sayakpaul/glpn-nyu-finetuned-diode-230131-041708

Training Loss	Epoch	Step	Validation Loss	Mae	Rmse	Abs Rel	Log Mae	Log Rmse	Delta1	Delta2	Delta3
0.5276	1.0	72	0.4701	0.4590	0.6348	0.4983	0.1903	0.2393	0.3169	0.5544	0.7661
0.4595	2.0	144	0.4867	0.4690	0.6369	0.5588	0.1956	0.2483	0.3090	0.5269	0.7532
0.4802	3.0	216	0.4854	0.4648	0.6344	0.5581	0.1935	0.2475	0.3135	0.5355	0.7531
0.4566	4.0	288	0.4709	0.4559	0.6756	0.4223	0.1890	0.2516	0.3668	0.6329	0.7696
0.4916	5.0	360	0.4835	0.4555	0.6343	0.5302	0.1881	0.2447	0.3435	0.5716	0.7437
0.4822	6.0	432	0.4756	0.4585	0.6301	0.5264	0.1894	0.2414	0.3238	0.5628	0.7435
0.4588	7.0	504	0.4655	0.4481	0.6509	0.4425	0.1843	0.2413	0.3498	0.6157	0.7809
0.4214	8.0	576	0.4869	0.4706	0.6391	0.5669	0.1961	0.2500	0.3033	0.5388	0.7371
0.426	9.0	648	0.4835	0.4679	0.6472	0.5117	0.1951	0.2486	0.3216	0.5474	0.7399
0.4135	10.0	720	0.4621	0.4439	0.6287	0.4803	0.1825	0.2365	0.3451	0.5887	0.7878
0.3778	11.0	792	0.4756	0.4566	0.6337	0.5174	0.1892	0.2431	0.3297	0.5560	0.7690
0.426	12.0	864	0.4542	0.4362	0.6219	0.4621	0.1779	0.2303	0.3572	0.6083	0.7835
0.4282	13.0	936	0.4514	0.4306	0.6195	0.4678	0.1754	0.2307	0.3661	0.6228	0.8083
0.4045	14.0	1008	0.4575	0.4390	0.6315	0.4530	0.1794	0.2343	0.3641	0.6128	0.7787
0.4351	15.0	1080	0.4669	0.4373	0.6423	0.4322	0.1796	0.2423	0.3917	0.6233	0.7850
0.4001	16.0	1152	0.4540	0.4356	0.6331	0.4336	0.1767	0.2320	0.3919	0.6132	0.7732
0.3741	17.0	1224	0.4890	0.4645	0.6361	0.5707	0.1926	0.2494	0.3253	0.5469	0.7386
0.4128	18.0	1296	0.4815	0.4593	0.6328	0.5511	0.1899	0.2457	0.3302	0.5571	0.7471
0.3809	19.0	1368	0.5002	0.4768	0.6425	0.6061	0.1991	0.2560	0.3105	0.5222	0.7118
0.4089	20.0	1440	0.4503	0.4311	0.6449	0.4081	0.1752	0.2370	0.4147	0.6445	0.7823
0.3612	21.0	1512	0.4541	0.4280	0.6215	0.4543	0.1735	0.2302	0.3823	0.6291	0.7968
0.3664	22.0	1584	0.4425	0.4251	0.6347	0.3970	0.1717	0.2300	0.4181	0.6374	0.7860
0.3787	23.0	1656	0.4722	0.4477	0.6378	0.4868	0.1846	0.2432	0.3541	0.6041	0.7733
0.4184	24.0	1728	0.4749	0.4506	0.6303	0.5329	0.1857	0.2434	0.3465	0.5752	0.7698
