efficientvit-ena24-Original

emiliaviq/efficientvit-ena24-Original

efficientvit-ena24-Original is a image classification model from emiliaviq. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: timm_wrapper
Access: Public
Created: May 28, 2025
Updated: May 28, 2025
SHA: 2edef027

Base models

timm/efficientvit_b0.r224_in1k

Model card

From the Hugging Face model README

efficientvit-ena24-Original

This model is a fine-tuned version of timm/efficientvit_b0.r224_in1k on the Pamreth/ena24 dataset. It achieves the following results on the evaluation set:

Loss: 0.0992
Accuracy: 0.9786
F1: 0.9755
Acc American black bear: 0.9850
Acc American crow: 0.9853
Acc Bird: 0.9667
Acc Bobcat: 0.9792
Acc Chicken: 0.9744
Acc Coyote: 0.98
Acc Dog: 0.9905
Acc Domestic cat: 1.0
Acc Eastern chipmunk: 1.0
Acc Eastern cottontail: 0.8889
Acc Eastern fox squirrel: 1.0
Acc Eastern gray squirrel: 0.9778
Acc Grey fox: 0.9524
Acc Horse: 0.875
Acc Northern raccoon: 0.9535
Acc Red fox: 0.9344
Acc Striped skunk: 0.9545
Acc Vehicle: 1.0
Acc Virginia opossum: 1.0
Acc White Tailed Deer: 1.0
Acc Wild turkey: 1.0
Acc Woodchuck: 1.0
F1 American black bear: 0.9813
F1 American crow: 0.9745
F1 Bird: 0.9831
F1 Bobcat: 0.9691
F1 Chicken: 0.9870
F1 Coyote: 0.98
F1 Dog: 0.9952
F1 Domestic cat: 0.9863
F1 Eastern chipmunk: 0.9892
F1 Eastern cottontail: 0.9091
F1 Eastern fox squirrel: 0.9903
F1 Eastern gray squirrel: 0.9670
F1 Grey fox: 0.9677
F1 Horse: 0.9333
F1 Northern raccoon: 0.9535
F1 Red fox: 0.9580
F1 Striped skunk: 0.9655
F1 Vehicle: 1.0
F1 Virginia opossum: 1.0
F1 White Tailed Deer: 0.9714
F1 Wild turkey: 1.0
F1 Woodchuck: 1.0

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 Horse	Acc Northern raccoon	Acc Red fox	Acc Striped skunk	Acc Vehicle	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 Horse	F1 Northern raccoon	F1 Red fox	F1 Striped skunk	F1 Vehicle	F1 Virginia opossum	F1 White Tailed Deer	F1 Wild turkey	F1 Woodchuck
1.3767	0.1302	100	1.9571	0.4626	0.3682	0.9774	0.5294	0.0	0.0	0.7564	0.72	0.3619	0.4722	0.8043	0.1556	0.4118	0.7111	0.4603	0.0	0.2093	0.0	0.0	0.0	0.7593	0.1765	0.0	0.3667	0.3626	0.4932	0.0	0.0	0.7195	0.6990	0.5205	0.4444	0.8810	0.2154	0.5526	0.8205	0.6304	0.0	0.2951	0.0	0.0	0.0	0.8586	0.2647	0.0	0.3438
