videomae-small-finetuned-kinetics-finetuned-SoccerNetChunks-NoInference

This model is a fine-tuned version of MCG-NJU/videomae-small-finetuned-kinetics on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.9787
Accuracy: 0.6333
Balanced Accuracy: 0.6333
Matthews Correlation: 0.5649
Confusion Matrix: [[1007 111 66 107 22 59] [ 222 935 74 50 19 71] [ 114 27 969 172 77 11] [ 240 50 259 686 103 32] [ 154 59 299 489 343 27] [ 72 20 6 2 2 1268]]
0 Ball out of play: {'precision': 0.556661138750691, 'recall': 0.7339650145772595, 'f1-score': 0.6331342345174474, 'support': 1372.0}
Precision 0: 0.5567
Recall 0: 0.7340
F1-score 0: 0.6331
Support 0: 1372.0
1 Foul: {'precision': 0.7778702163061564, 'recall': 0.6819839533187454, 'f1-score': 0.7267780800621843, 'support': 1371.0}
Precision 1: 0.7779
Recall 1: 0.6820
F1-score 1: 0.7268
Support 1: 1371.0
2 Goal: {'precision': 0.5791990436341901, 'recall': 0.7072992700729926, 'f1-score': 0.6368715083798882, 'support': 1370.0}
Precision 2: 0.5792
Recall 2: 0.7073
F1-score 2: 0.6369
Support 2: 1370.0
3 Shots off target: {'precision': 0.4555112881806109, 'recall': 0.5007299270072992, 'f1-score': 0.4770514603616134, 'support': 1370.0}
Precision 3: 0.4555
Recall 3: 0.5007
F1-score 3: 0.4771
Support 3: 1370.0
4 Shots on target: {'precision': 0.6060070671378092, 'recall': 0.25018234865062, 'f1-score': 0.3541559112028911, 'support': 1371.0}
Precision 4: 0.6060
Recall 4: 0.2502
F1-score 4: 0.3542
Support 4: 1371.0
5 Throw-in: {'precision': 0.8637602179836512, 'recall': 0.9255474452554745, 'f1-score': 0.8935870331219168, 'support': 1370.0}
Precision 5: 0.8638
Recall 5: 0.9255
F1-score 5: 0.8936
Support 5: 1370.0
Precision Macro avg: 0.6398
Recall Macro avg: 0.6333
F1-score Macro avg: 0.6203
Support Macro avg: 8224.0
Precision Weighted avg: 0.6398
Recall Weighted avg: 0.6333
F1-score Weighted avg: 0.6202
Support Weighted avg: 8224.0

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: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
distributed_type: multi-GPU
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
training_steps: 20620

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Balanced Accuracy	Matthews Correlation	Confusion Matrix	0 Ball out of play	Precision 0	Recall 0	F1-score 0	Support 0	1 Foul	Precision 1	Recall 1	F1-score 1	Support 1	2 Goal	Precision 2	Recall 2	F1-score 2	Support 2	3 Shots off target	Precision 3	Recall 3	F1-score 3	Support 3	4 Shots on target	Precision 4	Recall 4	F1-score 4	Support 4	5 Throw-in	Precision 5	Recall 5	F1-score 5	Support 5	Precision Macro avg
1.5371	0.05	1031	1.2696	0.4884	0.4885	0.3949	[[ 214 227 131 266 173 361]
[ 24 763 108 72 97 307]
[ 20 29 893 202 140 86]
[ 34 32 436 460 320 88]
[ 18 21 459 363 403 107]
[ 3 22 24 14 23 1284]]	{'precision': 0.6837060702875399, 'recall': 0.15597667638483964, 'f1-score': 0.2540059347181009, 'support': 1372.0}	0.6837	0.1560	0.2540	1372.0	{'precision': 0.6974405850091407, 'recall': 0.5565280816921955, 'f1-score': 0.6190669371196754, 'support': 1371.0}	0.6974	0.5565	0.6191	1371.0	{'precision': 0.4353973671379815, 'recall': 0.6518248175182482, 'f1-score': 0.5220695703010816, 'support': 1370.0}	0.4354	0.6518	0.5221	1370.0	{'precision': 0.33405954974582425, 'recall': 0.3357664233576642, 'f1-score': 0.3349108117946851, 'support': 1370.0}	0.3341	0.3358	0.3349	1370.0	{'precision': 0.3486159169550173, 'recall': 0.2939460247994165, 'f1-score': 0.3189552829442026, 'support': 1371.0}	0.3486	0.2939	0.3190	1371.0	{'precision': 0.5750111957008509, 'recall': 0.9372262773722628, 'f1-score': 0.7127393838467944, 'support': 1370.0}	0.5750	0.9372	0.7127	1370.0	0.5124	0.4885	0.4603	8224.0	0.5124	0.4884	0.4602	8224.0