0.3928	25.0	1800	0.4646	0.4485	0.6395	0.4744	0.1847	0.2407	0.3528	0.5946	0.7816
0.3704	26.0	1872	0.4492	0.4340	0.6331	0.4344	0.1765	0.2326	0.3778	0.6314	0.7916
0.3462	27.0	1944	0.4467	0.4307	0.6314	0.4296	0.1751	0.2317	0.3840	0.6359	0.7983
0.3808	28.0	2016	0.4758	0.4622	0.6331	0.5236	0.1913	0.2425	0.3230	0.5439	0.7438
0.3641	29.0	2088	0.4609	0.4452	0.6315	0.4545	0.1824	0.2339	0.3484	0.5934	0.7716
0.3602	30.0	2160	0.4546	0.4413	0.6230	0.4729	0.1804	0.2318	0.3515	0.5944	0.7778
0.3638	31.0	2232	0.4498	0.4340	0.6245	0.4449	0.1764	0.2296	0.3725	0.6079	0.7923
0.3699	32.0	2304	0.4472	0.4305	0.6228	0.4568	0.1750	0.2307	0.3757	0.6239	0.8000
0.3805	33.0	2376	0.4647	0.4439	0.6325	0.4875	0.1823	0.2392	0.3609	0.5921	0.7833
0.3454	34.0	2448	0.4640	0.4442	0.6276	0.5008	0.1820	0.2376	0.3573	0.5865	0.7866
0.3452	35.0	2520	0.4646	0.4454	0.6276	0.4966	0.1827	0.2374	0.3489	0.5913	0.7726
0.3509	36.0	2592	0.4522	0.4394	0.6259	0.4605	0.1799	0.2321	0.3534	0.6001	0.7944
0.3432	37.0	2664	0.4656	0.4484	0.6290	0.5067	0.1841	0.2390	0.3487	0.5802	0.7687
0.381	38.0	2736	0.4630	0.4405	0.6287	0.4970	0.1807	0.2387	0.3565	0.6067	0.7907
0.3591	39.0	2808	0.4637	0.4452	0.6269	0.4995	0.1825	0.2374	0.3487	0.5966	0.7654
0.3826	40.0	2880	0.4723	0.4527	0.6307	0.5279	0.1867	0.2421	0.3338	0.5745	0.7713
0.3585	41.0	2952	0.4485	0.4306	0.6238	0.4470	0.1749	0.2297	0.3736	0.6251	0.7995
0.3518	42.0	3024	0.4369	0.4229	0.6293	0.4111	0.1701	0.2277	0.4004	0.6563	0.8009
0.359	43.0	3096	0.4545	0.4348	0.6274	0.4607	0.1777	0.2338	0.3592	0.6237	0.8000
0.3274	44.0	3168	0.4595	0.4359	0.6278	0.4781	0.1779	0.2357	0.3729	0.6093	0.7980
0.3368	45.0	3240	0.4617	0.4434	0.6253	0.5001	0.1819	0.2368	0.3400	0.5966	0.7953
0.3638	46.0	3312	0.4634	0.4380	0.6264	0.4925	0.1794	0.2371	0.3576	0.6158	0.7907
0.3698	47.0	3384	0.4559	0.4343	0.6223	0.4890	0.1776	0.2346	0.3579	0.6103	0.8110
0.3392	48.0	3456	0.4646	0.4477	0.6267	0.5029	0.1837	0.2374	0.3451	0.5798	0.7665
0.3548	49.0	3528	0.4598	0.4394	0.6245	0.4885	0.1793	0.2351	0.3647	0.6016	0.7815