0.9485	0.2604	200	1.3869	0.6191	0.5612	0.9323	0.8603	0.7333	0.25	0.4872	0.98	0.5524	0.5417	0.9348	0.2222	0.8039	0.7556	0.6190	0.125	0.3488	0.2131	0.3182	0.55	0.8148	0.4706	0.4186	0.0333	0.6108	0.8269	0.6769	0.3636	0.6387	0.4395	0.6629	0.4756	0.9663	0.3175	0.6613	0.8608	0.5493	0.2222	0.4839	0.3210	0.3944	0.7097	0.8934	0.6316	0.5806	0.0606
0.5111	0.3906	300	1.2253	0.6992	0.7108	0.6391	0.9118	0.7	0.8333	0.8718	0.8	0.8095	0.7778	0.9348	0.3556	0.9804	0.7778	0.5714	0.625	0.8372	0.2787	0.3409	0.95	0.4167	0.6471	0.5814	0.7333	0.7489	0.8185	0.6885	0.2817	0.8193	0.8602	0.8374	0.7619	0.9663	0.5	0.8696	0.8434	0.7273	0.7692	0.6857	0.4359	0.4688	0.7917	0.5844	0.6875	0.6757	0.8148
0.5202	0.5208	400	1.0903	0.6504	0.6495	0.9850	0.3235	0.7667	0.4167	0.7821	0.82	0.6286	0.5	0.9565	0.6667	0.7451	0.7778	0.6190	0.0	0.3256	0.8033	0.3864	0.7	0.5926	0.6863	0.6977	0.7	0.5078	0.4862	0.6765	0.4255	0.8079	0.8542	0.7630	0.6207	0.9670	0.6316	0.8261	0.7609	0.7647	0.0	0.4746	0.6759	0.5075	0.8	0.7442	0.7692	0.5128	0.7119
0.6265	0.6510	500	0.8185	0.7947	0.7629	0.8496	0.9118	0.2	0.7917	0.9615	1.0	0.7429	0.8056	0.9565	0.6444	0.8431	0.8889	0.5079	0.625	0.7442	0.7377	0.5227	0.8	0.9907	0.5686	0.7209	0.7667	0.8933	0.8921	0.2667	0.5170	0.8108	0.8475	0.8387	0.6784	0.9670	0.6304	0.8515	0.7843	0.6667	0.7692	0.8205	0.8108	0.5412	0.8889	0.9907	0.7160	0.7949	0.8070
0.4103	0.7812	600	1.2751	0.6298	0.5905	0.9474	0.7353	0.8	0.375	0.7436	0.9	0.6	0.5278	0.9348	0.2667	0.4314	0.7111	0.5397	0.0	0.6512	0.1639	0.5682	0.3	0.9630	0.2745	0.1860	0.5	0.4764	0.8	0.7059	0.4390	0.7945	0.8654	0.7456	0.6496	0.9663	0.4211	0.6027	0.8101	0.7010	0.0	0.7368	0.2817	0.2538	0.3529	0.9765	0.4308	0.3137	0.6667
0.355	0.9115	700	0.8689	0.7672	0.7439	0.9699	0.9191	0.8333	0.4792	0.7692	0.92	0.6476	0.6389	0.9783	0.7111	0.9608	0.8444	0.4762	0.75	0.8605	0.1475	0.6591	0.9	0.8981	0.8824	0.5814	0.7667	0.8113	0.8117	0.7463	0.5227	0.8571	0.8679	0.7640	0.6970	0.9783	0.6957	0.9608	0.8736	0.6452	0.8571	0.6435	0.2571	0.6988	0.5373	0.9463	0.6767	0.6494	0.8679
0.3807	1.0417	800	0.8685	0.7527	0.7404	0.6992	1.0	0.8333	0.375	0.6667	0.7	0.9238	0.8333	0.8913	0.6667	0.9608	0.7111	0.7937	0.75	0.6744	0.4918	0.3409	0.85	0.9907	0.6667	0.2093	0.7	0.8017	0.64	0.6667	0.4186	0.8	0.8235	0.9108	0.8451	0.9318	0.5405	0.784	0.6957	0.8	0.8571	0.7733	0.6593	0.5085	0.9189	0.9427	0.8	0.3462	0.8235
0.204	1.1719	900	0.6047	0.8366	0.8159	0.9398	0.8088	0.4667	0.8333	0.9231	0.9	0.8952	0.9306	0.9348	0.7111	1.0	0.8889	0.7302	0.875	0.7674	0.7049	0.7273	0.8	0.9537	0.6471	0.5814	0.8333	0.8562	0.8907	0.6087	0.6061	0.8834	0.9184	0.94	0.8272	0.9556	0.7191	0.7786	0.7843	0.8364	0.8235	0.825	0.7414	0.7711	0.8889	0.9763	0.7765	0.7353	0.8065