0.946	0.1	2062	1.1950	0.4993	0.4993	0.4176	[[1020 44 64 224 10 10]
[ 510 602 79 135 24 21]
[ 117 25 758 434 30 6]
[ 206 32 217 883 25 7]
[ 156 21 238 889 61 6]
[ 394 48 39 102 5 782]]	{'precision': 0.42446941323345816, 'recall': 0.7434402332361516, 'f1-score': 0.5403973509933775, 'support': 1372.0}	0.4245	0.7434	0.5404	1372.0	{'precision': 0.7797927461139896, 'recall': 0.4390955506929249, 'f1-score': 0.5618292113859076, 'support': 1371.0}	0.7798	0.4391	0.5618	1371.0	{'precision': 0.5433691756272402, 'recall': 0.5532846715328467, 'f1-score': 0.5482820976491862, 'support': 1370.0}	0.5434	0.5533	0.5483	1370.0	{'precision': 0.33108361454818147, 'recall': 0.6445255474452555, 'f1-score': 0.43745355461976715, 'support': 1370.0}	0.3311	0.6445	0.4375	1370.0	{'precision': 0.3935483870967742, 'recall': 0.04449307075127644, 'f1-score': 0.0799475753604194, 'support': 1371.0}	0.3935	0.0445	0.0799	1371.0	{'precision': 0.9399038461538461, 'recall': 0.5708029197080292, 'f1-score': 0.7102633969118983, 'support': 1370.0}	0.9399	0.5708	0.7103	1370.0	0.5687	0.4993	0.4797	8224.0	0.5687	0.4993	0.4797	8224.0
1.6051	0.15	3093	1.1348	0.5418	0.5419	0.4626	[[ 849 48 194 135 31 115]
[ 408 534 225 27 63 114]
[ 71 28 1101 103 49 18]
[ 165 21 516 509 127 32]
[ 116 15 563 379 262 36]
[ 87 9 44 13 16 1201]]	{'precision': 0.5005896226415094, 'recall': 0.6188046647230321, 'f1-score': 0.5534550195567145, 'support': 1372.0}	0.5006	0.6188	0.5535	1372.0	{'precision': 0.815267175572519, 'recall': 0.38949671772428884, 'f1-score': 0.5271470878578479, 'support': 1371.0}	0.8153	0.3895	0.5271	1371.0	{'precision': 0.41657207718501704, 'recall': 0.8036496350364963, 'f1-score': 0.5487166708198357, 'support': 1370.0}	0.4166	0.8036	0.5487	1370.0	{'precision': 0.4365351629502573, 'recall': 0.3715328467153285, 'f1-score': 0.40141955835962145, 'support': 1370.0}	0.4365	0.3715	0.4014	1370.0	{'precision': 0.4781021897810219, 'recall': 0.1911013858497447, 'f1-score': 0.273058884835852, 'support': 1371.0}	0.4781	0.1911	0.2731	1371.0	{'precision': 0.7922163588390502, 'recall': 0.8766423357664234, 'f1-score': 0.8322938322938324, 'support': 1370.0}	0.7922	0.8766	0.8323	1370.0	0.5732	0.5419	0.5227	8224.0	0.5732	0.5418	0.5227	8224.0
1.2631	1.0	4124	0.9987	0.6069	0.6069	0.5309	[[ 692 217 105 187 53 118]
[ 127 995 63 42 38 106]
[ 40 52 996 142 127 13]
[ 80 84 360 541 273 32]
[ 41 71 368 321 546 24]