0.3375	50.0	3600	0.4441	0.4271	0.6226	0.4487	0.1729	0.2293	0.3808	0.6354	0.8075
0.3315	51.0	3672	0.4613	0.4403	0.6292	0.4868	0.1805	0.2373	0.3630	0.6016	0.7905
0.3313	52.0	3744	0.4445	0.4307	0.6442	0.4108	0.1746	0.2342	0.3942	0.6577	0.7932
0.3372	53.0	3816	0.4456	0.4258	0.6269	0.4404	0.1720	0.2308	0.3924	0.6489	0.8027
0.3285	54.0	3888	0.4526	0.4348	0.6241	0.4723	0.1772	0.2328	0.3615	0.6160	0.8027
0.3474	55.0	3960	0.4498	0.4369	0.6258	0.4595	0.1782	0.2315	0.3617	0.6070	0.7978
0.3349	56.0	4032	0.4613	0.4428	0.6307	0.4858	0.1819	0.2376	0.3523	0.6012	0.7875
0.3207	57.0	4104	0.4476	0.4342	0.6230	0.4500	0.1765	0.2289	0.3658	0.6151	0.7910
0.3399	58.0	4176	0.4600	0.4413	0.6248	0.4940	0.1812	0.2360	0.3531	0.5954	0.7814
0.3327	59.0	4248	0.4463	0.4339	0.6215	0.4570	0.1770	0.2294	0.3590	0.6069	0.8063
0.3215	60.0	4320	0.4482	0.4317	0.6203	0.4595	0.1756	0.2295	0.3698	0.6154	0.8034
0.3276	61.0	4392	0.4406	0.4218	0.6192	0.4370	0.1705	0.2268	0.3878	0.6425	0.8111
0.3179	62.0	4464	0.4530	0.4331	0.6217	0.4765	0.1764	0.2327	0.3660	0.6121	0.8068
0.3129	63.0	4536	0.4614	0.4398	0.6263	0.5002	0.1803	0.2378	0.3529	0.6023	0.7974
0.3354	64.0	4608	0.4538	0.4374	0.6234	0.4777	0.1788	0.2333	0.3565	0.6013	0.7995
0.3261	65.0	4680	0.4367	0.4258	0.6283	0.4249	0.1724	0.2291	0.3861	0.6484	0.8026
0.3114	66.0	4752	0.4565	0.4366	0.6225	0.4852	0.1780	0.2334	0.3647	0.6073	0.7939
0.3377	67.0	4824	0.4519	0.4308	0.6185	0.4771	0.1755	0.2314	0.3681	0.6175	0.8079
0.3266	68.0	4896	0.4372	0.4216	0.6167	0.4345	0.1702	0.2245	0.3850	0.6392	0.8163
0.3347	69.0	4968	0.4343	0.4193	0.6179	0.4318	0.1690	0.2252	0.3890	0.6557	0.8114
0.3207	70.0	5040	0.4426	0.4269	0.6180	0.4465	0.1728	0.2266	0.3810	0.6296	0.8038
0.3313	71.0	5112	0.4362	0.4234	0.6177	0.4360	0.1712	0.2252	0.3777	0.6386	0.8133
0.326	72.0	5184	0.4392	0.4251	0.6182	0.4431	0.1723	0.2265	0.3783	0.6356	0.8088
0.3141	73.0	5256	0.4532	0.4385	0.6214	0.4818	0.1796	0.2327	0.3513	0.5981	0.8044
0.3301	74.0	5328	0.4536	0.4361	0.6230	0.4808	0.1783	0.2333	0.3585	0.6097	0.8037
0.3194	75.0	5400	0.4501	0.4335	0.6216	0.4698	0.1765	0.2312	0.3623	0.6164	0.8033
