This model is a fine-tuned version of timm/efficientvit_b0.r224_in1k on the mbiarreta/ena24_MD dataset. It achieves the following results on the evaluation set:

Loss: 2.2599
Accuracy: 0.4824
F1: 0.4440
Acc American black bear: 0.9375
Acc American crow: 0.5781
Acc Bird: 0.3913
Acc Bobcat: 0.4118
Acc Chicken: 0.12
Acc Coyote: 0.2857
Acc Dog: 0.3462
Acc Domestic cat: 0.14
Acc Eastern chipmunk: 0.8438
Acc Eastern cottontail: 0.5294
Acc Eastern fox squirrel: 0.6
Acc Eastern gray squirrel: 0.4688
Acc Grey fox: 0.1364
Acc Horse: 0.0
Acc Northern raccoon: 0.3333
Acc Red fox: 0.5
Acc Striped skunk: 0.6667
Acc Virginia opossum: 0.8219
Acc White Tailed Deer: 0.7692
Acc Wild turkey: 0.3953
Acc Woodchuck: 0.4286
F1 American black bear: 0.6406
F1 American crow: 0.7255
F1 Bird: 0.3
F1 Bobcat: 0.2718
F1 Chicken: 0.1314
F1 Coyote: 0.4
F1 Dog: 0.45
F1 Domestic cat: 0.1157
F1 Eastern chipmunk: 0.8060
F1 Eastern cottontail: 0.4615
F1 Eastern fox squirrel: 0.4773
F1 Eastern gray squirrel: 0.5455
F1 Grey fox: 0.1690
F1 Horse: 0.0
F1 Northern raccoon: 0.3846
F1 Red fox: 0.4773
F1 Striped skunk: 0.625
F1 Virginia opossum: 0.8451
F1 White Tailed Deer: 0.5556
F1 Wild turkey: 0.4928
F1 Woodchuck: 0.45

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: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 7
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Acc American black bear	Acc American crow	Acc Bird	Acc Bobcat	Acc Chicken	Acc Coyote	Acc Dog	Acc Domestic cat	Acc Eastern chipmunk	Acc Eastern cottontail	Acc Eastern fox squirrel	Acc Eastern gray squirrel	Acc Grey fox	Acc Northern raccoon	Acc Red fox	Acc Striped skunk	Acc Virginia opossum	Acc White Tailed Deer	Acc Wild turkey	Acc Woodchuck	F1 American black bear	F1 American crow	F1 Bird	F1 Bobcat	F1 Chicken	F1 Coyote	F1 Dog	F1 Domestic cat	F1 Eastern chipmunk	F1 Eastern cottontail	F1 Eastern fox squirrel	F1 Eastern gray squirrel	F1 Grey fox	F1 Northern raccoon	F1 Red fox	F1 Striped skunk	F1 Virginia opossum	F1 White Tailed Deer	F1 Wild turkey	F1 Woodchuck
1.7495	0.1259	100	2.8202	0.1963	0.1387	0.8125	0.125	0.0	0.0294	0.0	0.1429	0.0385	0.0	0.0625	0.3235	0.1429	0.7812	0.1818	0.0667	0.1190	0.2333	0.4247	0.0256	0.0233	0.0	0.3284	0.2078	0.0	0.0571	0.0	0.1639	0.0732	0.0	0.0635	0.3385	0.1818	0.1493	0.1404	0.1176	0.1786	0.28	0.5536	0.0455	0.0333	0.0
