deit-base-distilled-patch16-224-75-fold2

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: 0.2625
• Accuracy: 0.9302

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	2	0.8815	0.4419
No log	2.0	4	0.6436	0.6977
No log	3.0	6	0.8488	0.6977
No log	4.0	8	0.8219	0.6977
0.6918	5.0	10	0.5491	0.6977
0.6918	6.0	12	0.4603	0.7209
0.6918	7.0	14	0.5602	0.7442
0.6918	8.0	16	0.5694	0.7442
0.6918	9.0	18	0.4430	0.8140
0.3867	10.0	20	0.3880	0.8605
0.3867	11.0	22	0.5069	0.8140
0.3867	12.0	24	0.3739	0.8605
0.3867	13.0	26	0.2981	0.8837
0.3867	14.0	28	0.3170	0.8837
0.2722	15.0	30	0.2511	0.8837
0.2722	16.0	32	0.2408	0.8837
0.2722	17.0	34	0.3751	0.8605
0.2722	18.0	36	0.3081	0.8605
0.2722	19.0	38	0.2489	0.8837
0.209	20.0	40	0.2802	0.8837
0.209	21.0	42	0.2625	0.9302
0.209	22.0	44	0.2595	0.9302
0.209	23.0	46	0.5048	0.8372
0.209	24.0	48	0.2880	0.8605
0.2027	25.0	50	0.2860	0.8372
0.2027	26.0	52	0.4067	0.8372
0.2027	27.0	54	0.2462	0.9070
0.2027	28.0	56	0.2753	0.9070
0.2027	29.0	58	0.3699	0.8140
0.1426	30.0	60	0.4983	0.8372
0.1426	31.0	62	0.3140	0.8605
0.1426	32.0	64	0.3470	0.8372
0.1426	33.0	66	0.4443	0.8372
0.1426	34.0	68	0.2583	0.8837
0.1385	35.0	70	0.2239	0.9302
0.1385	36.0	72	0.2708	0.9070
0.1385	37.0	74	0.2660	0.9070
0.1385	38.0	76	0.2754	0.9070
0.1385	39.0	78	0.4246	0.8605
0.1202	40.0	80	0.2779	0.9070
0.1202	41.0	82	0.2726	0.8837
0.1202	42.0	84	0.2536	0.9070
0.1202	43.0	86	0.2667	0.9302
0.1202	44.0	88	0.4191	0.8837
0.1211	45.0	90	0.3213	0.9302
0.1211	46.0	92	0.2290	0.9070
0.1211	47.0	94	0.3043	0.8837
0.1211	48.0	96	0.1906	0.9302
0.1211	49.0	98	0.3201	0.8605
0.1067	50.0	100	0.3062	0.8837
0.1067	51.0	102	0.2047	0.9302
0.1067	52.0	104	0.2116	0.9070
0.1067	53.0	106	0.2113	0.9302
0.1067	54.0	108	0.2340	0.9302
0.0826	55.0	110	0.2328	0.9302
0.0826	56.0	112	0.2824	0.8837
0.0826	57.0	114	0.2921	0.8837
0.0826	58.0	116	0.2608	0.9302
0.0826	59.0	118	0.2650	0.9302
0.0894	60.0	120	0.2878	0.9070
0.0894	61.0	122	0.2935	0.9070
0.0894	62.0	124	0.2656	0.9302
0.0894	63.0	126	0.3438	0.9070
0.0894	64.0	128	0.2840	0.9302
0.0964	65.0	130	0.2711	0.9070
0.0964	66.0	132	0.2888	0.9070
0.0964	67.0	134	0.2723	0.9070
0.0964	68.0	136	0.2563	0.8837
0.0964	69.0	138	0.2336	0.9302
0.0711	70.0	140	0.2386	0.9302
0.0711	71.0	142	0.2482	0.9070
0.0711	72.0	144	0.2821	0.9070
0.0711	73.0	146	0.2941	0.8837
0.0711	74.0	148	0.2564	0.9070
0.0824	75.0	150	0.2509	0.9302
0.0824	76.0	152	0.2544	0.9302
0.0824	77.0	154	0.2474	0.9302
0.0824	78.0	156	0.2375	0.9302
0.0824	79.0	158	0.2389	0.9302
0.0691	80.0	160	0.2371	0.9302
0.0691	81.0	162	0.2393	0.9302
0.0691	82.0	164	0.2523	0.9070
0.0691	83.0	166	0.2677	0.8837
0.0691	84.0	168	0.2945	0.8837
0.0638	85.0	170	0.3245	0.8605
0.0638	86.0	172	0.2960	0.8837
0.0638	87.0	174	0.2658	0.9302
0.0638	88.0	176	0.2614	0.9302
0.0638	89.0	178	0.2613	0.9302
0.0705	90.0	180	0.2549	0.9302
0.0705	91.0	182	0.2510	0.9302
0.0705	92.0	184	0.2514	0.9302
0.0705	93.0	186	0.2522	0.9302
0.0705	94.0	188	0.2504	0.9070
0.0666	95.0	190	0.2459	0.9302
0.0666	96.0	192	0.2424	0.9302
0.0666	97.0	194	0.2428	0.9302
0.0666	98.0	196	0.2451	0.9302
0.0666	99.0	198	0.2471	0.9302
0.0566	100.0	200	0.2482	0.9302

Framework versions

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