0.3071	76.0	5472	0.4455	0.4310	0.6201	0.4598	0.1751	0.2292	0.3625	0.6231	0.8087
0.3174	77.0	5544	0.4472	0.4316	0.6219	0.4625	0.1756	0.2307	0.3654	0.6256	0.8022
0.3171	78.0	5616	0.4461	0.4305	0.6204	0.4614	0.1750	0.2298	0.3663	0.6263	0.8052
0.3244	79.0	5688	0.4501	0.4328	0.6226	0.4725	0.1765	0.2324	0.3611	0.6233	0.8083
0.3188	80.0	5760	0.4427	0.4280	0.6199	0.4507	0.1735	0.2281	0.3757	0.6307	0.8054
0.3212	81.0	5832	0.4383	0.4222	0.6196	0.4365	0.1702	0.2266	0.3875	0.6476	0.8093
0.3234	82.0	5904	0.4434	0.4278	0.6216	0.4479	0.1735	0.2288	0.3728	0.6337	0.8064
0.3024	83.0	5976	0.4502	0.4331	0.6214	0.4728	0.1764	0.2317	0.3645	0.6192	0.8070
0.3145	84.0	6048	0.4409	0.4258	0.6199	0.4475	0.1726	0.2280	0.3778	0.6357	0.8075
0.329	85.0	6120	0.4491	0.4302	0.6221	0.4710	0.1749	0.2322	0.3755	0.6246	0.8065
0.3034	86.0	6192	0.4504	0.4321	0.6241	0.4699	0.1757	0.2325	0.3767	0.6217	0.8035
0.3074	87.0	6264	0.4373	0.4224	0.6188	0.4396	0.1706	0.2267	0.3878	0.6439	0.8107
0.3089	88.0	6336	0.4379	0.4235	0.6191	0.4402	0.1709	0.2266	0.3893	0.6410	0.8089
0.2995	89.0	6408	0.4448	0.4292	0.6193	0.4597	0.1744	0.2292	0.3740	0.6225	0.8065
0.3248	90.0	6480	0.4413	0.4279	0.6227	0.4494	0.1732	0.2283	0.3766	0.6305	0.8100
0.3203	91.0	6552	0.4445	0.4290	0.6213	0.4568	0.1740	0.2295	0.3754	0.6290	0.8085
0.3109	92.0	6624	0.4452	0.4295	0.6203	0.4597	0.1744	0.2297	0.3749	0.6245	0.8035
0.3241	93.0	6696	0.4419	0.4258	0.6190	0.4533	0.1725	0.2285	0.3833	0.6313	0.8092
0.3078	94.0	6768	0.4446	0.4278	0.6201	0.4597	0.1736	0.2297	0.3778	0.6284	0.8097
0.3141	95.0	6840	0.4466	0.4305	0.6208	0.4660	0.1749	0.2306	0.3720	0.6233	0.8058
0.3198	96.0	6912	0.4440	0.4275	0.6194	0.4584	0.1736	0.2293	0.3774	0.6279	0.8088
0.3	97.0	6984	0.4426	0.4269	0.6192	0.4545	0.1731	0.2287	0.3770	0.6302	0.8091
0.3096	98.0	7056	0.4433	0.4274	0.6197	0.4568	0.1735	0.2292	0.3765	0.6296	0.8088
0.3317	99.0	7128	0.4394	0.4244	0.6196	0.4448	0.1716	0.2276	0.3844	0.6374	0.8096
0.3132	100.0	7200	0.4425	0.4270	0.6196	0.4543	0.1732	0.2288	0.3787	0.6298	0.8083