1.3081	0.2519	200	2.7355	0.2266	0.1655	0.9062	0.1406	0.0	0.0	0.1467	0.5143	0.1282	0.0	0.0	0.0882	0.2286	0.625	0.25	0.0	0.2619	0.0	0.2055	0.1282	0.3256	0.0476	0.4394	0.2323	0.0	0.0	0.1028	0.5294	0.1389	0.0	0.0	0.1395	0.2963	0.1762	0.2115	0.0	0.2933	0.0	0.3297	0.0769	0.4179	0.0909
1.2023	0.3778	300	2.8526	0.2725	0.2011	0.7708	0.1953	0.0435	0.0294	0.0267	0.0	0.0769	0.04	0.6875	0.0	0.2857	0.125	0.0909	0.2	0.3571	0.6667	0.9589	0.0	0.2326	0.3333	0.4625	0.3205	0.0541	0.0488	0.0392	0.0	0.1224	0.0563	0.6471	0.0	0.3333	0.1333	0.0762	0.0968	0.3614	0.5	0.5224	0.0	0.3333	0.1148
0.9314	0.5038	400	2.6854	0.2734	0.1956	0.6042	0.5	0.0	0.0294	0.0	0.2857	0.0256	0.02	0.0	0.1765	0.4	0.0	0.1591	0.0	0.0476	0.2333	0.6301	0.7692	0.7442	0.0	0.4854	0.5	0.0	0.0488	0.0	0.4167	0.0444	0.0286	0.0	0.1379	0.5	0.0	0.1443	0.0	0.0755	0.3415	0.736	0.5	0.1488	0.0
0.8631	0.6297	500	2.3217	0.4033	0.3350	0.8333	0.6562	0.6087	0.1765	0.1733	0.1143	0.1667	0.14	0.6875	0.1176	0.4286	0.0312	0.2727	0.0333	0.5714	0.4333	0.8493	0.5641	0.1628	0.4286	0.6584	0.6364	0.2979	0.2182	0.1287	0.1702	0.2364	0.14	0.7719	0.2105	0.5357	0.0571	0.1935	0.0444	0.48	0.4815	0.7848	0.4490	0.2642	0.2769
0.7006	0.7557	600	2.4787	0.3877	0.2936	0.5208	0.9219	0.3478	0.3529	0.0933	0.7714	0.2051	0.0	0.875	0.1765	0.5429	0.0312	0.2273	0.0	0.0714	0.1333	0.8767	0.1026	0.2558	0.4286	0.5714	0.8369	0.3404	0.1132	0.1217	0.2547	0.3299	0.0	0.6022	0.2449	0.6032	0.0606	0.1869	0.0	0.1224	0.2162	0.7619	0.1481	0.3607	0.2903
1.0206	0.8816	700	2.5881	0.3867	0.3037	0.9375	0.6719	0.3913	0.2059	0.0667	0.4857	0.0385	0.04	0.5312	0.0294	0.2286	0.0938	0.4091	0.1	0.4762	0.1333	0.8904	0.5385	0.2558	0.2857	0.4891	0.7783	0.3	0.14	0.0943	0.3617	0.0698	0.0571	0.6939	0.0541	0.3478	0.1667	0.2118	0.125	0.4494	0.2353	0.6952	0.5185	0.3860	0.2034
0.6393	1.0076	800	2.5619	0.3906	0.3520	0.4062	0.3125	0.3478	0.3235	0.04	0.0286	0.7821	0.04	0.8438	0.2353	0.7143	0.4375	0.1591	0.5	0.5714	0.5333	0.8356	0.4359	0.3023	0.3810	0.4815	0.4651	0.3265	0.1982	0.075	0.0556	0.5810	0.0360	0.7297	0.2353	0.3125	0.4444	0.1818	0.4348	0.3310	0.6038	0.8472	0.4198	0.3133	0.32
0.4724	1.1335	900	2.6886	0.3359	0.2632	0.8646	0.3281	0.3478	0.0588	0.04	0.0	0.0513	0.0	0.6875	0.5	0.1143	0.0938	0.3409	0.3	0.5238	0.0667	0.9178	0.3846	0.5814	0.0476	0.4689	0.4941	0.3137	0.1053	0.0492	0.0	0.0952	0.0	0.6984	0.2957	0.1951	0.1463	0.2069	0.3214	0.5116	0.125	0.8816	0.3125	0.2146	0.0909
