LamaAldakhil/SL-CvT

来源：🤗有风险🏴‍☠️ 救援者：isiliconimage-classificationapache-2.079 MBother✓ 9 个校验和1,277 天前更新

SL-CvT

This model is a fine-tuned version of microsoft/cvt-13 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.3430
F1: 0.9298
Roc Auc: 0.9777
Accuracy: 0.9317

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	F1	Roc Auc	Accuracy
1.2379	1.0	60	1.0716	0.6422	0.7323	0.7246
1.0186	2.0	120	0.8477	0.6425	0.7879	0.7293
0.9433	3.0	180	0.7473	0.7060	0.8454	0.7538
0.8644	4.0	240	0.6831	0.7188	0.8696	0.7663
0.7985	5.0	300	0.6420	0.7409	0.8943	0.7799
0.7322	6.0	360	0.5713	0.7886	0.9196	0.8101
0.725	7.0	420	0.5311	0.7989	0.9324	0.8190
0.6529	8.0	480	0.5246	0.7852	0.9404	0.8117
0.6224	9.0	540	0.4598	0.8282	0.9517	0.8440
0.6315	10.0	600	0.4363	0.8457	0.9585	0.8529
0.5651	11.0	660	0.4437	0.8323	0.9564	0.8503
0.574	12.0	720	0.4003	0.8531	0.9617	0.8638
0.5269	13.0	780	0.3901	0.8676	0.9671	0.8722
0.5138	14.0	840	0.3984	0.8607	0.9685	0.8732
0.4839	15.0	900	0.3763	0.8683	0.9701	0.8769
0.463	16.0	960	0.3398	0.8837	0.9718	0.8894
0.4767	17.0	1020	0.3293	0.8846	0.9738	0.8915
0.4985	18.0	1080	0.3350	0.8852	0.9763	0.8863
0.4657	19.0	1140	0.3369	0.8872	0.9746	0.8951
0.4514	20.0	1200	0.3213	0.8880	0.9750	0.8925
0.4207	21.0	1260	0.3175	0.8943	0.9771	0.8978
0.4522	22.0	1320	0.3229	0.8970	0.9767	0.8983
0.4328	23.0	1380	0.3121	0.8948	0.9791	0.8978
0.3942	24.0	1440	0.3111	0.8993	0.9765	0.9030
0.4414	25.0	1500	0.3062	0.9032	0.9763	0.9061
0.3608	26.0	1560	0.3099	0.8997	0.9787	0.9014
0.3729	27.0	1620	0.3050	0.9029	0.9783	0.9082
0.393	28.0	1680	0.2970	0.9090	0.9797	0.9108
0.402	29.0	1740	0.2986	0.9087	0.9793	0.9113
0.3697	30.0	1800	0.3384	0.8968	0.9769	0.9025
0.3502	31.0	1860	0.3035	0.9058	0.9789	0.9103
0.3653	32.0	1920	0.3127	0.9024	0.9788	0.9025
0.3898	33.0	1980	0.3222	0.9050	0.9778	0.9061
0.317	34.0	2040	0.3013	0.9124	0.9798	0.9139
0.3166	35.0	2100	0.3185	0.9095	0.9775	0.9134
0.3771	36.0	2160	0.3067	0.9049	0.9782	0.9066
0.3487	37.0	2220	0.2948	0.9118	0.9801	0.9134
0.3202	38.0	2280	0.2916	0.9168	0.9788	0.9186
0.3163	39.0	2340	0.3149	0.9141	0.9777	0.9155
0.3605	40.0	2400	0.2964	0.9192	0.9797	0.9207
0.3636	41.0	2460	0.3142	0.9111	0.9810	0.9134
0.3454	42.0	2520	0.3133	0.9111	0.9792	0.9113
0.3561	43.0	2580	0.3090	0.9073	0.9804	0.9077
0.3136	44.0	2640	0.3236	0.9144	0.9782	0.9176
0.3529	45.0	2700	0.3054	0.9175	0.9800	0.9202
0.2987	46.0	2760	0.2944	0.9222	0.9802	0.9233
0.2966	47.0	2820	0.3215	0.9201	0.9786	0.9233
0.3203	48.0	2880	0.3150	0.9219	0.9797	0.9244