glpn-nyu-finetuned-diode-230131-041708

This model is a fine-tuned version of vinvino02/glpn-nyu on the diode-subset dataset.
It achieves the following results on the evaluation set:

Loss: 0.4425

Mae: 0.4270

Rmse: 0.6196

Abs Rel: 0.4543

Log Mae: 0.1732

Log Rmse: 0.2288

Delta1: 0.3787

Delta2: 0.6298

Delta3: 0.8083

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: 0.0003

train_batch_size: 24

eval_batch_size: 48

seed: 2022

optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08

lr_scheduler_type: linear

num_epochs: 100

mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Mae Rmse Abs Rel Log Mae Log Rmse Delta1 Delta2 Delta3

0.5276 1.0 72 0.4701 0.4590 0.6348 0.4983 0.1903 0.2393 0.3169 0.5544 0.7661

0.4595 2.0 144 0.4867 0.4690 0.6369 0.5588 0.1956 0.2483 0.3090 0.5269 0.7532

0.4802 3.0 216 0.4854 0.4648 0.6344 0.5581 0.1935 0.2475 0.3135 0.5355 0.7531

0.4566 4.0 288 0.4709 0.4559 0.6756 0.4223 0.1890 0.2516 0.3668 0.6329 0.7696

0.4916 5.0 360 0.4835 0.4555 0.6343 0.5302 0.1881 0.2447 0.3435 0.5716 0.7437

0.4822 6.0 432 0.4756 0.4585 0.6301 0.5264 0.1894 0.2414 0.3238 0.5628 0.7435

0.4588 7.0 504 0.4655 0.4481 0.6509 0.4425 0.1843 0.2413 0.3498 0.6157 0.7809

0.4214 8.0 576 0.4869 0.4706 0.6391 0.5669 0.1961 0.2500 0.3033 0.5388 0.7371

0.426 9.0 648 0.4835 0.4679 0.6472 0.5117 0.1951 0.2486 0.3216 0.5474 0.7399

0.4135 10.0 720 0.4621 0.4439 0.6287 0.4803 0.1825 0.2365 0.3451 0.5887 0.7878

0.3778 11.0 792 0.4756 0.4566 0.6337 0.5174 0.1892 0.2431 0.3297 0.5560 0.7690

0.426 12.0 864 0.4542 0.4362 0.6219 0.4621 0.1779 0.2303 0.3572 0.6083 0.7835

0.4282 13.0 936 0.4514 0.4306 0.6195 0.4678 0.1754 0.2307 0.3661 0.6228 0.8083

0.4045 14.0 1008 0.4575 0.4390 0.6315 0.4530 0.1794 0.2343 0.3641 0.6128 0.7787

0.4351 15.0 1080 0.4669 0.4373 0.6423 0.4322 0.1796 0.2423 0.3917 0.6233 0.7850

0.4001 16.0 1152 0.4540 0.4356 0.6331 0.4336 0.1767 0.2320 0.3919 0.6132 0.7732

0.3741 17.0 1224 0.4890 0.4645 0.6361 0.5707 0.1926 0.2494 0.3253 0.5469 0.7386

0.4128 18.0 1296 0.4815 0.4593 0.6328 0.5511 0.1899 0.2457 0.3302 0.5571 0.7471

0.3809 19.0 1368 0.5002 0.4768 0.6425 0.6061 0.1991 0.2560 0.3105 0.5222 0.7118

0.4089 20.0 1440 0.4503 0.4311 0.6449 0.4081 0.1752 0.2370 0.4147 0.6445 0.7823

0.3612 21.0 1512 0.4541 0.4280 0.6215 0.4543 0.1735 0.2302 0.3823 0.6291 0.7968