0.3145	1.3021	1000	0.5570	0.8489	0.8175	0.9774	0.9779	0.4667	0.7292	0.9231	0.98	0.9524	0.7639	1.0	0.5778	1.0	0.9111	0.8254	0.875	0.5581	0.7213	0.7045	0.85	0.9907	0.8824	0.2558	0.7333	0.8497	0.8808	0.5957	0.7692	0.8780	0.9703	0.9615	0.7971	0.9787	0.65	0.8430	0.9425	0.8189	0.875	0.7059	0.8148	0.6966	0.9189	0.9817	0.8036	0.4074	0.8462
0.2662	1.4323	1100	0.4117	0.8901	0.8754	0.9549	0.9485	0.8667	0.7292	0.7692	0.96	0.9048	0.9167	0.9565	0.7333	1.0	0.9111	0.9048	0.875	0.8605	0.8197	0.7727	0.7	0.9907	0.9216	0.6744	0.9667	0.9170	0.9214	0.7536	0.5983	0.8333	0.9697	0.9453	0.9296	0.9670	0.7416	0.9714	0.9425	0.9344	0.9333	0.9024	0.8333	0.8193	0.8235	0.9907	0.9216	0.7436	0.8657
0.391	1.5625	1200	0.4867	0.8664	0.8440	0.9398	0.8382	0.9667	0.4792	0.6795	0.9	0.9143	0.875	1.0	0.7778	0.9020	0.9333	0.8889	0.875	0.9070	0.7705	0.75	0.9	0.9907	0.9608	0.9070	0.7667	0.8741	0.9084	0.5421	0.6389	0.8092	0.9278	0.9552	0.8936	0.8598	0.6604	0.9485	0.9545	0.8296	0.6667	0.8966	0.8545	0.8049	0.8372	0.9953	0.9423	0.9176	0.8519
0.3194	1.6927	1300	0.2562	0.9237	0.9099	0.9624	0.8676	0.9667	0.7292	0.9359	0.94	0.9810	0.9722	1.0	0.7556	1.0	0.9111	0.9365	0.625	0.9767	0.9016	0.75	0.95	0.9907	0.9216	0.9070	0.9667	0.9275	0.9008	0.9206	0.8140	0.9605	0.94	0.9856	0.9272	0.9892	0.68	1.0	0.9318	0.8939	0.7143	0.9655	0.9167	0.8049	0.9744	0.9953	0.9216	0.9176	0.9355
0.0988	1.8229	1400	0.3040	0.9092	0.8956	0.9850	0.8824	0.9667	0.8542	0.9103	0.94	0.9333	0.9028	1.0	0.8222	0.9608	0.9556	0.9524	0.875	0.8605	0.7869	0.75	0.85	1.0	0.9412	0.6512	0.9333	0.9493	0.8955	0.9355	0.7961	0.9281	0.9592	0.9655	0.9028	0.9787	0.74	0.9608	0.9451	0.8333	0.7778	0.8916	0.8807	0.7952	0.9189	0.9863	0.9412	0.7887	0.9333
0.0861	1.9531	1500	0.2570	0.9298	0.9183	0.9624	0.9926	0.9667	0.8125	0.9103	0.94	0.9524	0.875	1.0	0.6444	1.0	0.9111	0.8571	0.875	0.9302	0.8852	0.9545	1.0	0.9907	0.9412	0.9070	0.9333	0.9734	0.9712	0.8788	0.78	0.9281	0.9216	0.9662	0.8811	0.9892	0.7532	0.9808	0.9425	0.9153	0.9333	0.8421	0.8926	0.8936	0.9302	0.9817	0.9320	0.9512	0.9655
0.1595	2.0833	1600	0.2599	0.9359	0.9282	0.9850	0.9926	0.8333	0.9167	0.7949	0.96	0.9905	0.9444	1.0	0.6444	1.0	0.9778	0.9206	0.875	0.9767	0.8361	0.8864	0.95	0.9630	0.9804	0.9302	0.9667	0.9424	0.9677	0.8929	0.9167	0.8671	0.9505	0.9455	0.9067	0.9892	0.7436	0.9714	0.9778	0.9431	0.875	0.8842	0.9107	0.9176	0.95	0.9811	0.9524	0.9524	0.9831
