Weather Prediction Benchmarks

We provide benchmark results of spatiotemporal prediction learning (STL) methods on the famous weather prediction datasets, WeatherBench. More STL methods will be supported in the future. Issues and PRs are welcome! Currently, we only provide benchmark results, trained models and logs will be released soon (you can contact us if you require these files). You can download model files from Baidu Cloud (brlk).

Table of Contents

• WeatherBench Benchmarks

  • STL/MetaVP Benchmarks on Temperature (t2m)

  • STL/MetaVP Benchmarks on Humidity (r)

  • STL/MetaVP Benchmarks on Wind Component (uv10)

  • STL/MetaVP Benchmarks on Cloud Cover (tcc)

  • STL/MetaVP Benchmarks on Multiple Variants (MV)

Currently supported spatiotemporal prediction methods

• [x] ConvLSTM (NeurIPS’2015)

• [x] PredNet (ICLR’2017)

• [x] PredRNN (NeurIPS’2017)

• [x] PredRNN++ (ICML’2018)

• [x] E3D-LSTM (ICLR’2018)

• [x] MIM (CVPR’2019)

• [x] CrevNet (ICLR’2020)

• [x] PhyDNet (CVPR’2020)

• [x] MAU (NeurIPS’2021)

• [x] PredRNN.V2 (TPAMI’2022)

• [x] SimVP (CVPR’2022)

• [x] SimVP.V2 (ArXiv’2022)

• [x] TAU (CVPR’2023)

• [x] DMVFN (CVPR’2023)

Currently supported MetaFormer models for SimVP

• [x] ViT (ICLR’2021)

• [x] Swin-Transformer (ICCV’2021)

• [x] MLP-Mixer (NeurIPS’2021)

• [x] ConvMixer (Openreview’2021)

• [x] UniFormer (ICLR’2022)

• [x] PoolFormer (CVPR’2022)

• [x] ConvNeXt (CVPR’2022)

• [x] VAN (ArXiv’2022)

• [x] IncepU (SimVP.V1) (CVPR’2022)

• [x] gSTA (SimVP.V2) (ArXiv’2022)

• [x] HorNet (NeurIPS’2022)

• [x] MogaNet (ArXiv’2022)

WeatherBench Benchmarks

We provide temperature prediction benchmark results on the popular WeatherBench dataset (temperature prediction t2m) using \(12\rightarrow 12\) frames prediction setting. Metrics (MSE, MAE, SSIM, pSNR) of the best models are reported in three trials. Parameters (M), FLOPs (G), and V100 inference FPS (s) are also reported for all methods. All methods are trained by Adam optimizer with Cosine Annealing scheduler (no warmup and min lr is 1e-6).

STL Benchmarks on Temperature (t2m)

Similar to Moving MNIST Benchmarks, we benchmark STL methods and Metaformer architectures on SimVP training 50 epochs with single GPU on t2m (K). We provide config files in configs/weather/t2m_5_625 for 5.625 settings (\(32\times 64\) resolutions).

Method	Setting	Params	FLOPs	FPS	MSE	MAE	RMSE	Download
ConvLSTM	50 epoch	14.98M	136G	46	1.521	0.7949	1.233	model | log
E3D-LSTM	50 epoch	51.09M	169G	35	1.592	0.8059	1.262	model | log
PhyDNet	50 epoch	3.09M	36.8G	177	285.9	8.7370	16.91	model | log
PredRNN	50 epoch	23.57M	278G	22	1.331	0.7246	1.154	model | log
PredRNN++	50 epoch	38.31M	413G	15	1.634	0.7883	1.278	model | log
MIM	50 epoch	37.75M	109G	126	1.784	0.8716	1.336	model | log
MAU	50 epoch	5.46M	39.6G	237	1.251	0.7036	1.119	model | log
PredRNNv2	50 epoch	23.59M	279G	22	1.545	0.7986	1.243	model | log
IncepU (SimVPv1)	50 epoch	14.67M	8.03G	160	1.238	0.7037	1.113	model | log
gSTA (SimVPv2)	50 epoch	12.76M	7.01G	504	1.105	0.6567	1.051	model | log
ViT	50 epoch	12.41M	7.99G	432	1.146	0.6712	1.070	model | log
Swin Transformer	50 epoch	12.42M	6.88G	581	1.143	0.6735	1.069	model | log
Uniformer	50 epoch	12.02M	7.45G	465	1.204	0.6885	1.097	model | log
MLP-Mixer	50 epoch	11.10M	5.92G	713	1.255	0.7011	1.119	model | log
ConvMixer	50 epoch	1.13M	0.95G	1705	1.267	0.7073	1.126	model | log
Poolformer	50 epoch	9.98M	5.61G	722	1.156	0.6715	1.075	model | log
ConvNeXt	50 epoch	10.09M	5.66G	689	1.277	0.7220	1.130	model | log
VAN	50 epoch	12.15M	6.70G	523	1.150	0.6803	1.072	model | log
HorNet	50 epoch	12.42M	6.84G	517	1.201	0.6906	1.096	model | log
MogaNet	50 epoch	12.76M	7.01G	416	1.152	0.6665	1.073	model | log
TAU	50 epoch	12.22M	6.70G	511	1.162	0.6707	1.078	model | log