0.3664 22.0 1584 0.4425 0.4251 0.6347 0.3970 0.1717 0.2300 0.4181 0.6374 0.7860

0.3787 23.0 1656 0.4722 0.4477 0.6378 0.4868 0.1846 0.2432 0.3541 0.6041 0.7733

0.4184 24.0 1728 0.4749 0.4506 0.6303 0.5329 0.1857 0.2434 0.3465 0.5752 0.7698

0.3928 25.0 1800 0.4646 0.4485 0.6395 0.4744 0.1847 0.2407 0.3528 0.5946 0.7816

0.3704 26.0 1872 0.4492 0.4340 0.6331 0.4344 0.1765 0.2326 0.3778 0.6314 0.7916

0.3462 27.0 1944 0.4467 0.4307 0.6314 0.4296 0.1751 0.2317 0.3840 0.6359 0.7983

0.3808 28.0 2016 0.4758 0.4622 0.6331 0.5236 0.1913 0.2425 0.3230 0.5439 0.7438

0.3641 29.0 2088 0.4609 0.4452 0.6315 0.4545 0.1824 0.2339 0.3484 0.5934 0.7716

0.3602 30.0 2160 0.4546 0.4413 0.6230 0.4729 0.1804 0.2318 0.3515 0.5944 0.7778

0.3638 31.0 2232 0.4498 0.4340 0.6245 0.4449 0.1764 0.2296 0.3725 0.6079 0.7923

0.3699 32.0 2304 0.4472 0.4305 0.6228 0.4568 0.1750 0.2307 0.3757 0.6239 0.8000

0.3805 33.0 2376 0.4647 0.4439 0.6325 0.4875 0.1823 0.2392 0.3609 0.5921 0.7833

0.3454 34.0 2448 0.4640 0.4442 0.6276 0.5008 0.1820 0.2376 0.3573 0.5865 0.7866

0.3452 35.0 2520 0.4646 0.4454 0.6276 0.4966 0.1827 0.2374 0.3489 0.5913 0.7726

0.3509 36.0 2592 0.4522 0.4394 0.6259 0.4605 0.1799 0.2321 0.3534 0.6001 0.7944

0.3432 37.0 2664 0.4656 0.4484 0.6290 0.5067 0.1841 0.2390 0.3487 0.5802 0.7687

0.381 38.0 2736 0.4630 0.4405 0.6287 0.4970 0.1807 0.2387 0.3565 0.6067 0.7907

0.3591 39.0 2808 0.4637 0.4452 0.6269 0.4995 0.1825 0.2374 0.3487 0.5966 0.7654

0.3826 40.0 2880 0.4723 0.4527 0.6307 0.5279 0.1867 0.2421 0.3338 0.5745 0.7713

0.3585 41.0 2952 0.4485 0.4306 0.6238 0.4470 0.1749 0.2297 0.3736 0.6251 0.7995

0.3518 42.0 3024 0.4369 0.4229 0.6293 0.4111 0.1701 0.2277 0.4004 0.6563 0.8009

0.359 43.0 3096 0.4545 0.4348 0.6274 0.4607 0.1777 0.2338 0.3592 0.6237 0.8000

0.3274 44.0 3168 0.4595 0.4359 0.6278 0.4781 0.1779 0.2357 0.3729 0.6093 0.7980

0.3368 45.0 3240 0.4617 0.4434 0.6253 0.5001 0.1819 0.2368 0.3400 0.5966 0.7953

0.3638 46.0 3312 0.4634 0.4380 0.6264 0.4925 0.1794 0.2371 0.3576 0.6158 0.7907

0.3698 47.0 3384 0.4559 0.4343 0.6223 0.4890 0.1776 0.2346 0.3579 0.6103 0.8110

0.3392 48.0 3456 0.4646 0.4477 0.6267 0.5029 0.1837 0.2374 0.3451 0.5798 0.7665

0.3548 49.0 3528 0.4598 0.4394 0.6245 0.4885 0.1793 0.2351 0.3647 0.6016 0.7815

0.3375 50.0 3600 0.4441 0.4271 0.6226 0.4487 0.1729 0.2293 0.3808 0.6354 0.8075