[ 58 38 30 8 15 1221]]	{'precision': 0.6666666666666666, 'recall': 0.5043731778425656, 'f1-score': 0.5742738589211619, 'support': 1372.0}	0.6667	0.5044	0.5743	1372.0	{'precision': 0.6829100892244337, 'recall': 0.7257476294675419, 'f1-score': 0.7036775106082037, 'support': 1371.0}	0.6829	0.7257	0.7037	1371.0	{'precision': 0.518210197710718, 'recall': 0.727007299270073, 'f1-score': 0.6051032806804374, 'support': 1370.0}	0.5182	0.7270	0.6051	1370.0	{'precision': 0.43593875906526997, 'recall': 0.3948905109489051, 'f1-score': 0.4144006127920337, 'support': 1370.0}	0.4359	0.3949	0.4144	1370.0	{'precision': 0.5190114068441065, 'recall': 0.3982494529540481, 'f1-score': 0.4506809739991746, 'support': 1371.0}	0.5190	0.3982	0.4507	1371.0	{'precision': 0.8064729194187582, 'recall': 0.8912408759124087, 'f1-score': 0.8467406380027739, 'support': 1370.0}	0.8065	0.8912	0.8467	1370.0	0.6049	0.6069	0.5991	8224.0	0.6049	0.6069	0.5991	8224.0
1.2292	1.05	5155	1.1215	0.5412	0.5412	0.4641	[[1041 41 100 167 7 16]
[ 456 628 83 139 34 31]
[ 112 13 898 322 20 5]
[ 276 19 261 768 33 13]
[ 213 27 340 691 87 13]
[ 249 16 56 17 3 1029]]	{'precision': 0.4435449510012782, 'recall': 0.7587463556851312, 'f1-score': 0.5598279107286904, 'support': 1372.0}	0.4435	0.7587	0.5598	1372.0	{'precision': 0.8440860215053764, 'recall': 0.45805981035740334, 'f1-score': 0.5938534278959811, 'support': 1371.0}	0.8441	0.4581	0.5939	1371.0	{'precision': 0.5166858457997698, 'recall': 0.6554744525547446, 'f1-score': 0.5778635778635779, 'support': 1370.0}	0.5167	0.6555	0.5779	1370.0	{'precision': 0.3650190114068441, 'recall': 0.5605839416058395, 'f1-score': 0.4421416234887737, 'support': 1370.0}	0.3650	0.5606	0.4421	1370.0	{'precision': 0.47282608695652173, 'recall': 0.06345733041575492, 'f1-score': 0.11189710610932474, 'support': 1371.0}	0.4728	0.0635	0.1119	1371.0	{'precision': 0.9295392953929539, 'recall': 0.7510948905109489, 'f1-score': 0.8308437626160677, 'support': 1370.0}	0.9295	0.7511	0.8308	1370.0	0.5953	0.5412	0.5194	8224.0	0.5953	0.5412	0.5194	8224.0
0.733	1.1	6186	1.0294	0.5803	0.5803	0.5073	[[ 861 72 61 229 20 129]
[ 225 782 71 135 33 125]
[ 93 21 806 389 43 18]
[ 141 26 224 873 71 35]
[ 90 24 275 780 174 28]
[ 47 17 11 15 4 1276]]	{'precision': 0.5909402882635553, 'recall': 0.6275510204081632, 'f1-score': 0.608695652173913, 'support': 1372.0}	0.5909	0.6276	0.6087	1372.0	{'precision': 0.8301486199575372, 'recall': 0.5703865791393143, 'f1-score': 0.6761781236489407, 'support': 1371.0}	0.8301	0.5704	0.6762	1371.0	{'precision': 0.5566298342541437, 'recall': 0.5883211678832116, 'f1-score': 0.5720369056068133, 'support': 1370.0}	0.5566	0.5883	0.5720	1370.0	{'precision': 0.36059479553903345, 'recall': 0.6372262773722628, 'f1-score': 0.4605644948562385, 'support': 1370.0}	0.3606	0.6372	0.4606	1370.0	{'precision': 0.5043478260869565, 'recall': 0.12691466083150985, 'f1-score': 0.2027972027972028, 'support': 1371.0}	0.5043	0.1269	0.2028	1371.0	{'precision': 0.7920546244568591, 'recall': 0.9313868613138686, 'f1-score': 0.8560885608856088, 'support': 1370.0}	0.7921	0.9314	0.8561	1370.0	0.6058	0.5803	0.5627	8224.0	0.6058	0.5803	0.5627	8224.0
1.0566	1.15	7217	1.0046	0.6037	0.6037	0.5314	[[ 941 83 42 200 15 91]
[ 273 859 43 67 12 117]
[ 106 41 763 348 92 20]
[ 156 61 180 826 93 54]
[ 93 68 192 657 305 56]