0.102	2.2135	1700	0.2977	0.9214	0.9054	0.9850	0.9044	0.9667	0.7917	0.8846	0.94	0.9810	0.9167	1.0	0.7333	1.0	0.9778	0.8571	0.875	0.9535	0.8197	0.8864	0.95	0.9907	0.9020	0.8140	0.9667	0.9097	0.9425	0.8923	0.8736	0.9324	0.9495	0.9904	0.9103	0.9787	0.7416	0.8793	0.9263	0.9153	0.6667	0.9318	0.8696	0.8764	0.9744	0.9907	0.92	0.8974	0.9508
0.3103	2.3438	1800	0.2418	0.9313	0.9239	0.9699	0.9338	0.9	0.8542	0.9615	0.96	0.9714	0.8056	1.0	0.9111	0.9804	0.9333	0.9206	0.875	0.9302	0.9672	0.8864	1.0	0.9537	0.7451	0.9535	0.9667	0.9556	0.9585	0.9153	0.8817	0.9146	0.9796	0.9623	0.8855	0.9892	0.7387	0.9524	0.9655	0.9431	0.875	0.9302	0.9219	0.8764	0.9524	0.9763	0.8352	0.9647	0.9508
0.0823	2.4740	1900	0.2137	0.9458	0.9382	0.9699	0.9485	0.9667	0.875	0.9359	0.96	0.9905	0.9583	1.0	0.8222	0.9412	0.9556	0.9048	0.875	0.8837	0.9180	0.9318	0.95	0.9907	0.9804	0.8837	0.9667	0.9382	0.9520	0.9355	0.8317	0.9669	0.9796	0.9952	0.9324	0.9892	0.7957	0.9697	0.9663	0.9268	0.875	0.9383	0.9412	0.9111	0.9744	0.9953	0.9524	0.9383	0.9355
0.0975	2.6042	2000	0.1996	0.9450	0.9378	0.9624	0.8824	0.6667	0.8542	0.9615	0.96	0.9905	0.9722	1.0	0.9111	1.0	0.9333	0.9683	0.875	0.9767	0.9508	0.8864	1.0	0.9815	0.9608	0.9767	0.9333	0.9734	0.9231	0.7843	0.9011	0.9317	0.9796	0.9858	0.9589	0.9892	0.7736	0.9623	0.9655	0.9313	0.9333	0.9767	0.9431	0.9070	0.9756	0.9907	0.9245	0.9882	0.9333
0.1853	2.7344	2100	0.2121	0.9489	0.9399	0.9474	0.9485	0.8333	0.875	0.9231	0.96	0.9905	1.0	1.0	0.8222	1.0	0.9778	0.9524	0.75	0.9535	0.8525	0.9318	0.95	0.9907	1.0	0.9767	0.9333	0.9692	0.9314	0.8929	0.8317	0.9412	0.96	0.9952	0.9474	0.9892	0.8315	0.9808	0.9888	0.9449	0.8571	0.9425	0.9204	0.9011	0.95	0.9953	0.9533	0.9882	0.9655
0.0936	2.8646	2200	0.1857	0.9527	0.9481	0.9774	0.9265	0.9667	0.7292	0.9744	0.98	0.9619	0.9861	1.0	0.9333	0.9020	0.9556	0.9365	0.875	0.9070	0.9508	0.9545	1.0	0.9907	1.0	0.9535	1.0	0.9524	0.9509	0.9667	0.8140	0.9806	0.98	0.9806	0.9726	0.9892	0.8571	0.9388	0.9773	0.9672	0.9333	0.9286	0.928	0.8485	1.0	0.9953	0.9808	0.9647	0.9524
0.2936	2.9948	2300	0.1717	0.9588	0.9569	0.8947	0.9926	0.9333	1.0	0.9615	0.96	0.9714	0.9861	1.0	0.8	1.0	0.9778	0.9524	0.875	0.9535	0.8852	0.9091	1.0	1.0	1.0	1.0	0.9667	0.9407	0.9541	0.9655	0.9412	0.9677	0.9796	0.9855	0.9726	0.9892	0.8675	1.0	0.9670	0.9023	0.9333	0.9318	0.9391	0.9302	1.0	1.0	0.9623	0.9556	0.9667