Then, we also provide the high-resolution benchmark of t2m using the similar training settings with 4GPUs (4xbs4). The config files are in configs/weather/t2m_1_40625 for 1.40625 settings (\(128\times 256\) resolutions).

Method	Setting	Params	FLOPs	FPS	MSE	MAE	RMSE	Download
ConvLSTM	50 epoch	15.08M	550G	35	1.0625	0.6517	1.031	model | log
PhyDNet	50 epoch	3.09M	148G	41	297.34	8.9788	17.243	model | log
PredRNN	50 epoch	23.84M	1123G	3	0.8966	0.5869	0.9469	model | log
PredRNN++	50 epoch	38.58M	1663G	2	0.8538	0.5708	0.9240	model | log
MIM	50 epoch	42.17M	1739G	11	1.2138	0.6857	1.1017	model | log
MAU	50 epoch	11.82M	172G	17	1.0031	0.6316	1.0016	model | log
PredRNNv2	50 epoch	23.86M	1129G	3	1.0451	0.6190	1.0223	model | log
IncepU (SimVPv1)	50 epoch	14.67M	128G	27	0.8492	0.5636	0.9215	model | log
gSTA (SimVPv2)	50 epoch	12.76M	112G	33	0.6499	0.4909	0.8062	model | log
ViT	50 epoch	12.48M	36.8G	50	0.8969	0.5834	0.9470	model | log
Swin Transformer	50 epoch	12.42M	110G	38	0.7606	0.5193	0.8721	model | log
Uniformer	50 epoch	12.09M	48.8G	57	1.0052	0.6294	1.0026	model | log
MLP-Mixer	50 epoch	27.87M	94.7G	49	1.1865	0.6593	1.0893	model | log
ConvMixer	50 epoch	1.14M	15.1G	117	0.8557	0.5669	0.9250	model | log
Poolformer	50 epoch	9.98M	89.7G	42	0.7983	0.5316	0.8935	model | log
ConvNeXt	50 epoch	10.09M	90.5G	47	0.8058	0.5406	0.8976	model | log
VAN	50 epoch	12.15M	107G	34	0.7110	0.5094	0.8432	model | log
HorNet	50 epoch	12.42M	109G	34	0.8250	0.5467	0.9083	model | log
MogaNet	50 epoch	12.76M	112G	27	0.7517	0.5232	0.8670	model | log
TAU	50 epoch	12.29M	36.1G	94	0.8316	0.5615	0.9119	model | log

(back to top)

STL Benchmarks on Humidity (r)

Similar to Weather Benchmark, we benchmark STL methods and Metaformer architectures on SimVP training 50 epochs with single GPU on r (%). We provide config files in configs/weather/r_5_625 for 5.625 settings (\(32\times 64\) resolutions).