0.3315 51.0 3672 0.4613 0.4403 0.6292 0.4868 0.1805 0.2373 0.3630 0.6016 0.7905

0.3313 52.0 3744 0.4445 0.4307 0.6442 0.4108 0.1746 0.2342 0.3942 0.6577 0.7932

0.3372 53.0 3816 0.4456 0.4258 0.6269 0.4404 0.1720 0.2308 0.3924 0.6489 0.8027

0.3285 54.0 3888 0.4526 0.4348 0.6241 0.4723 0.1772 0.2328 0.3615 0.6160 0.8027

0.3474 55.0 3960 0.4498 0.4369 0.6258 0.4595 0.1782 0.2315 0.3617 0.6070 0.7978

0.3349 56.0 4032 0.4613 0.4428 0.6307 0.4858 0.1819 0.2376 0.3523 0.6012 0.7875

0.3207 57.0 4104 0.4476 0.4342 0.6230 0.4500 0.1765 0.2289 0.3658 0.6151 0.7910

0.3399 58.0 4176 0.4600 0.4413 0.6248 0.4940 0.1812 0.2360 0.3531 0.5954 0.7814

0.3327 59.0 4248 0.4463 0.4339 0.6215 0.4570 0.1770 0.2294 0.3590 0.6069 0.8063

0.3215 60.0 4320 0.4482 0.4317 0.6203 0.4595 0.1756 0.2295 0.3698 0.6154 0.8034

0.3276 61.0 4392 0.4406 0.4218 0.6192 0.4370 0.1705 0.2268 0.3878 0.6425 0.8111

0.3179 62.0 4464 0.4530 0.4331 0.6217 0.4765 0.1764 0.2327 0.3660 0.6121 0.8068

0.3129 63.0 4536 0.4614 0.4398 0.6263 0.5002 0.1803 0.2378 0.3529 0.6023 0.7974

0.3354 64.0 4608 0.4538 0.4374 0.6234 0.4777 0.1788 0.2333 0.3565 0.6013 0.7995

0.3261 65.0 4680 0.4367 0.4258 0.6283 0.4249 0.1724 0.2291 0.3861 0.6484 0.8026

0.3114 66.0 4752 0.4565 0.4366 0.6225 0.4852 0.1780 0.2334 0.3647 0.6073 0.7939

0.3377 67.0 4824 0.4519 0.4308 0.6185 0.4771 0.1755 0.2314 0.3681 0.6175 0.8079

0.3266 68.0 4896 0.4372 0.4216 0.6167 0.4345 0.1702 0.2245 0.3850 0.6392 0.8163

0.3347 69.0 4968 0.4343 0.4193 0.6179 0.4318 0.1690 0.2252 0.3890 0.6557 0.8114

0.3207 70.0 5040 0.4426 0.4269 0.6180 0.4465 0.1728 0.2266 0.3810 0.6296 0.8038

0.3313 71.0 5112 0.4362 0.4234 0.6177 0.4360 0.1712 0.2252 0.3777 0.6386 0.8133

0.326 72.0 5184 0.4392 0.4251 0.6182 0.4431 0.1723 0.2265 0.3783 0.6356 0.8088

0.3141 73.0 5256 0.4532 0.4385 0.6214 0.4818 0.1796 0.2327 0.3513 0.5981 0.8044

0.3301 74.0 5328 0.4536 0.4361 0.6230 0.4808 0.1783 0.2333 0.3585 0.6097 0.8037

0.3194 75.0 5400 0.4501 0.4335 0.6216 0.4698 0.1765 0.2312 0.3623 0.6164 0.8033

0.3071 76.0 5472 0.4455 0.4310 0.6201 0.4598 0.1751 0.2292 0.3625 0.6231 0.8087

0.3174 77.0 5544 0.4472 0.4316 0.6219 0.4625 0.1756 0.2307 0.3654 0.6256 0.8022

0.3171 78.0 5616 0.4461 0.4305 0.6204 0.4614 0.1750 0.2298 0.3663 0.6263 0.8052

0.3244 79.0 5688 0.4501 0.4328 0.6226 0.4725 0.1765 0.2324 0.3611 0.6233 0.8083