0.0065	3.125	2400	0.1338	0.9733	0.9711	0.9699	0.9779	0.9667	1.0	0.9615	0.96	0.9905	1.0	1.0	0.8667	1.0	0.9778	0.9365	0.875	0.9767	0.9344	0.9091	1.0	1.0	1.0	1.0	1.0	0.9736	0.9708	0.9831	0.9505	0.9804	0.9796	0.9905	0.9796	0.9892	0.8864	1.0	0.9888	0.9291	0.9333	0.9655	0.9580	0.9524	1.0	1.0	0.9533	1.0	1.0
0.0467	3.2552	2500	0.1448	0.9672	0.9640	0.9699	0.9338	0.9667	0.9167	0.9487	0.96	0.9905	0.9583	1.0	0.9556	1.0	0.9556	0.9365	0.875	0.9767	0.9508	0.9773	1.0	1.0	0.9804	1.0	1.0	0.9810	0.9585	0.9667	0.9263	0.9737	0.9796	0.9952	0.9650	0.9892	0.86	0.9714	0.9773	0.9593	0.9333	0.9655	0.9431	0.9348	1.0	1.0	0.9434	1.0	0.9836
0.0757	3.3854	2600	0.1638	0.9626	0.9592	0.9624	1.0	0.9667	0.9375	0.9615	0.96	0.9905	0.9583	1.0	0.8	1.0	0.9556	0.9841	0.875	0.8837	0.8852	0.9091	1.0	1.0	0.9804	0.9767	1.0	0.9697	0.9577	0.9831	0.9184	0.9740	0.9796	0.9905	0.9583	0.9892	0.8571	0.9903	0.9663	0.9118	0.9333	0.9383	0.9310	0.9524	1.0	1.0	0.9615	0.9882	0.9524
0.0302	3.5156	2700	0.1556	0.9702	0.9657	0.9925	0.9926	0.9667	1.0	0.9744	0.98	0.9905	0.9861	1.0	0.8	1.0	0.9778	0.9841	0.875	0.9535	0.8852	0.8636	1.0	1.0	0.9412	0.9767	1.0	0.9851	0.9677	0.9667	0.9231	0.9806	0.9899	0.9952	0.9595	0.9892	0.8675	1.0	0.9462	0.9612	0.9333	0.9762	0.9391	0.9268	1.0	1.0	0.9505	0.9882	1.0
0.2921	3.6458	2800	0.1312	0.9656	0.9635	0.9624	0.9412	0.9667	0.9583	0.9744	0.96	0.9905	0.9861	1.0	0.9111	1.0	0.9778	0.9524	0.875	0.9302	0.9016	0.8864	1.0	1.0	1.0	1.0	1.0	0.9734	0.9481	0.9831	0.8846	0.9870	0.9796	0.9952	0.9660	0.9892	0.8454	1.0	0.9888	0.9677	0.9333	0.9412	0.9244	0.9286	1.0	1.0	0.9623	1.0	1.0
0.0554	3.7760	2900	0.1303	0.9740	0.9701	0.9850	0.9853	0.9667	0.9375	0.9615	0.98	0.9905	1.0	1.0	0.8222	1.0	0.9778	0.9683	0.875	0.9302	0.9344	0.9545	1.0	1.0	1.0	1.0	1.0	0.9887	0.9675	0.9831	0.9375	0.9804	0.98	0.9952	0.9730	0.9892	0.8706	0.9714	0.9888	0.9606	0.9333	0.9639	0.9580	0.9545	1.0	1.0	0.9623	1.0	0.9836
0.0171	3.9062	3000	0.1468	0.9733	0.9703	0.9850	1.0	0.9667	0.9792	0.9615	0.96	0.9905	1.0	1.0	0.8	1.0	0.9778	0.9524	0.875	0.9302	0.8852	1.0	1.0	1.0	0.9804	1.0	1.0	0.9813	0.9749	0.9831	0.9895	0.9804	0.9796	0.9858	0.9730	0.9892	0.8780	0.9808	0.9778	0.9449	0.9333	0.9412	0.9231	0.9778	1.0	1.0	0.9524	1.0	1.0
0.0813	4.0365	3100	0.1277	0.9748	0.9721	0.9850	0.9632	0.9667	0.9583	0.9744	0.98	0.9810	1.0	1.0	0.9111	1.0	0.9778	0.9524	0.875	0.9302	0.9344	0.9773	1.0	1.0	0.9804	1.0	1.0	0.9813	0.9740	0.9831	0.9684	0.9806	0.98	0.9856	0.9730	0.9892	0.8913	0.9714	0.9778	0.9677	0.9333	0.9524	0.95	0.9663	1.0	1.0	0.9615	1.0	1.0