0.5096	1.2594	1000	2.7225	0.3701	0.3186	0.375	0.7188	0.3043	0.2941	0.0267	0.0857	0.1154	0.22	0.5938	0.6471	0.6857	0.5938	0.0682	0.1	0.4286	0.4333	0.9178	0.0	0.2791	0.4286	0.5333	0.7797	0.2979	0.1887	0.0482	0.0645	0.2069	0.1095	0.6786	0.2558	0.4571	0.4	0.1224	0.1091	0.4557	0.5306	0.7403	0.0	0.4211	0.2903
0.3412	1.3854	1100	2.8234	0.3457	0.3339	0.4375	0.2891	0.6957	0.0882	0.04	0.4857	0.0641	0.02	0.6875	0.5882	0.6286	0.8125	0.1364	0.1	0.0952	0.6333	0.6164	0.9744	0.3953	0.3810	0.5350	0.4431	0.2462	0.1071	0.0368	0.5763	0.1176	0.0333	0.7719	0.3448	0.3411	0.2781	0.15	0.1154	0.16	0.5758	0.7317	0.4720	0.5312	0.4444
0.4115	1.5113	1200	2.4271	0.4746	0.4125	0.9271	0.5547	0.6522	0.1176	0.08	0.6286	0.2436	0.06	0.9375	0.2647	0.6571	0.3438	0.2727	0.4	0.3333	0.6667	0.9315	0.6410	0.5349	0.4762	0.712	0.7065	0.3297	0.1455	0.0930	0.5116	0.3220	0.0833	0.6522	0.3273	0.5897	0.4151	0.2759	0.3871	0.3733	0.6061	0.7120	0.5319	0.5610	0.3279
0.6126	1.6373	1300	2.4840	0.4473	0.3804	0.8125	0.5234	0.6087	0.0294	0.0667	0.3143	0.3846	0.08	0.9688	0.2059	0.6857	0.1562	0.1591	0.3667	0.3333	0.7333	0.9178	0.5385	0.6744	0.4762	0.7290	0.6634	0.3146	0.0385	0.0877	0.3056	0.4615	0.0920	0.7126	0.3182	0.4706	0.2703	0.1443	0.3056	0.3733	0.352	0.8758	0.3784	0.6304	0.4651
0.3078	1.7632	1400	2.2599	0.4824	0.4440	0.9375	0.5781	0.3913	0.4118	0.12	0.2857	0.3462	0.14	0.8438	0.5294	0.6	0.4688	0.1364	0.3333	0.5	0.6667	0.8219	0.7692	0.3953	0.4286	0.6406	0.7255	0.3	0.2718	0.1314	0.4	0.45	0.1157	0.8060	0.4615	0.4773	0.5455	0.1690	0.3846	0.4773	0.625	0.8451	0.5556	0.4928	0.45
0.3732	1.8892	1500	2.6001	0.5088	0.4613	0.7708	0.6328	0.9130	0.2059	0.0267	0.3714	0.6282	0.08	0.9062	0.2941	0.6	0.5625	0.0909	0.4	0.6905	0.6667	0.9178	0.7179	0.5349	0.4286	0.7150	0.7570	0.35	0.1795	0.0412	0.4727	0.6405	0.0879	0.8169	0.4	0.6	0.6545	0.1111	0.2857	0.5043	0.6349	0.8535	0.3972	0.6389	0.5455
0.2557	2.0151	1600	2.9190	0.3965	0.3423	0.9271	0.5234	0.2609	0.1176	0.0533	0.5143	0.1282	0.0	0.6562	0.2059	0.2857	0.0	0.3182	0.3333	0.4048	0.4667	0.8493	0.7692	0.3256	0.4286	0.4623	0.6802	0.2727	0.0851	0.0816	0.5143	0.1786	0.0	0.7778	0.2745	0.4255	0.0	0.1918	0.2941	0.3953	0.3889	0.8671	0.48	0.4179	0.4
0.2271	2.1411	1700	2.7502	0.4619	0.4165	0.9271	0.6094	0.7826	0.0294	0.2	0.2571	0.0513	0.12	0.7188	0.3235	0.5714	0.1875	0.2955	0.4333	0.4048	0.5667	0.9041	0.7179	0.6512	0.5238	0.6593	0.7027	0.4337	0.0513	0.1523	0.2727	0.0833	0.1132	0.7797	0.4	0.6780	0.3158	0.25	0.5	0.4928	0.5484	0.8098	0.5437	0.6222	0.3385