Method	Setting	Params	FLOPs	FPS	MSE	MAE	RMSE	Download
ConvLSTM	50 epoch	14.98M	136G	46	35.146	4.012	5.928	model | log
E3D-LSTM	50 epoch	51.09M	169G	35	36.534	4.100	6.044	model | log
PhyDNet	50 epoch	3.09M	36.8G	177	239.00	8.975	15.46	model | log
PredRNN	50 epoch	23.57M	278G	22	37.611	4.096	6.133	model | log
PredRNN++	50 epoch	38.31M	413G	15	35.146	4.012	5.928	model | log
MIM	50 epoch	37.75M	109G	126	36.534	4.100	6.044	model | log
MAU	50 epoch	5.46M	39.6G	237	34.529	4.004	5.876	model | log
PredRNNv2	50 epoch	23.59M	279G	22	36.508	4.087	6.042	model | log
IncepU (SimVPv1)	50 epoch	14.67M	8.03G	160	34.355	3.994	5.861	model | log
gSTA (SimVPv2)	50 epoch	12.76M	7.01G	504	31.426	3.765	5.606	model | log
ViT	50 epoch	12.41M	7.99G	432	32.616	3.852	5.711	model | log
Swin Transformer	50 epoch	12.42M	6.88G	581	31.332	3.776	5.597	model | log
Uniformer	50 epoch	12.02M	7.45G	465	32.199	3.864	5.674	model | log
MLP-Mixer	50 epoch	11.10M	5.92G	713	34.467	3.950	5.871	model | log
ConvMixer	50 epoch	1.13M	0.95G	1705	32.829	3.909	5.730	model | log
Poolformer	50 epoch	9.98M	5.61G	722	31.989	3.803	5.656	model | log
ConvNeXt	50 epoch	10.09M	5.66G	689	33.179	3.928	5.760	model | log
VAN	50 epoch	12.15M	6.70G	523	31.712	3.812	5.631	model | log
HorNet	50 epoch	12.42M	6.84G	517	32.081	3.826	5.664	model | log
MogaNet	50 epoch	12.76M	7.01G	416	31.795	3.816	5.639	model | log
TAU	50 epoch	12.22M	6.70G	511	31.831	3.818	5.642	model | log

STL Benchmarks on Wind Component (uv10)

Similar to Weather Benchmark, we benchmark STL methods and Metaformer architectures on SimVP training 50 epochs with single GPU on uv10 (ms-1). We provide config files in configs/weather/uv10_5_625 for 5.625 settings (\(32\times 64\) resolutions). Notice that the input data of uv10 has two channels.

Method	Setting	Params	FLOPs	FPS	MSE	MAE	RMSE	Download
ConvLSTM	50 epoch	14.98M	136G	43	1.8976	0.9215	1.3775	model | log
E3D-LSTM	50 epoch	51.81M	171G	35	2.4111	1.0342	1.5528	model | log
PhyDNet	50 epoch	3.09M	36.8G	172	16.798	2.9208	4.0986	model | log
PredRNN	50 epoch	23.65M	279G	21	1.8810	0.9068	1.3715	model | log
PredRNN++	50 epoch	38.40M	414G	14	1.8727	0.9019	1.3685	model | log
MIM	50 epoch	37.75M	109G	122	3.1399	1.1837	1.7720	model | log
MAU	50 epoch	5.46M	39.6G	233	1.9001	0.9194	1.3784	model | log
PredRNNv2	50 epoch	23.68M	280G	21	2.0072	0.9413	1.4168	model | log
IncepU (SimVPv1)	50 epoch	14.67M	8.04G	154	1.9993	0.9510	1.4140	model | log
gSTA (SimVPv2)	50 epoch	12.76M	7.02G	498	1.5069	0.8142	1.2276	model | log
ViT	50 epoch	12.42M	8.0G	427	1.6262	0.8438	1.2752	model | log
Swin Transformer	50 epoch	12.42M	6.89G	577	1.4996	0.8145	1.2246	model | log
Uniformer	50 epoch	12.03M	7.46G	459	1.4850	0.8085	1.2186	model | log
MLP-Mixer	50 epoch	11.10M	5.93G	707	1.6066	0.8395	1.2675	model | log
ConvMixer	50 epoch	1.14M	0.96G	1698	1.7067	0.8714	1.3064	model | log
Poolformer	50 epoch	9.99M	5.62G	717	1.6123	0.8410	1.2698	model | log
ConvNeXt	50 epoch	10.09M	5.67G	682	1.6914	0.8698	1.3006	model | log
VAN	50 epoch	12.15M	6.71G	520	1.5958	0.8371	1.2632	model | log
HorNet	50 epoch	12.42M	6.85G	513	1.5539	0.8254	1.2466	model | log
MogaNet	50 epoch	12.76M	7.01G	411	1.6072	0.8451	1.2678	model | log
TAU	50 epoch	12.22M	6.70G	505	1.5925	0.8426	1.2619	model | log

STL Benchmarks on Cloud Cover (tcc)

Similar to Weather Benchmark, we benchmark STL methods and Metaformer architectures on SimVP training 50 epochs with single GPU on tcc (%). We provide config files in configs/weather/tcc_5_625 for 5.625 settings (\(32\times 64\) resolutions).