0.0048	4.1667	3200	0.1341	0.9733	0.9710	0.9850	0.9265	0.9667	0.9792	0.9744	0.98	0.9810	1.0	1.0	0.9333	1.0	0.9778	0.9524	0.875	0.9302	0.9344	1.0	1.0	1.0	0.9804	1.0	1.0	0.9813	0.9582	0.9831	0.9895	0.9806	0.9899	0.9856	0.9730	0.9892	0.8571	1.0	0.9778	0.9677	0.9333	0.9302	0.95	0.9778	0.9756	1.0	0.9615	1.0	1.0
0.0939	4.2969	3300	0.1393	0.9733	0.9705	0.9850	0.9338	0.9667	0.9792	0.9615	0.98	0.9810	1.0	1.0	0.9333	1.0	0.9778	0.9524	0.875	0.9535	0.9344	0.9545	1.0	1.0	1.0	1.0	1.0	0.9813	0.9585	0.9831	0.9592	0.9740	0.9899	0.9904	0.9796	0.9892	0.8660	1.0	0.9778	0.9756	0.9333	0.9425	0.9580	0.9545	0.9756	1.0	0.9623	1.0	1.0
0.0281	4.4271	3400	0.1279	0.9748	0.9712	0.9925	0.9853	0.9667	0.9583	0.9615	0.98	0.9905	1.0	1.0	0.8222	1.0	0.9778	0.9683	0.875	0.9302	0.9672	0.9545	1.0	0.9907	0.9608	1.0	1.0	0.9814	0.9710	0.9831	0.9485	0.9804	0.98	0.9952	0.9796	0.9892	0.8706	1.0	0.9888	0.976	0.9333	0.9639	0.9365	0.9655	0.9756	0.9953	0.9515	1.0	1.0
0.0157	4.5573	3500	0.1141	0.9748	0.9715	0.9850	0.9853	0.9667	0.9583	0.9744	0.96	0.9905	1.0	1.0	0.8222	1.0	0.9778	0.9683	0.875	0.9302	0.9508	0.9773	1.0	1.0	0.9608	1.0	1.0	0.9850	0.9675	0.9831	0.9583	0.9870	0.9697	0.9952	0.9796	0.9892	0.8706	0.9623	0.9888	0.9606	0.9333	0.9639	0.9508	0.9773	1.0	1.0	0.9515	1.0	1.0
0.0007	4.6875	3600	0.1217	0.9794	0.9761	0.9925	0.9926	0.9667	0.9792	1.0	0.98	0.9905	0.9861	1.0	0.8444	1.0	0.9778	0.9365	0.875	0.9535	0.9672	0.9773	1.0	1.0	0.9608	1.0	1.0	0.9814	0.9747	0.9831	0.9691	0.9873	0.98	0.9952	0.9861	0.9892	0.8837	0.9903	0.9778	0.9672	0.9333	0.9535	0.9752	0.9773	1.0	1.0	0.9703	1.0	1.0
0.0003	4.8177	3700	0.1051	0.9756	0.9723	0.9850	0.9632	0.9667	0.9583	0.9615	0.98	0.9905	1.0	1.0	0.9333	1.0	0.9778	0.9524	0.875	0.9535	0.9344	0.9318	1.0	1.0	1.0	1.0	1.0	0.9813	0.9704	0.9831	0.9583	0.9804	0.98	0.9952	0.9863	0.9892	0.9032	0.9903	0.9670	0.9756	0.9333	0.9425	0.95	0.9425	1.0	1.0	0.9623	1.0	1.0
0.0675	4.9479	3800	0.0992	0.9786	0.9755	0.9850	0.9853	0.9667	0.9792	0.9744	0.98	0.9905	1.0	1.0	0.8889	1.0	0.9778	0.9524	0.875	0.9535	0.9344	0.9545	1.0	1.0	1.0	1.0	1.0	0.9813	0.9745	0.9831	0.9691	0.9870	0.98	0.9952	0.9863	0.9892	0.9091	0.9903	0.9670	0.9677	0.9333	0.9535	0.9580	0.9655	1.0	1.0	0.9714	1.0	1.0