0.3188 80.0 5760 0.4427 0.4280 0.6199 0.4507 0.1735 0.2281 0.3757 0.6307 0.8054

0.3212 81.0 5832 0.4383 0.4222 0.6196 0.4365 0.1702 0.2266 0.3875 0.6476 0.8093

0.3234 82.0 5904 0.4434 0.4278 0.6216 0.4479 0.1735 0.2288 0.3728 0.6337 0.8064

0.3024 83.0 5976 0.4502 0.4331 0.6214 0.4728 0.1764 0.2317 0.3645 0.6192 0.8070

0.3145 84.0 6048 0.4409 0.4258 0.6199 0.4475 0.1726 0.2280 0.3778 0.6357 0.8075

0.329 85.0 6120 0.4491 0.4302 0.6221 0.4710 0.1749 0.2322 0.3755 0.6246 0.8065

0.3034 86.0 6192 0.4504 0.4321 0.6241 0.4699 0.1757 0.2325 0.3767 0.6217 0.8035

0.3074 87.0 6264 0.4373 0.4224 0.6188 0.4396 0.1706 0.2267 0.3878 0.6439 0.8107

0.3089 88.0 6336 0.4379 0.4235 0.6191 0.4402 0.1709 0.2266 0.3893 0.6410 0.8089

0.2995 89.0 6408 0.4448 0.4292 0.6193 0.4597 0.1744 0.2292 0.3740 0.6225 0.8065

0.3248 90.0 6480 0.4413 0.4279 0.6227 0.4494 0.1732 0.2283 0.3766 0.6305 0.8100

0.3203 91.0 6552 0.4445 0.4290 0.6213 0.4568 0.1740 0.2295 0.3754 0.6290 0.8085

0.3109 92.0 6624 0.4452 0.4295 0.6203 0.4597 0.1744 0.2297 0.3749 0.6245 0.8035

0.3241 93.0 6696 0.4419 0.4258 0.6190 0.4533 0.1725 0.2285 0.3833 0.6313 0.8092

0.3078 94.0 6768 0.4446 0.4278 0.6201 0.4597 0.1736 0.2297 0.3778 0.6284 0.8097

0.3141 95.0 6840 0.4466 0.4305 0.6208 0.4660 0.1749 0.2306 0.3720 0.6233 0.8058

0.3198 96.0 6912 0.4440 0.4275 0.6194 0.4584 0.1736 0.2293 0.3774 0.6279 0.8088

0.3 97.0 6984 0.4426 0.4269 0.6192 0.4545 0.1731 0.2287 0.3770 0.6302 0.8091

0.3096 98.0 7056 0.4433 0.4274 0.6197 0.4568 0.1735 0.2292 0.3765 0.6296 0.8088

0.3317 99.0 7128 0.4394 0.4244 0.6196 0.4448 0.1716 0.2276 0.3844 0.6374 0.8096

0.3132 100.0 7200 0.4425 0.4270 0.6196 0.4543 0.1732 0.2288 0.3787 0.6298 0.8083

前往AI网址导航

sayakpaul/glpn-nyu-finetuned-diode-230131-041708

glpn-nyu-finetuned-diode-230131-041708

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

腾讯会议怎么看回放腾讯会议看回放教程

腾讯会议如何导出参会成员腾讯会议导出参会成员教程

腾讯会议怎么放PPT 腾讯会议放PPT教程

腾讯会议怎么静音腾讯会议静音教程

腾讯会议怎么共享屏幕腾讯会议共享屏幕方法

酷狗音乐怎么找伴奏酷狗音乐找伴奏的方法

sayakpaul/glpn-nyu-finetuned-diode-230131-041708

glpn-nyu-finetuned-diode-230131-041708

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

腾讯会议怎么看回放 腾讯会议看回放教程

腾讯会议如何导出参会成员 腾讯会议导出参会成员教程

腾讯会议怎么放PPT 腾讯会议放PPT教程

腾讯会议怎么静音 腾讯会议静音教程

腾讯会议怎么共享屏幕 腾讯会议共享屏幕方法

酷狗音乐怎么找伴奏 酷狗音乐找伴奏的方法

腾讯会议怎么看回放腾讯会议看回放教程

腾讯会议如何导出参会成员腾讯会议导出参会成员教程

腾讯会议怎么静音腾讯会议静音教程

腾讯会议怎么共享屏幕腾讯会议共享屏幕方法

酷狗音乐怎么找伴奏酷狗音乐找伴奏的方法