Method	Setting	Params	FLOPs	FPS	MSE	MAE	RMSE	Download
ConvLSTM	50 epoch	14.98M	136G	46	0.04944	0.15419	0.22234	model | log
E3D-LSTM	50 epoch	51.09M	169G	35	0.05729	0.15293	0.23936	model | log
PhyDNet	50 epoch	3.09M	36.8G	172	0.09913	0.22614	0.31485	model | log
PredRNN	50 epoch	23.57M	278G	22	0.05504	0.15877	0.23461	model | log
PredRNN++	50 epoch	38.31M	413G	15	0.05479	0.15435	0.23407	model | log
MIM	50 epoch	37.75M	109G	126	0.05729	0.15293	0.23936	model | log
MAU	50 epoch	5.46M	39.6G	237	0.04955	0.15158	0.22260	model | log
PredRNNv2	50 epoch	23.59M	279G	22	0.05051	0.15867	0.22475	model | log
IncepU (SimVPv1)	50 epoch	14.67M	8.03G	160	0.04765	0.15029	0.21829	model | log
gSTA (SimVPv2)	50 epoch	12.76M	7.01G	504	0.04657	0.14688	0.21580	model | log
ViT	50 epoch	12.41M	7.99G	432	0.04778	0.15026	0.21859	model | log
Swin Transformer	50 epoch	12.42M	6.88G	581	0.04639	0.14729	0.21539	model | log
Uniformer	50 epoch	12.02M	7.45G	465	0.04680	0.14777	0.21634	model | log
MLP-Mixer	50 epoch	11.10M	5.92G	713	0.04925	0.15264	0.22192	model | log
ConvMixer	50 epoch	1.13M	0.95G	1705	0.04717	0.14874	0.21718	model | log
Poolformer	50 epoch	9.98M	5.61G	722	0.04694	0.14884	0.21667	model | log
ConvNeXt	50 epoch	10.09M	5.66G	689	0.04742	0.14867	0.21775	model | log
VAN	50 epoch	12.15M	6.70G	523	0.04694	0.14725	0.21665	model | log
HorNet	50 epoch	12.42M	6.84G	517	0.04692	0.14751	0.21661	model | log
MogaNet	50 epoch	12.76M	7.01G	416	0.04699	0.14802	0.21676	model | log
TAU	50 epoch	12.22M	6.70G	511	0.04723	0.14604	0.21733	model | log

(back to top)

STL Benchmarks on Multiple Variants (MV)

Using the similar setting as Weather Benchmark, we benchmark popular Metaformer architectures on SimVP training 50 epochs with single GPU on r, t, u, v, which have 13 levels and we chose 3 levels (150m, 500m, and 850m). We provide config files in configs/weather/mv_4_s6_5_625 for 5.625 settings (\(32\times 64\) resolutions). Here, we employ Adam optimizer with Cosine Annealing scheduler (5-epoch warmup and min lr is 1e-6) to various methods. We provide results of each variant and the sum of four variants.

Method (sum)	Params	FLOPs	FPS	MSE(sum)	MAE(sum)	RMSE(sum)	Download
ConvLSTM	15.50M	43.33G	11	108.81	5.7439	8.1810	model | log
PhyDNet	3.10M	11.25G	14	228.83	10.109	13.213	model | log
PredRNN	24.56M	88.02G	5	104.16	5.5373	7.9553	model | log
PredRNN++	39.31M	129.0G	4	106.77	5.5821	8.0568	model | log
MIM	41.71M	35.77G	17	121.95	6.2786	8.7376	model | log
MAU	5.46M	12.07G	21	106.13	5.6487	7.9928	model | log
PredRNNv2	24.58M	88.49G	5	108.94	5.7747	8.1872	model | log
IncepU (SimVPv1)	13.80M	7.26G	16	108.50	5.7360	8.1165	model | log
gSTA (SimVPv2)	9.96M	5.25G	18	103.36	5.4856	7.9059	model | log
ViT	9.66M	6.12G	8	102.90	5.4776	7.8643	model | log
Swin Transformer	9.66M	5.15G	24	102.14	5.4400	7.8325	model | log
Uniformer	9.53M	5.85G	9	102.39	5.4361	7.8225	model | log
MLP-Mixer	8.74M	4.39G	32	107.65	5.6820	8.1058	model | log
ConvMixer	0.85M	0.49G	157	112.76	5.9114	8.3238	model | log
Poolformer	7.76M	4.14G	26	114.05	5.9979	8.4760	model | log
ConvNeXt	7.85M	4.19G	42	108.66	5.7432	8.1763	model | log
VAN	9.49M	5.01G	21	99.816	5.3351	7.7075	model | log
HorNet	9.68M	5.12G	21	104.21	5.5181	7.9854	model | log
MogaNet	9.97M	5.25G	17	98.664	5.3003	7.6539	model | log
TAU	9.55M	5.01G	21	99.428	5.3282	7.6855	model | log