0.2821	49.0	2940	0.3072	0.9273	0.9800	0.9291
0.2852	50.0	3000	0.3265	0.9155	0.9792	0.9176
0.3544	51.0	3060	0.3175	0.9150	0.9802	0.9150
0.3327	52.0	3120	0.3134	0.9222	0.9802	0.9244
0.2877	53.0	3180	0.3222	0.9154	0.9805	0.9165
0.3089	54.0	3240	0.3045	0.9248	0.9811	0.9259
0.2904	55.0	3300	0.3301	0.9175	0.9787	0.9186
0.2821	56.0	3360	0.3069	0.9206	0.9810	0.9218
0.321	57.0	3420	0.3209	0.9254	0.9800	0.9270
0.2995	58.0	3480	0.3281	0.9202	0.9802	0.9233
0.2683	59.0	3540	0.3263	0.9174	0.9802	0.9202
0.3021	60.0	3600	0.3484	0.9170	0.9788	0.9186
0.3262	61.0	3660	0.3270	0.9151	0.9807	0.9165
0.2329	62.0	3720	0.3280	0.9211	0.9807	0.9233
0.2935	63.0	3780	0.3296	0.9244	0.9807	0.9264
0.2856	64.0	3840	0.3323	0.9209	0.9811	0.9218
0.2829	65.0	3900	0.3390	0.9200	0.9802	0.9218
0.3044	66.0	3960	0.3324	0.9215	0.9799	0.9228
0.2767	67.0	4020	0.3496	0.9150	0.9778	0.9160
0.2936	68.0	4080	0.3378	0.9257	0.9790	0.9275
0.2884	69.0	4140	0.3493	0.9227	0.9790	0.9249
0.2906	70.0	4200	0.3408	0.9259	0.9794	0.9275
0.2542	71.0	4260	0.3559	0.9233	0.9769	0.9249
0.2557	72.0	4320	0.3481	0.9237	0.9779	0.9254
0.2266	73.0	4380	0.3518	0.9208	0.9781	0.9223
0.2771	74.0	4440	0.3544	0.9231	0.9776	0.9254
0.2747	75.0	4500	0.3469	0.9270	0.9780	0.9285
0.2443	76.0	4560	0.3513	0.9216	0.9767	0.9233
0.2859	77.0	4620	0.3456	0.9234	0.9771	0.9254
0.2677	78.0	4680	0.3474	0.9239	0.9780	0.9254
0.2492	79.0	4740	0.3513	0.9235	0.9778	0.9254
0.2532	80.0	4800	0.3524	0.9210	0.9773	0.9233
0.2646	81.0	4860	0.3529	0.9240	0.9784	0.9238
0.2842	82.0	4920	0.3433	0.9260	0.9777	0.9280
0.2872	83.0	4980	0.3584	0.9272	0.9771	0.9285
0.2678	84.0	5040	0.3430	0.9298	0.9777	0.9317
0.2705	85.0	5100	0.3534	0.9268	0.9777	0.9291
0.2605	86.0	5160	0.3574	0.9272	0.9777	0.9296
0.2572	87.0	5220	0.3426	0.9273	0.9781	0.9291
0.2646	88.0	5280	0.3472	0.9234	0.9789	0.9244
0.2831	89.0	5340	0.3433	0.9272	0.9779	0.9291
0.277	90.0	5400	0.3441	0.9263	0.9789	0.9280
0.2584	91.0	5460	0.3432	0.9236	0.9788	0.9249
0.2703	92.0	5520	0.3409	0.9248	0.9789	0.9259
0.2811	93.0	5580	0.3449	0.9215	0.9795	0.9228
0.2786	94.0	5640	0.3465	0.9260	0.9789	0.9280
0.267	95.0	5700	0.3472	0.9260	0.9791	0.9275
0.2695	96.0	5760	0.3500	0.9268	0.9786	0.9285
0.279	97.0	5820	0.3582	0.9249	0.9782	0.9270
0.2774	98.0	5880	0.3486	0.9251	0.9790	0.9270
0.2512	99.0	5940	0.3514	0.9287	0.9786	0.9306
0.2218	100.0	6000	0.3482	0.9269	0.9789	0.9285

Framework versions

Transformers 4.29.2
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3