0.0207	5.0781	3900	0.1001	0.9794	0.9762	0.9925	0.9706	0.9667	0.9792	0.9744	0.98	1.0	0.9722	1.0	0.9556	1.0	0.9778	0.9524	0.875	0.9535	0.9344	0.9545	1.0	1.0	1.0	1.0	1.0	0.9888	0.9778	0.9831	0.9592	0.9870	0.98	0.9906	0.9790	0.9892	0.9247	0.9903	0.9778	0.9677	0.9333	0.9535	0.9661	0.9655	1.0	1.0	0.9623	1.0	1.0
0.0024	5.2083	4000	0.1021	0.9763	0.9745	0.9850	0.9338	0.9667	1.0	0.9872	0.98	1.0	0.9583	1.0	1.0	1.0	0.9778	0.9524	0.875	0.9535	0.9344	0.9545	1.0	1.0	0.9804	1.0	1.0	0.9813	0.9621	0.9831	0.9796	0.9935	0.9899	0.9906	0.9718	0.9892	0.8911	0.9903	0.9778	0.9677	0.9333	0.9535	0.9580	0.9655	1.0	1.0	0.9615	1.0	1.0
0.0001	5.3385	4100	0.1299	0.9740	0.9723	0.9774	0.9338	0.9667	1.0	0.9615	0.98	1.0	0.9722	1.0	0.9778	1.0	0.9778	0.9524	0.875	0.9535	0.9016	0.9773	1.0	1.0	1.0	1.0	1.0	0.9811	0.9621	0.9831	0.9796	0.9804	0.9899	0.9906	0.9722	0.9892	0.8889	0.9903	0.9670	0.9524	0.9333	0.9425	0.9483	0.9773	1.0	1.0	0.9623	1.0	1.0
0.071	5.4688	4200	0.1155	0.9786	0.9759	0.9850	0.9926	0.9667	0.9792	0.9744	0.96	0.9905	1.0	1.0	0.8667	1.0	0.9778	0.9841	0.875	0.9535	0.9180	0.9545	1.0	1.0	1.0	1.0	1.0	0.9813	0.9712	0.9831	0.9691	0.9870	0.9697	0.9952	0.9863	0.9892	0.9070	0.9903	0.9670	0.9688	0.9333	0.9762	0.9573	0.9655	1.0	1.0	0.9714	1.0	1.0
0.0005	5.5990	4300	0.1067	0.9763	0.9736	0.9850	0.9485	0.9667	0.9583	0.9744	0.96	0.9905	1.0	1.0	0.9778	1.0	0.9778	0.9683	0.875	0.9535	0.9180	0.9545	1.0	1.0	1.0	1.0	1.0	0.9850	0.9663	0.9831	0.9485	0.9870	0.9697	0.9952	0.9863	0.9892	0.9072	0.9808	0.9670	0.9606	0.9333	0.9647	0.9573	0.9655	1.0	1.0	0.9714	1.0	1.0
0.0656	5.7292	4400	0.1128	0.9802	0.9774	0.9850	0.9853	0.9667	0.9792	0.9744	0.96	0.9905	1.0	1.0	0.8889	1.0	0.9778	0.9841	0.875	0.9535	0.9344	0.9773	1.0	1.0	1.0	1.0	1.0	0.9850	0.9745	0.9831	0.9691	0.9870	0.9697	0.9952	0.9863	0.9892	0.9091	0.9903	0.9778	0.9612	0.9333	0.9762	0.9661	0.9773	1.0	1.0	0.9714	1.0	1.0
0.0503	5.8594	4500	0.1144	0.9779	0.9751	0.9850	0.9926	0.9667	0.9792	0.9744	0.96	1.0	0.9722	1.0	0.8444	1.0	0.9778	0.9841	0.875	0.9535	0.9180	0.9773	1.0	1.0	1.0	1.0	1.0	0.9850	0.9712	0.9831	0.9592	0.9870	0.9697	0.9906	0.9722	0.9892	0.8941	0.9903	0.9778	0.9764	0.9333	0.9762	0.9573	0.9773	1.0	1.0	0.9623	1.0	1.0
0.0072	5.9896	4600	0.1050	0.9763	0.9736	0.9850	0.9853	0.9667	0.9792	0.9872	0.96	1.0	0.9722	1.0	0.8667	1.0	0.9778	0.9683	0.875	0.9535	0.9180	0.9773	1.0	1.0	0.9608	1.0	1.0	0.9850	0.9710	0.9831	0.9691	0.9809	0.9697	0.9906	0.9722	0.9892	0.8966	0.9903	0.9670	0.9606	0.9333	0.9647	0.9573	0.9773	1.0	1.0	0.9608	1.0	1.0