0.1942	2.2670	1800	2.8448	0.4697	0.4437	0.9479	0.4297	0.7391	0.5	0.1333	0.3143	0.3846	0.06	0.6875	0.1765	0.6	0.3438	0.2045	0.6	0.3333	0.8333	0.8493	0.5641	0.5581	0.6190	0.5322	0.5759	0.4416	0.425	0.1439	0.3014	0.5172	0.0698	0.7586	0.2609	0.6774	0.4889	0.2687	0.3303	0.4667	0.5263	0.8794	0.55	0.6316	0.4727
0.1128	2.3929	1900	2.8647	0.4980	0.4378	0.9062	0.7891	0.9130	0.2941	0.0533	0.3143	0.2308	0.04	0.875	0.4706	0.6286	0.4062	0.1591	0.3	0.4524	0.7333	0.9452	0.2564	0.7209	0.4762	0.6797	0.8632	0.4038	0.2128	0.0734	0.2444	0.3462	0.0556	0.8	0.5	0.6027	0.5652	0.1538	0.3273	0.4810	0.7719	0.8961	0.2632	0.4882	0.4651
0.1483	2.5189	2000	2.6665	0.5322	0.4788	0.9271	0.7031	0.8261	0.4706	0.1067	0.5429	0.3590	0.04	0.9062	0.4118	0.6286	0.7188	0.2273	0.1667	0.5238	0.8	0.9726	0.4872	0.5814	0.4762	0.7355	0.7930	0.4176	0.3902	0.1176	0.6129	0.4746	0.0597	0.7838	0.4746	0.6027	0.6133	0.1852	0.2381	0.5116	0.7385	0.7889	0.4524	0.5882	0.4762
0.4629	2.6448	2100	2.8979	0.5068	0.4758	0.9479	0.5859	0.7391	0.2647	0.04	0.4857	0.3333	0.02	0.75	0.5	0.7429	0.5938	0.4318	0.3667	0.4762	0.6333	0.8767	0.6410	0.5349	0.6190	0.6341	0.7317	0.4	0.24	0.0522	0.5667	0.4771	0.0241	0.8421	0.3542	0.7027	0.6909	0.3423	0.3793	0.5634	0.7037	0.8533	0.5	0.5823	0.3514
0.175	2.7708	2200	2.5847	0.5479	0.4691	0.8438	0.9297	0.7826	0.0882	0.08	0.2571	0.7949	0.12	0.7812	0.4706	0.6857	0.7188	0.25	0.4667	0.4762	0.3333	0.9041	0.4359	0.4651	0.5238	0.7826	0.9297	0.3673	0.1	0.1091	0.3673	0.6966	0.1143	0.8065	0.4384	0.7273	0.6389	0.2821	0.3836	0.5333	0.4762	0.7857	0.3333	0.5556	0.4231
0.2218	2.8967	2300	2.7785	0.5400	0.4982	0.8646	0.6797	0.6957	0.3529	0.16	0.6857	0.4872	0.04	0.8438	0.3529	0.6857	0.625	0.2045	0.4	0.4286	0.8667	0.8767	0.9231	0.4884	0.4762	0.7793	0.7982	0.2783	0.3333	0.1387	0.7059	0.5468	0.0702	0.7606	0.4528	0.6857	0.5634	0.2609	0.375	0.4932	0.5977	0.8649	0.5255	0.6269	0.6061
0.0857	3.0227	2400	2.8899	0.5527	0.4934	0.9792	0.8359	0.6957	0.3824	0.0667	0.6571	0.5769	0.02	0.8438	0.2941	0.6857	0.5312	0.1591	0.4	0.5714	0.5333	0.8630	0.7949	0.4419	0.5714	0.6225	0.8807	0.3951	0.4062	0.0909	0.6667	0.5769	0.0328	0.8710	0.4348	0.5275	0.6182	0.1667	0.4444	0.4706	0.6531	0.875	0.5962	0.5	0.5333
0.2255	3.1486	2500	2.7107	0.5566	0.4745	0.9271	0.7578	0.3478	0.2941	0.1467	0.8286	0.8462	0.04	0.9688	0.5294	0.6857	0.4688	0.4318	0.2667	0.4762	0.6	0.9589	0.2051	0.3488	0.5714	0.712	0.8326	0.2759	0.3636	0.2056	0.5686	0.7135	0.0606	0.8052	0.5538	0.5217	0.5263	0.3248	0.2963	0.5063	0.6545	0.8537	0.2286	0.5085	0.4528