Method (r)	Params	FLOPs	FPS	MSE(r)	MAE(r)	RMSE(r)	Download
ConvLSTM	15.50M	43.33G	11	368.15	13.490	19.187	model | log
PhyDNet	3.10M	11.25G	14	668.40	21.398	25.853	model | log
PredRNN	24.56M	88.02G	5	354.57	13.169	18.830	model | log
PredRNN++	39.31M	129.0G	4	363.15	13.246	19.056	model | log
MIM	41.71M	35.77G	17	408.24	14.658	20.205	model | log
MAU	5.46M	12.07G	21	363.36	13.503	19.062	model | log
PredRNNv2	24.58M	88.49G	5	368.52	13.594	19.197	model | log
IncepU (SimVPv1)	13.80M	7.26G	16	370.03	13.584	19.236	model | log
gSTA (SimVPv2)	9.96M	5.25G	18	352.79	13.021	18.783	model | log
ViT	9.66M	6.12G	8	352.36	13.056	18.771	model | log
Swin Transformer	9.66M	5.15G	24	349.92	12.984	18.706	model | log
Uniformer	9.53M	5.85G	9	351.66	12.994	18.753	model | log
MLP-Mixer	8.74M	4.39G	32	365.48	13.408	19.118	model | log
ConvMixer	0.85M	0.49G	157	381.85	13.917	19.541	model | log
Poolformer	7.76M	4.14G	26	380.18	13.908	19.498	model | log
ConvNeXt	7.85M	4.19G	42	367.39	13.516	19.168	model | log
VAN	9.49M	5.01G	21	343.61	12.790	18.537	model | log
HorNet	9.68M	5.12G	21	353.02	13.024	18.789	model | log
MogaNet	9.97M	5.25G	17	340.06	12.738	18.441	model | log
TAU	9.55M	5.01G	21	342.63	12.801	18.510	model | log

Method (t)	Params	FLOPs	FPS	MSE(t)	MAE(t)	RMSE(t)	Download
ConvLSTM	15.50M	43.33G	11	6.3034	1.7695	2.5107	model | log
PhyDNet	3.10M	11.25G	14	95.113	6.4749	9.7526	model | log
PredRNN	24.56M	88.02G	5	5.5966	1.6411	2.3657	model | log
PredRNN++	39.31M	129.0G	4	5.6471	1.6433	2.3763	model | log
MIM	41.71M	35.77G	17	7.5152	1.9650	2.7414	model | log
MAU	5.46M	12.07G	21	5.6287	1.6810	2.3725	model | log
PredRNNv2	24.58M	88.49G	5	6.3078	1.7770	2.5110	model | log
IncepU (SimVPv1)	13.80M	7.26G	16	6.1068	1.7554	2.4712	model | log
gSTA (SimVPv2)	9.96M	5.25G	18	5.4382	1.6129	2.3319	model | log
ViT	9.66M	6.12G	8	5.2722	1.6005	2.2961	model | log
Swin Transformer	9.66M	5.15G	24	5.2486	1.5856	2.2910	model | log
Uniformer	9.53M	5.85G	9	5.1174	1.5758	2.2622	model | log
MLP-Mixer	8.74M	4.39G	32	5.8546	1.6948	2.4196	model | log
ConvMixer	0.85M	0.49G	157	6.5838	1.8228	2.5660	model | log
Poolformer	7.76M	4.14G	26	7.1077	1.8791	2.6660	model | log
ConvNeXt	7.85M	4.19G	42	6.1749	1.7448	2.4849	model | log
VAN	9.49M	5.01G	21	4.9396	1.5390	2.2225	model | log
HorNet	9.68M	5.12G	21	5.5856	1.6198	2.3634	model | log
MogaNet	9.97M	5.25G	17	4.8335	1.5246	2.1985	model | log
TAU	9.55M	5.01G	21	4.9042	1.5341	2.2145	model | log