[ 64 20 6 5 4 1271]]	{'precision': 0.5762400489895897, 'recall': 0.6858600583090378, 'f1-score': 0.6262895174708818, 'support': 1372.0}	0.5762	0.6859	0.6263	1372.0	{'precision': 0.758833922261484, 'recall': 0.6265499635302699, 'f1-score': 0.6863763483819417, 'support': 1371.0}	0.7588	0.6265	0.6864	1371.0	{'precision': 0.6223491027732463, 'recall': 0.5569343065693431, 'f1-score': 0.5878274268104776, 'support': 1370.0}	0.6223	0.5569	0.5878	1370.0	{'precision': 0.3927722301474085, 'recall': 0.602919708029197, 'f1-score': 0.47566945004319033, 'support': 1370.0}	0.3928	0.6029	0.4757	1370.0	{'precision': 0.5854126679462572, 'recall': 0.2224653537563822, 'f1-score': 0.3224101479915433, 'support': 1371.0}	0.5854	0.2225	0.3224	1371.0	{'precision': 0.7899316345556247, 'recall': 0.9277372262773723, 'f1-score': 0.8533064786841222, 'support': 1370.0}	0.7899	0.9277	0.8533	1370.0	0.6209	0.6037	0.5920	8224.0	0.6209	0.6037	0.5920	8224.0
1.2033	2.0	8248	1.1187	0.5755	0.5755	0.4993	[[1013 54 78 81 24 122]
[ 365 704 80 46 59 117]
[ 160 27 982 126 56 19]
[ 299 39 335 516 115 66]
[ 257 43 368 366 270 67]
[ 67 15 31 4 5 1248]]	{'precision': 0.46876446089773255, 'recall': 0.7383381924198251, 'f1-score': 0.5734503255024059, 'support': 1372.0}	0.4688	0.7383	0.5735	1372.0	{'precision': 0.7981859410430839, 'recall': 0.513493800145879, 'f1-score': 0.6249445184198846, 'support': 1371.0}	0.7982	0.5135	0.6249	1371.0	{'precision': 0.5240128068303095, 'recall': 0.7167883211678832, 'f1-score': 0.6054254007398273, 'support': 1370.0}	0.5240	0.7168	0.6054	1370.0	{'precision': 0.45302897278314314, 'recall': 0.37664233576642336, 'f1-score': 0.4113192506974891, 'support': 1370.0}	0.4530	0.3766	0.4113	1370.0	{'precision': 0.5103969754253308, 'recall': 0.19693654266958424, 'f1-score': 0.28421052631578947, 'support': 1371.0}	0.5104	0.1969	0.2842	1371.0	{'precision': 0.7614399023794997, 'recall': 0.910948905109489, 'f1-score': 0.8295114656031903, 'support': 1370.0}	0.7614	0.9109	0.8295	1370.0	0.5860	0.5755	0.5548	8224.0	0.5860	0.5755	0.5548	8224.0
0.9223	2.05	9279	1.0713	0.5793	0.5793	0.5049	[[1039 51 64 88 20 110]
[ 357 747 78 42 18 129]
[ 173 25 919 194 47 12]
[ 343 32 273 582 104 36]
[ 307 29 301 473 203 58]
[ 67 10 14 4 1 1274]]	{'precision': 0.4545056867891514, 'recall': 0.7572886297376094, 'f1-score': 0.5680699835975944, 'support': 1372.0}	0.4545	0.7573	0.5681	1372.0	{'precision': 0.8355704697986577, 'recall': 0.5448577680525164, 'f1-score': 0.6596026490066225, 'support': 1371.0}	0.8356	0.5449	0.6596	1371.0	{'precision': 0.5573074590661007, 'recall': 0.6708029197080292, 'f1-score': 0.608810864524677, 'support': 1370.0}	0.5573	0.6708	0.6088	1370.0	{'precision': 0.420824295010846, 'recall': 0.4248175182481752, 'f1-score': 0.42281147838721395, 'support': 1370.0}	0.4208	0.4248	0.4228	1370.0	{'precision': 0.5165394402035624, 'recall': 0.14806710430342815, 'f1-score': 0.23015873015873015, 'support': 1371.0}	0.5165	0.1481	0.2302	1371.0	{'precision': 0.7869054972205065, 'recall': 0.92992700729927, 'f1-score': 0.8524590163934427, 'support': 1370.0}	0.7869	0.9299	0.8525	1370.0	0.5953	0.5793	0.5570	8224.0	0.5953	0.5793	0.5570	8224.0
0.6639	2.1	10310	0.9879	0.6091	0.6091	0.5358	[[ 988 65 71 104 26 118]
[ 262 816 85 62 40 106]
[ 127 18 870 231 105 19]
[ 236 27 243 692 135 37]
[ 169 24 252 534 355 37]