0.2458	3.2746	2600	2.9639	0.5176	0.4577	0.9479	0.6172	0.4348	0.3824	0.1333	0.8	0.5641	0.0	0.8438	0.2059	0.6857	0.3438	0.2955	0.1667	0.5	0.7667	0.9178	0.7692	0.4186	0.4286	0.6254	0.7453	0.2899	0.3059	0.1460	0.6914	0.5906	0.0	0.8182	0.2979	0.6316	0.5116	0.2364	0.25	0.5060	0.6389	0.8590	0.5217	0.5373	0.4091
0.0796	3.4005	2700	2.8778	0.5635	0.5053	0.9375	0.7969	0.7826	0.5	0.1067	0.4286	0.4615	0.02	0.9375	0.5882	0.7143	0.5625	0.2273	0.3333	0.5952	0.7333	0.8904	0.9231	0.4651	0.4286	0.7563	0.8755	0.3711	0.3366	0.1231	0.5660	0.576	0.0282	0.8	0.5714	0.6173	0.7059	0.2985	0.3922	0.5	0.6377	0.8784	0.5669	0.5714	0.4390
0.2234	3.5264	2800	3.0540	0.5576	0.4922	0.9375	0.7188	0.8261	0.2941	0.0667	0.7429	0.6282	0.02	0.9062	0.5294	0.6571	0.75	0.3636	0.2667	0.4048	0.6667	0.8904	0.9231	0.3488	0.3810	0.6844	0.8070	0.3585	0.2532	0.1031	0.7536	0.6853	0.0370	0.8406	0.4444	0.6667	0.6234	0.3721	0.3019	0.4474	0.6897	0.8075	0.5625	0.4762	0.4211
0.0656	3.6524	2900	2.9764	0.5684	0.4927	0.9375	0.8125	0.6087	0.3824	0.0533	0.3714	0.6795	0.06	0.9688	0.5294	0.6286	0.5	0.2273	0.4333	0.6429	0.5	0.9315	0.8205	0.5814	0.5238	0.7171	0.8703	0.35	0.3714	0.0988	0.5	0.7518	0.0845	0.7848	0.4737	0.7097	0.5818	0.2564	0.3824	0.5143	0.6	0.8395	0.4571	0.5952	0.4074
0.2256	3.7783	3000	3.1337	0.5420	0.4856	0.9375	0.6953	0.5652	0.5	0.0667	0.5714	0.4231	0.02	0.8438	0.4706	0.6571	0.7188	0.25	0.4	0.5	0.7	0.9726	0.8718	0.4651	0.3810	0.6897	0.8203	0.3291	0.3469	0.0847	0.6061	0.5116	0.0286	0.7714	0.4638	0.6479	0.6216	0.2933	0.3380	0.5060	0.7119	0.8452	0.6296	0.5797	0.3721
0.0854	3.9043	3100	3.0200	0.5732	0.5050	0.8854	0.875	0.7826	0.3235	0.0667	0.4	0.5	0.1	0.875	0.3824	0.7143	0.7188	0.25	0.4333	0.7143	0.4333	0.9726	0.8718	0.6512	0.4286	0.7798	0.9180	0.3871	0.3333	0.0980	0.5091	0.5735	0.1190	0.8485	0.4062	0.6849	0.7667	0.2683	0.3824	0.5941	0.5778	0.8554	0.4892	0.6667	0.3462
0.0079	4.0302	3200	2.8893	0.5693	0.5022	0.9375	0.8438	0.6957	0.3824	0.0667	0.7429	0.3846	0.02	0.9688	0.4412	0.6857	0.5938	0.2273	0.4333	0.6190	0.6667	0.9589	0.6667	0.7209	0.4286	0.7531	0.8963	0.4324	0.3133	0.0826	0.5591	0.5172	0.0299	0.7949	0.5	0.6234	0.6667	0.2353	0.3824	0.5778	0.5970	0.8537	0.5843	0.6966	0.45
0.1127	4.1562	3300	3.1284	0.5684	0.5108	0.9375	0.7422	0.7826	0.2941	0.1067	0.6286	0.5897	0.06	0.9375	0.5294	0.7143	0.6875	0.1364	0.4	0.5952	0.6333	0.9178	0.8462	0.4884	0.5714	0.7287	0.8444	0.3871	0.25	0.1495	0.6286	0.6434	0.0779	0.8333	0.5217	0.6579	0.7586	0.1765	0.3529	0.5102	0.7308	0.8758	0.5739	0.6176	0.4068
