efficientnet-b0

This model is a fine-tuned version of google/efficientnet-b0 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1329
• Accuracy: 0.9837
• Precision: 0.9907
• Recall: 0.9737
• F1: 0.9821
• Tp: 1595
• Tn: 1895
• Fp: 15
• Fn: 43

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: 0.0002
• train_batch_size: 256
• eval_batch_size: 256
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 110
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Tp	Tn	Fp	Fn
1.3349	0.1964	11	1.4004	0.5397	0.5013	0.5702	0.5336	934	981	929	704
1.1370	0.3929	22	1.1454	0.7514	0.6654	0.9286	0.7752	1521	1145	765	117
0.8613	0.5893	33	0.9204	0.8089	0.7277	0.9365	0.8190	1534	1336	574	104
0.6781	0.7857	44	0.7617	0.8351	0.7610	0.9371	0.8399	1535	1428	482	103
0.5845	0.9821	55	0.7031	0.8548	0.7829	0.9487	0.8579	1554	1479	431	84
0.5412	1.1786	66	0.8732	0.8024	0.7091	0.9701	0.8193	1589	1258	652	49
0.4811	1.375	77	0.4708	0.9228	0.8915	0.9481	0.9189	1553	1721	189	85
0.4485	1.5714	88	0.7378	0.8520	0.7740	0.9597	0.8569	1572	1451	459	66
0.4350	1.7679	99	0.3992	0.9377	0.9141	0.9548	0.9340	1564	1763	147	74
0.4226	1.9643	110	0.4571	0.9202	0.8787	0.9597	0.9174	1572	1693	217	66
0.3743	2.1607	121	0.3237	0.9405	0.9136	0.9621	0.9373	1576	1761	149	62
0.3571	2.3571	132	0.3736	0.9422	0.9144	0.9652	0.9391	1581	1762	148	57
0.3744	2.5536	143	0.2479	0.9715	0.9753	0.9628	0.9690	1577	1870	40	61
0.3674	2.75	154	0.2033	0.9766	0.9838	0.9652	0.9744	1581	1884	26	57
0.3061	2.9464	165	0.1885	0.9732	0.9789	0.9628	0.9708	1577	1876	34	61
0.3311	3.1429	176	0.1790	0.9741	0.9783	0.9652	0.9717	1581	1875	35	57
0.3647	3.3393	187	0.1867	0.9755	0.9784	0.9683	0.9733	1586	1875	35	52
0.3031	3.5357	198	0.5063	0.9188	0.8689	0.9707	0.9170	1590	1670	240	48
0.3186	3.7321	209	0.1682	0.9786	0.9821	0.9713	0.9767	1591	1881	29	47
0.3246	3.9286	220	0.2225	0.9727	0.9695	0.9713	0.9704	1591	1860	50	47
0.3421	4.125	231	0.2672	0.9631	0.9493	0.9719	0.9605	1592	1825	85	46
0.3318	4.3214	242	0.2246	0.9715	0.9677	0.9707	0.9692	1590	1857	53	48
0.2790	4.5179	253	0.1860	0.9760	0.9767	0.9713	0.9740	1591	1872	38	47
0.3365	4.7143	264	0.2379	0.9639	0.9467	0.9768	0.9615	1600	1820	90	38
0.2756	4.9107	275	0.2062	0.9673	0.9568	0.9731	0.9649	1594	1838	72	44
0.2819	5.1071	286	0.1483	0.9808	0.9968	0.9615	0.9789	1575	1905	5	63
0.2779	5.3036	297	0.1609	0.9797	0.9888	0.9670	0.9778	1584	1892	18	54
0.2755	5.5	308	0.1355	0.9839	0.9907	0.9744	0.9825	1596	1895	15	42
0.2827	5.6964	319	0.1778	0.9729	0.9673	0.9744	0.9708	1596	1856	54	42
0.2922	5.8929	330	0.1379	0.9828	0.9882	0.9744	0.9812	1596	1891	19	42
0.2901	6.0893	341	0.6696	0.9008	0.8342	0.9799	0.9012	1605	1591	319	33
0.2770	6.2857	352	0.1327	0.9837	0.9962	0.9683	0.9820	1586	1904	6	52
0.3000	6.4821	363	0.1351	0.9848	0.9956	0.9713	0.9833	1591	1903	7	47
0.3076	6.6786	374	0.1507	0.9811	0.9882	0.9707	0.9794	1590	1891	19	48
0.3077	6.875	385	0.1286	0.9853	0.9981	0.9701	0.9839	1589	1907	3	49
0.2734	7.0714	396	0.1406	0.9839	0.9859	0.9792	0.9825	1604	1887	23	34
0.2986	7.2679	407	0.1655	0.9822	0.9840	0.9774	0.9807	1601	1884	26	37
0.3002	7.4643	418	0.1377	0.9834	0.9876	0.9762	0.9819	1599	1890	20	39
0.2972	7.6607	429	0.2116	0.9684	0.9526	0.9805	0.9663	1606	1830	80	32
0.2796	7.8571	440	0.1383	0.9853	0.9932	0.9750	0.9840	1597	1899	11	41
0.2678	8.0536	451	0.1483	0.9825	0.9894	0.9725	0.9809	1593	1893	17	45
0.2526	8.25	462	0.1413	0.9831	0.9907	0.9725	0.9815	1593	1895	15	45
0.3135	8.4464	473	0.2835	0.9557	0.9323	0.9750	0.9531	1597	1794	116	41
0.2698	8.6429	484	0.1726	0.9758	0.9732	0.9744	0.9738	1596	1866	44	42
0.2768	8.8393	495	0.1527	0.9808	0.9840	0.9744	0.9791	1596	1884	26	42
0.2596	9.0357	506	0.1653	0.9783	0.9791	0.9737	0.9764	1595	1876	34	43
0.2720	9.2321	517	0.1347	0.9851	0.9919	0.9756	0.9837	1598	1897	13	40
0.2530	9.4286	528	0.1626	0.9789	0.9803	0.9737	0.9770	1595	1878	32	43
0.2987	9.625	539	0.1398	0.9834	0.9907	0.9731	0.9818	1594	1895	15	44
0.2643	9.8214	550	0.1329	0.9837	0.9907	0.9737	0.9821	1595	1895	15	43

Framework versions

• Transformers 5.2.0
• Pytorch 2.9.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.2