.7273
0.1729	26.0	26	1.4130	0.7273
0.1729	27.0	27	1.5155	0.7273
0.1729	28.0	28	1.6224	0.7273
0.1729	29.0	29	1.5918	0.7273
0.0904	30.0	30	1.4099	0.7273
0.0904	31.0	31	1.2681	0.7273
0.0904	32.0	32	1.1371	0.7273
0.0904	33.0	33	1.0387	0.7273
0.0904	34.0	34	1.0128	0.7273
0.0904	35.0	35	1.0423	0.7273
0.0904	36.0	36	1.1730	0.7273
0.0904	37.0	37	1.3305	0.7273
0.0904	38.0	38	1.3820	0.7273
0.0904	39.0	39	1.3682	0.7273
0.0591	40.0	40	1.2854	0.7273
0.0591	41.0	41	1.2000	0.7273
0.0591	42.0	42	1.2079	0.7273
0.0591	43.0	43	1.2967	0.7273
0.0591	44.0	44	1.3672	0.7273
0.0591	45.0	45	1.3709	0.7273
0.0591	46.0	46	1.4370	0.7273
0.0591	47.0	47	1.4863	0.7273
0.0591	48.0	48	1.5696	0.7273
0.0591	49.0	49	1.5678	0.7273
0.0289	50.0	50	1.5444	0.7273
0.0289	51.0	51	1.5086	0.7273
0.0289	52.0	52	1.4333	0.7273
0.0289	53.0	53	1.3644	0.7273
0.0289	54.0	54	1.3099	0.7273
0.0289	55.0	55	1.3355	0.7273
0.0289	56.0	56	1.4286	0.7273
0.0289	57.0	57	1.6121	0.7273
0.0289	58.0	58	1.8074	0.7273
0.0289	59.0	59	1.9461	0.7273
0.0342	60.0	60	2.0314	0.7273
0.0342	61.0	61	2.0408	0.7273
0.0342	62.0	62	2.0476	0.7273
0.0342	63.0	63	2.0517	0.7273
0.0342	64.0	64	2.0217	0.7273
0.0342	65.0	65	1.9582	0.7273
0.0342	66.0	66	1.8825	0.7273
0.0342	67.0	67	1.8085	0.7273
0.0342	68.0	68	1.7880	0.7273
0.0342	69.0	69	1.7796	0.7273
0.0203	70.0	70	1.7929	0.7273
0.0203	71.0	71	1.8213	0.7273
0.0203	72.0	72	1.8388	0.7273
0.0203	73.0	73	1.8488	0.7273
0.0203	74.0	74	1.8753	0.7273
0.0203	75.0	75	1.9079	0.7273
0.0203	76.0	76	1.9396	0.7273
0.0203	77.0	77	1.9556	0.7273
0.0203	78.0	78	1.9591	0.7273
0.0203	79.0	79	1.9668	0.7273
0.0324	80.0	80	1.9865	0.7273
0.0324	81.0	81	2.0098	0.7273
0.0324	82.0	82	2.0384	0.7273
0.0324	83.0	83	2.0659	0.7273
0.0324	84.0	84	2.0952	0.7273
0.0324	85.0	85	2.1069	0.7273
0.0324	86.0	86	2.1130	0.7273
0.0324	87.0	87	2.1181	0.7273
0.0324	88.0	88	2.1180	0.7273
0.0324	89.0	89	2.1173	0.7273
0.0244	90.0	90	2.1193	0.7273
0.0244	91.0	91	2.1226	0.7273
0.0244	92.0	92	2.1256	0.7273
0.0244	93.0	93	2.1312	0.7273
0.0244	94.0	94	2.1333	0.7273
0.0244	95.0	95	2.1371	0.7273
0.0244	96.0	96	2.1413	0.7273
0.0244	97.0	97	2.1438	0.7273
0.0244	98.0	98	2.1454	0.7273
0.0244	99.0	99	2.1451	0.7273
0.027	100.0	100	2.1448	0.7273

Framework versions

Transformers 4.41.0
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

BilalMuftuoglu/deit-base-distilled-patch16-224-hasta-85-fold5

deit-base-distilled-patch16-224-hasta-85-fold5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

BilalMuftuoglu/
deit-base-distilled-patch16-224-hasta-85-fold5