0.0115	4.2821	3400	3.3688	0.5391	0.4841	0.9271	0.7422	0.8261	0.3824	0.0933	0.8286	0.1923	0.0	0.8125	0.2353	0.6571	0.6562	0.2273	0.3333	0.6190	0.6	0.9726	0.8462	0.6279	0.5714	0.6473	0.8370	0.4222	0.3171	0.1228	0.6667	0.3093	0.0	0.8525	0.3404	0.7077	0.7241	0.1786	0.3704	0.5253	0.6102	0.8712	0.6286	0.6667	0.3692
0.0615	4.4081	3500	3.1003	0.5771	0.5119	0.9271	0.8047	0.7826	0.3235	0.1067	0.7714	0.6154	0.02	0.9375	0.4412	0.7429	0.625	0.25	0.4	0.5714	0.6667	0.9589	0.8205	0.3953	0.4286	0.7177	0.8766	0.3789	0.3548	0.1524	0.7606	0.6115	0.0333	0.9091	0.4918	0.5417	0.7018	0.2472	0.375	0.5	0.7407	0.8696	0.6038	0.5312	0.3529
0.0625	4.5340	3600	3.1293	0.5693	0.5022	0.9271	0.75	0.7391	0.5	0.08	0.5714	0.5897	0.02	0.9688	0.5588	0.7429	0.6562	0.1591	0.4	0.6190	0.6333	0.9726	0.8974	0.3721	0.3810	0.6768	0.8421	0.4048	0.3736	0.1290	0.6349	0.6866	0.0328	0.8052	0.5588	0.5417	0.7241	0.1892	0.4528	0.4815	0.7308	0.8765	0.6140	0.5	0.2909
0.0079	4.6599	3700	3.1365	0.5801	0.5172	0.8854	0.8828	0.8696	0.3529	0.08	0.6857	0.4744	0.04	0.9688	0.5588	0.6857	0.7812	0.2727	0.3333	0.4286	0.7333	0.9178	0.9231	0.4651	0.5238	0.7113	0.8863	0.4124	0.3288	0.1017	0.75	0.6066	0.0615	0.8267	0.5135	0.6076	0.7042	0.2727	0.3390	0.4615	0.6984	0.8816	0.6667	0.5634	0.4681
0.0124	4.7859	3800	3.0567	0.5732	0.5129	0.9375	0.7969	0.7826	0.2941	0.0933	0.7143	0.4872	0.04	0.9375	0.5588	0.7429	0.6875	0.1818	0.4333	0.6667	0.6333	0.9041	0.9231	0.4651	0.3810	0.7143	0.8681	0.4390	0.2740	0.1207	0.7463	0.5938	0.0625	0.8696	0.5672	0.5652	0.7333	0.1882	0.4727	0.5333	0.6441	0.8516	0.6154	0.5634	0.3478
0.0592	4.9118	3900	2.9430	0.6172	0.5467	0.9583	0.8438	0.8261	0.3235	0.1467	0.7429	0.6538	0.06	0.9688	0.5294	0.7429	0.6875	0.1136	0.4667	0.6905	0.8	0.9589	0.9487	0.5814	0.4762	0.7667	0.9	0.4634	0.3385	0.1930	0.7536	0.7286	0.0952	0.8267	0.5455	0.6842	0.7333	0.1449	0.3457	0.5859	0.7385	0.8917	0.6325	0.7042	0.4082
0.0176	5.0378	4000	3.1490	0.5967	0.5418	0.9271	0.7266	0.7391	0.5	0.1333	0.7429	0.7308	0.04	0.9375	0.5882	0.7429	0.7188	0.1818	0.3667	0.6667	0.6	0.9589	0.9487	0.4651	0.4286	0.7149	0.8341	0.3778	0.4533	0.1613	0.7647	0.7651	0.0656	0.8696	0.5128	0.7123	0.7302	0.1905	0.4314	0.5385	0.7059	0.9211	0.5827	0.5970	0.45