0.0001	6.1198	4700	0.1132	0.9786	0.9763	0.9850	0.9485	0.9667	0.9792	0.9744	0.96	0.9905	1.0	1.0	0.9778	1.0	0.9778	0.9841	0.875	0.9535	0.9344	0.9545	1.0	1.0	1.0	1.0	1.0	0.9813	0.9663	0.9831	0.9592	0.9870	0.9697	0.9952	0.9863	0.9892	0.9072	0.9903	0.9670	0.9841	0.9333	0.9762	0.9661	0.9655	1.0	1.0	0.9714	1.0	1.0
0.0001	6.25	4800	0.1129	0.9779	0.9752	0.9850	0.9632	0.9667	0.9792	0.9744	0.96	0.9905	1.0	1.0	0.9111	1.0	0.9778	0.9841	0.875	0.9535	0.9344	0.9545	1.0	1.0	1.0	1.0	1.0	0.9850	0.9632	0.9831	0.94	0.9870	0.9697	0.9952	0.9863	0.9892	0.8913	1.0	0.9670	0.9841	0.9333	0.9762	0.9661	0.9655	1.0	1.0	0.9714	1.0	1.0
0.0001	6.3802	4900	0.1115	0.9794	0.9769	0.9850	0.9632	0.9667	0.9792	0.9744	0.98	0.9905	1.0	1.0	0.9556	1.0	0.9778	0.9683	0.875	0.9535	0.9344	0.9545	1.0	1.0	1.0	1.0	1.0	0.9813	0.9704	0.9831	0.9691	0.9870	0.98	0.9952	0.9863	0.9892	0.9149	0.9903	0.9670	0.976	0.9333	0.9647	0.9661	0.9655	1.0	1.0	0.9714	1.0	1.0
0.0135	6.5104	5000	0.1094	0.9779	0.9754	0.9850	0.9779	0.9667	0.9792	0.9744	0.96	0.9905	1.0	1.0	0.8667	1.0	0.9778	0.9683	0.875	0.9535	0.9344	0.9773	1.0	1.0	1.0	1.0	1.0	0.9813	0.9638	0.9831	0.9691	0.9870	0.9697	0.9952	0.9863	0.9892	0.8864	1.0	0.9670	0.9683	0.9333	0.9647	0.9661	0.9773	1.0	1.0	0.9714	1.0	1.0
0.0001	6.6406	5100	0.1110	0.9794	0.9770	0.9850	0.9779	0.9667	0.9792	0.9744	0.98	0.9905	1.0	1.0	0.8889	1.0	0.9778	0.9683	0.875	0.9535	0.9344	0.9773	1.0	1.0	1.0	1.0	1.0	0.9813	0.9673	0.9831	0.9792	0.9870	0.98	0.9952	0.9863	0.9892	0.8989	0.9903	0.9670	0.976	0.9333	0.9647	0.9661	0.9773	1.0	1.0	0.9714	1.0	1.0
0.0003	6.7708	5200	0.1118	0.9786	0.9760	0.9850	0.9779	0.9667	0.9792	0.9744	0.98	0.9905	1.0	1.0	0.8889	1.0	0.9778	0.9683	0.875	0.9535	0.9344	0.9545	1.0	1.0	1.0	1.0	1.0	0.9813	0.9673	0.9831	0.9691	0.9870	0.98	0.9952	0.9863	0.9892	0.8989	0.9903	0.9670	0.976	0.9333	0.9647	0.9661	0.9655	1.0	1.0	0.9714	1.0	1.0
0.0104	6.9010	5300	0.1088	0.9809	0.9785	0.9850	0.9779	0.9667	0.9792	0.9744	0.98	0.9905	1.0	1.0	0.9111	1.0	0.9778	0.9841	0.875	0.9535	0.9508	0.9545	1.0	1.0	1.0	1.0	1.0	0.9813	0.9708	0.9831	0.9691	0.9870	0.98	0.9952	0.9863	0.9892	0.9111	0.9903	0.9778	0.9841	0.9333	0.9762	0.9748	0.9655	1.0	1.0	0.9714	1.0	1.0

Framework versions

Transformers 4.52.2
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

Task: Image Classification
Library: transformers
Type: timm_wrapper
License: apache-2.0
Created: May 28, 2025
Updated: May 28, 2025