deit-base-distilled-patch16-224-hasta-85-fold3

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: 0.7871
• 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	0.8686	0.6364
No log	2.0	2	0.7871	0.7273
No log	3.0	3	0.7116	0.7273
No log	4.0	4	0.7585	0.7273
No log	5.0	5	0.9216	0.7273
No log	6.0	6	1.0848	0.7273
No log	7.0	7	1.1931	0.7273
No log	8.0	8	1.2543	0.7273
No log	9.0	9	1.3098	0.7273
0.2667	10.0	10	1.3979	0.7273
0.2667	11.0	11	1.4209	0.7273
0.2667	12.0	12	1.4302	0.7273
0.2667	13.0	13	1.4202	0.7273
0.2667	14.0	14	1.3569	0.7273
0.2667	15.0	15	1.3033	0.7273
0.2667	16.0	16	1.3414	0.7273
0.2667	17.0	17	1.4312	0.7273
0.2667	18.0	18	1.6063	0.7273
0.2667	19.0	19	1.7308	0.7273
0.1296	20.0	20	1.7541	0.7273
0.1296	21.0	21	1.7183	0.7273
0.1296	22.0	22	1.6395	0.7273
0.1296	23.0	23	1.6197	0.7273
0.1296	24.0	24	1.6525	0.7273
0.1296	25.0	25	1.7183	0.7273
0.1296	26.0	26	1.7120	0.7273
0.1296	27.0	27	1.6748	0.7273
0.1296	28.0	28	1.5840	0.7273
0.1296	29.0	29	1.5963	0.7273
0.0834	30.0	30	1.7016	0.7273
0.0834	31.0	31	1.7780	0.7273
0.0834	32.0	32	1.7943	0.7273
0.0834	33.0	33	1.7993	0.7273
0.0834	34.0	34	1.7873	0.7273
0.0834	35.0	35	1.8196	0.7273
0.0834	36.0	36	1.9190	0.7273
0.0834	37.0	37	2.0467	0.7273
0.0834	38.0	38	2.1647	0.7273
0.0834	39.0	39	2.2634	0.7273
0.0441	40.0	40	2.3170	0.7273
0.0441	41.0	41	2.3263	0.7273
0.0441	42.0	42	2.2991	0.7273
0.0441	43.0	43	2.2792	0.7273
0.0441	44.0	44	2.2572	0.7273
0.0441	45.0	45	2.2751	0.7273
0.0441	46.0	46	2.3103	0.7273
0.0441	47.0	47	2.3325	0.7273
0.0441	48.0	48	2.3259	0.7273
0.0441	49.0	49	2.2974	0.7273
0.0437	50.0	50	2.2312	0.7273
0.0437	51.0	51	2.1638	0.7273
0.0437	52.0	52	2.0963	0.7273
0.0437	53.0	53	2.0074	0.7273
0.0437	54.0	54	1.9069	0.7273
0.0437	55.0	55	1.8899	0.7273
0.0437	56.0	56	1.9432	0.7273
0.0437	57.0	57	2.0307	0.7273
0.0437	58.0	58	2.1621	0.7273
0.0437	59.0	59	2.2470	0.7273
0.04	60.0	60	2.3281	0.7273
0.04	61.0	61	2.3529	0.7273
0.04	62.0	62	2.3491	0.7273
0.04	63.0	63	2.3534	0.7273
0.04	64.0	64	2.3541	0.7273
0.04	65.0	65	2.3474	0.7273
0.04	66.0	66	2.3363	0.7273
0.04	67.0	67	2.3193	0.7273
0.04	68.0	68	2.3144	0.7273
0.04	69.0	69	2.2847	0.7273
0.0232	70.0	70	2.2488	0.7273
0.0232	71.0	71	2.2175	0.7273
0.0232	72.0	72	2.1898	0.7273
0.0232	73.0	73	2.1797	0.7273
0.0232	74.0	74	2.1829	0.7273
0.0232	75.0	75	2.1806	0.7273
0.0232	76.0	76	2.1685	0.7273
0.0232	77.0	77	2.1505	0.7273
0.0232	78.0	78	2.1548	0.7273
0.0232	79.0	79	2.1650	0.7273
0.0243	80.0	80	2.1961	0.7273
0.0243	81.0	81	2.2283	0.7273
0.0243	82.0	82	2.2619	0.7273
0.0243	83.0	83	2.2783	0.7273
0.0243	84.0	84	2.2796	0.7273
0.0243	85.0	85	2.2780	0.7273
0.0243	86.0	86	2.2769	0.7273
0.0243	87.0	87	2.2655	0.7273
0.0243	88.0	88	2.2584	0.7273
0.0243	89.0	89	2.2419	0.7273
0.0301	90.0	90	2.2299	0.7273
0.0301	91.0	91	2.2151	0.7273
0.0301	92.0	92	2.2038	0.7273
0.0301	93.0	93	2.1956	0.7273
0.0301	94.0	94	2.1946	0.7273
0.0301	95.0	95	2.2012	0.7273
0.0301	96.0	96	2.2079	0.7273
0.0301	97.0	97	2.2151	0.7273
0.0301	98.0	98	2.2219	0.7273
0.0301	99.0	99	2.2261	0.7273
0.0154	100.0	100	2.2286	0.7273

Framework versions

• Transformers 4.41.0
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1