0.0219	5.1637	4100	3.0442	0.6045	0.5410	0.9271	0.8984	0.8696	0.4412	0.1467	0.7714	0.5769	0.06	0.9062	0.5588	0.7429	0.7812	0.2727	0.3333	0.5238	0.6333	0.9589	0.7949	0.4884	0.4762	0.7479	0.8949	0.4494	0.3896	0.1818	0.7606	0.6870	0.0909	0.8286	0.5135	0.6582	0.7246	0.2667	0.3571	0.5176	0.7308	0.8917	0.5962	0.6087	0.4651
0.0353	5.2897	4200	3.1909	0.5908	0.5274	0.9583	0.8125	0.7391	0.4118	0.1333	0.6	0.5513	0.06	0.9062	0.5294	0.6857	0.75	0.1818	0.4	0.5952	0.6667	0.9589	0.9487	0.6047	0.3810	0.7603	0.8889	0.3736	0.4	0.1587	0.6885	0.6466	0.0923	0.8286	0.5625	0.6667	0.7059	0.1975	0.3529	0.4902	0.7018	0.9150	0.6325	0.65	0.3636
0.0041	5.4156	4300	3.2985	0.5918	0.5253	0.9479	0.875	0.8261	0.6765	0.12	0.5143	0.4615	0.0	0.875	0.5588	0.7429	0.6875	0.1364	0.3667	0.7619	0.6333	0.9452	0.9487	0.4884	0.3810	0.7054	0.9218	0.3838	0.5542	0.1552	0.6102	0.6	0.0	0.875	0.6441	0.6667	0.6567	0.1739	0.4	0.5333	0.6667	0.8734	0.6116	0.6	0.4
0.1064	5.5416	4400	3.2297	0.6074	0.5383	0.9375	0.8516	0.7826	0.5294	0.12	0.4571	0.6923	0.08	0.9062	0.5588	0.7429	0.8125	0.0909	0.4333	0.7857	0.6	0.9589	0.9487	0.4651	0.4286	0.7143	0.9046	0.3673	0.4337	0.18	0.5926	0.7660	0.1143	0.8788	0.5672	0.7123	0.7647	0.1194	0.4262	0.5641	0.6792	0.8861	0.6271	0.5882	0.4186
0.0027	5.6675	4500	3.2256	0.6064	0.5373	0.9583	0.875	0.7826	0.5588	0.1467	0.5143	0.6282	0.02	0.9688	0.5294	0.7429	0.7188	0.2045	0.4333	0.5714	0.6	0.9589	1.0	0.5116	0.3810	0.7541	0.9180	0.4045	0.3838	0.1897	0.6545	0.7313	0.0339	0.8732	0.5806	0.5778	0.7419	0.2432	0.3768	0.5	0.6792	0.9211	0.624	0.6377	0.4571
0.0009	5.7935	4600	3.2828	0.5957	0.5257	0.9375	0.8672	0.7391	0.3529	0.1467	0.6	0.6410	0.04	0.9375	0.5294	0.7714	0.5625	0.2045	0.5	0.6190	0.6333	0.9589	0.9487	0.4419	0.3810	0.7229	0.9174	0.3908	0.3582	0.1849	0.6774	0.7143	0.0656	0.9091	0.5538	0.6136	0.6667	0.2278	0.4412	0.5	0.6667	0.8805	0.6167	0.5846	0.3478
0.0148	5.9194	4700	3.2196	0.6152	0.5491	0.9271	0.9062	0.8261	0.5	0.1467	0.6571	0.6026	0.08	0.9062	0.5588	0.7429	0.7812	0.1591	0.3667	0.6667	0.8	0.9452	0.9231	0.5116	0.3810	0.7807	0.928	0.3918	0.4595	0.1774	0.7188	0.6861	0.1176	0.8657	0.6129	0.6753	0.7143	0.2	0.3607	0.5437	0.7619	0.8790	0.6102	0.6377	0.4103
0.021	6.0453	4800	3.2310	0.6113	0.5423	0.9583	0.9062	0.8261	0.5294	0.1467	0.5429	0.6026	0.06	0.9375	0.5294	0.7429	0.75	0.2045	0.3667	0.6667	0.6667	0.9452	0.9744	0.4651	0.3810	0.736	0.9206	0.4	0.4235	0.1964	0.6441	0.6963	0.0952	0.8955	0.5455	0.6582	0.8	0.24	0.3667	0.5490	0.7018	0.8679	0.6726	0.5970	0.3810