Method (u)	Params	FLOPs	FPS	MSE(u)	MAE(u)	RMSE(u)	Download
ConvLSTM	15.50M	43.33G	11	30.002	3.8923	5.4774	model | log
PhyDNet	3.10M	11.25G	14	97.424	7.3637	9.8704	model | log
PredRNN	24.56M	88.02G	5	27.484	3.6776	5.2425	model | log
PredRNN++	39.31M	129.0G	4	28.396	3.7322	5.3288	model | log
MIM	41.71M	35.77G	17	35.586	4.2842	5.9654	model | log
MAU	5.46M	12.07G	21	27.582	3.7409	5.2519	model | log
PredRNNv2	24.58M	88.49G	5	29.833	3.8870	5.4620	model | log
IncepU (SimVPv1)	13.80M	7.26G	16	28.782	3.8435	5.3649	model | log
gSTA (SimVPv2)	9.96M	5.25G	18	27.166	3.6747	5.2121	model | log
ViT	9.66M	6.12G	8	26.595	3.6472	5.1570	model | log
Swin Transformer	9.66M	5.15G	24	26.292	3.6133	5.1276	model | log
Uniformer	9.53M	5.85G	9	25.994	3.6069	5.0985	model | log
MLP-Mixer	8.74M	4.39G	32	29.242	3.8407	5.4076	model | log
ConvMixer	0.85M	0.49G	157	30.983	3.9949	5.5662	model | log
Poolformer	7.76M	4.14G	26	33.757	4.1280	5.8101	model | log
ConvNeXt	7.85M	4.19G	42	29.764	3.8688	5.4556	model | log
VAN	9.49M	5.01G	21	24.991	3.5254	4.9991	model | log
HorNet	9.68M	5.12G	21	28.192	3.7142	5.3096	model | log
MogaNet	9.97M	5.25G	17	24.535	3.4882	4.9533	model | log
TAU	9.55M	5.01G	21	24.719	3.5060	4.9719	model | log

Method (v)	Params	FLOPs	FPS	MSE(v)	MAE(v)	RMSE(v)	Download
ConvLSTM	15.50M	43.33G	11	30.789	3.8238	5.5488	model | log
PhyDNet	3.10M	11.25G	14	54.389	5.1996	7.3749	model | log
PredRNN	24.56M	88.02G	5	28.973	3.6617	5.3827	model | log
PredRNN++	39.31M	129.0G	4	29.872	3.7067	5.4655	model | log
MIM	41.71M	35.77G	17	36.464	4.2066	6.0386	model | log
MAU	5.46M	12.07G	21	27.929	3.6700	5.2848	model | log
PredRNNv2	24.58M	88.49G	5	31.119	3.8406	5.5785	model | log
IncepU (SimVPv1)	13.80M	7.26G	16	29.094	3.7614	5.3939	model | log
gSTA (SimVPv2)	9.96M	5.25G	18	28.058	3.6335	5.2970	model | log
ViT	9.66M	6.12G	8	27.381	3.6068	5.2327	model | log
Swin Transformer	9.66M	5.15G	24	27.097	3.5777	5.2055	model | log
Uniformer	9.53M	5.85G	9	26.799	3.5676	5.1768	model | log
MLP-Mixer	8.74M	4.39G	32	30.014	3.7840	5.4785	model | log
ConvMixer	0.85M	0.49G	157	31.609	3.9104	5.6222	model | log
Poolformer	7.76M	4.14G	26	35.161	4.0764	5.9296	model | log
ConvNeXt	7.85M	4.19G	42	31.326	3.8435	5.5969	model | log
VAN	9.49M	5.01G	21	25.720	3.4858	5.0715	model | log
HorNet	9.68M	5.12G	21	30.028	3.7148	5.4798	model | log
MogaNet	9.97M	5.25G	17	25.232	3.4509	5.0231	model | log
TAU	9.55M	5.01G	21	25.456	3.4723	5.0454	model | log

(back to top)