[ 54 13 10 4 1 1288]]	{'precision': 0.5381263616557734, 'recall': 0.7201166180758017, 'f1-score': 0.6159600997506235, 'support': 1372.0}	0.5381	0.7201	0.6160	1372.0	{'precision': 0.8473520249221184, 'recall': 0.5951859956236324, 'f1-score': 0.6992287917737788, 'support': 1371.0}	0.8474	0.5952	0.6992	1371.0	{'precision': 0.5682560418027433, 'recall': 0.635036496350365, 'f1-score': 0.5997931747673216, 'support': 1370.0}	0.5683	0.6350	0.5998	1370.0	{'precision': 0.4253226797787339, 'recall': 0.5051094890510949, 'f1-score': 0.46179512846179516, 'support': 1370.0}	0.4253	0.5051	0.4618	1370.0	{'precision': 0.5362537764350453, 'recall': 0.2589350838803793, 'f1-score': 0.3492375799311362, 'support': 1371.0}	0.5363	0.2589	0.3492	1371.0	{'precision': 0.8024922118380062, 'recall': 0.9401459854014599, 'f1-score': 0.8658823529411765, 'support': 1370.0}	0.8025	0.9401	0.8659	1370.0	0.6196	0.6091	0.5986	8224.0	0.6196	0.6091	0.5986	8224.0
1.1311	2.15	11341	0.9851	0.6051	0.6051	0.5337	[[ 995 77 93 145 20 42]
[ 241 847 120 67 36 60]
[ 95 15 999 192 59 10]
[ 176 27 345 717 89 16]
[ 120 23 358 612 242 16]
[ 115 30 36 11 2 1176]]	{'precision': 0.571182548794489, 'recall': 0.7252186588921283, 'f1-score': 0.6390494540783558, 'support': 1372.0}	0.5712	0.7252	0.6390	1372.0	{'precision': 0.831207065750736, 'recall': 0.6177972283005105, 'f1-score': 0.708786610878661, 'support': 1371.0}	0.8312	0.6178	0.7088	1371.0	{'precision': 0.5120451050743209, 'recall': 0.7291970802919708, 'f1-score': 0.6016260162601627, 'support': 1370.0}	0.5120	0.7292	0.6016	1370.0	{'precision': 0.4111238532110092, 'recall': 0.5233576642335767, 'f1-score': 0.46050096339113683, 'support': 1370.0}	0.4111	0.5234	0.4605	1370.0	{'precision': 0.5401785714285714, 'recall': 0.1765134938001459, 'f1-score': 0.26608026388125344, 'support': 1371.0}	0.5402	0.1765	0.2661	1371.0	{'precision': 0.8909090909090909, 'recall': 0.8583941605839416, 'f1-score': 0.8743494423791821, 'support': 1370.0}	0.8909	0.8584	0.8743	1370.0	0.6261	0.6051	0.5917	8224.0	0.6261	0.6051	0.5917	8224.0
0.4786	3.0	12372	0.9868	0.6189	0.6189	0.5473	[[ 960 111 60 139 25 77]
[ 239 916 71 49 12 84]
[ 141 34 962 151 69 13]
[ 211 51 315 629 138 26]
[ 145 57 340 446 357 26]
[ 59 23 12 7 3 1266]]	{'precision': 0.5470085470085471, 'recall': 0.6997084548104956, 'f1-score': 0.6140070354972819, 'support': 1372.0}	0.5470	0.6997	0.6140	1372.0	{'precision': 0.7684563758389261, 'recall': 0.6681254558716265, 'f1-score': 0.7147873585641824, 'support': 1371.0}	0.7685	0.6681	0.7148	1371.0	{'precision': 0.5465909090909091, 'recall': 0.7021897810218978, 'f1-score': 0.6146964856230032, 'support': 1370.0}	0.5466	0.7022	0.6147	1370.0	{'precision': 0.4426460239268121, 'recall': 0.4591240875912409, 'f1-score': 0.4507345037620925, 'support': 1370.0}	0.4426	0.4591	0.4507	1370.0	{'precision': 0.5910596026490066, 'recall': 0.2603938730853392, 'f1-score': 0.3615189873417722, 'support': 1371.0}	0.5911	0.2604	0.3615	1371.0	{'precision': 0.8485254691689008, 'recall': 0.9240875912408759, 'f1-score': 0.8846960167714885, 'support': 1370.0}	0.8485	0.9241	0.8847	1370.0	0.6240	0.6189	0.6067	8224.0	0.6240	0.6189	0.6067	8224.0
0.6052	3.05	13403	0.9818	0.6126	0.6126	0.5421	[[ 935 141 90 111 18 77]
[ 196 953 94 44 17 67]
[ 104 30 1044 123 56 13]
[ 236 37 367 612 89 29]
[ 155 43 417 474 259 23]
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Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3

videomae-small-finetuned-kinetics-finetuned-SoccerNetChunks-NoInference

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

NSDT 3DConvert

UnrealSynth

DreamTexture.js