0.0377	6.1713	4900	3.2332	0.6240	0.5569	0.9271	0.9141	0.8261	0.5	0.1333	0.6286	0.6410	0.06	0.9688	0.5882	0.7429	0.8438	0.1818	0.3667	0.7143	0.7	0.9315	0.9744	0.5581	0.3810	0.7639	0.9323	0.4130	0.4096	0.1754	0.7097	0.7092	0.0984	0.9254	0.6061	0.6933	0.7941	0.2192	0.3729	0.5405	0.75	0.8718	0.6441	0.6667	0.4
0.0004	6.2972	5000	3.2382	0.6182	0.5485	0.9479	0.9141	0.8696	0.4118	0.1333	0.6571	0.6154	0.08	0.9375	0.5588	0.7143	0.7188	0.2273	0.4	0.6429	0.6333	0.9589	1.0	0.5581	0.3810	0.7745	0.9323	0.4124	0.3544	0.1802	0.6970	0.7111	0.1290	0.8824	0.5846	0.6410	0.7797	0.2439	0.4138	0.5347	0.7037	0.8805	0.65	0.6667	0.3478
0.0099	6.4232	5100	3.2434	0.6133	0.5455	0.9479	0.8828	0.8696	0.4706	0.1467	0.6286	0.6154	0.04	0.9062	0.5882	0.7429	0.7812	0.1818	0.4333	0.6667	0.7333	0.9589	0.9231	0.4651	0.3810	0.7552	0.9150	0.4124	0.4156	0.1880	0.6567	0.7111	0.0678	0.8788	0.6061	0.6420	0.7812	0.2078	0.4194	0.5437	0.7719	0.8805	0.6486	0.6061	0.3478
0.0009	6.5491	5200	3.2188	0.6113	0.5396	0.9167	0.8984	0.8696	0.4412	0.12	0.5429	0.6410	0.06	0.9375	0.5882	0.7143	0.75	0.1364	0.4	0.6905	0.6667	0.9589	0.9744	0.5814	0.3810	0.7928	0.92	0.4040	0.3947	0.1607	0.6230	0.7246	0.0938	0.8824	0.5882	0.6098	0.7742	0.1714	0.3934	0.5321	0.7407	0.8642	0.6230	0.6667	0.3721
0.0066	6.6751	5300	3.2749	0.6113	0.5482	0.9583	0.8516	0.8696	0.4706	0.1333	0.6857	0.6026	0.06	0.9062	0.5588	0.7143	0.75	0.1591	0.4333	0.6667	0.6333	0.9726	0.9231	0.6047	0.3810	0.7731	0.8934	0.4124	0.4103	0.1653	0.7164	0.6912	0.0968	0.8788	0.6032	0.6757	0.7869	0.1867	0.4062	0.5385	0.6909	0.8659	0.6372	0.7123	0.3721
0.0006	6.8010	5400	3.2724	0.6006	0.5389	0.9375	0.8203	0.8696	0.4412	0.1467	0.6857	0.6154	0.04	0.9062	0.5588	0.7143	0.7812	0.1818	0.3667	0.6429	0.6	0.9589	0.9231	0.5581	0.3810	0.7407	0.875	0.4082	0.375	0.176	0.6957	0.7111	0.0645	0.8657	0.5758	0.6757	0.7937	0.2078	0.3860	0.5243	0.6792	0.875	0.6486	0.6761	0.3636
0.0006	6.9270	5500	3.2286	0.6201	0.5505	0.9479	0.8906	0.8261	0.5294	0.1333	0.6286	0.6538	0.06	0.9688	0.5882	0.7429	0.7812	0.1136	0.4333	0.6667	0.6333	0.9589	0.9487	0.5814	0.3810	0.7845	0.9194	0.4086	0.4337	0.1739	0.6875	0.7338	0.0984	0.9118	0.5797	0.6842	0.7937	0.1429	0.4262	0.5185	0.7037	0.8642	0.6379	0.6849	0.3721

Framework versions

Transformers 4.52.2
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

emiliaviq/efficientvit-ena24_MD

efficientvit-ena24

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

emiliaviq/
efficientvit-ena24_MD