videomae-small-finetuned-kinetics-finetuned-SNchunks-5c-a40

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: 0.7599
Accuracy: 0.7159
Balanced Accuracy: 0.7157
Matthews Correlation: 0.6515
Confusion Matrix: [[1135 54 69 73 41] [ 333 828 92 50 68] [ 161 23 1008 165 13] [ 306 34 292 705 27] [ 102 17 16 9 1226]]
0 Ball out of play: {'precision': 0.5571919489445263, 'recall': 0.827259475218659, 'f1-score': 0.665884423584629, 'support': 1372.0}
Precision 0: 0.5572
Recall 0: 0.8273
F1-score 0: 0.6659
Support 0: 1372.0
1 Foul: {'precision': 0.8661087866108786, 'recall': 0.6039387308533917, 'f1-score': 0.7116458960034379, 'support': 1371.0}
Precision 1: 0.8661
Recall 1: 0.6039
F1-score 1: 0.7116
Support 1: 1371.0
2 Goal: {'precision': 0.6824644549763034, 'recall': 0.7357664233576642, 'f1-score': 0.7081138040042149, 'support': 1370.0}
Precision 2: 0.6825
Recall 2: 0.7358
F1-score 2: 0.7081
Support 2: 1370.0
3 Shots: {'precision': 0.7035928143712575, 'recall': 0.5168621700879765, 'f1-score': 0.5959425190194421, 'support': 1364.0}
Precision 3: 0.7036
Recall 3: 0.5169
F1-score 3: 0.5959
Support 3: 1364.0
4 Throw-in: {'precision': 0.8916363636363637, 'recall': 0.8948905109489051, 'f1-score': 0.8932604735883425, 'support': 1370.0}
Precision 4: 0.8916
Recall 4: 0.8949
F1-score 4: 0.8933
Support 4: 1370.0
Precision Macro avg: 0.7402
Recall Macro avg: 0.7157
F1-score Macro avg: 0.7150
Support Macro avg: 6847.0
Precision Weighted avg: 0.7402
Recall Weighted avg: 0.7159
F1-score Weighted avg: 0.7151
Support Weighted avg: 6847.0

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: 16
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

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	Precision 3	Recall 3	F1-score 3	Support 3	4 Throw-in	Precision 4	Recall 4	F1-score 4	Support 4	Precision Macro avg
0.7825	1.0	428	0.9234	0.6207	0.6205	0.5371	[[ 961 43 100 80 188]
[ 461 597 105 35 173]
[ 195 22 953 167 33]
[ 454 33 354 464 59]
[ 65 7 19 4 1275]]	{'precision': 0.4499063670411985, 'recall': 0.7004373177842566, 'f1-score': 0.5478905359179019, 'support': 1372.0}	0.4499	0.7004	0.5479	1372.0	{'precision': 0.8504273504273504, 'recall': 0.43544857768052514, 'f1-score': 0.5759768451519537, 'support': 1371.0}	0.8504	0.4354	0.5760	1371.0	{'precision': 0.6224689745264533, 'recall': 0.6956204379562044, 'f1-score': 0.6570148224750086, 'support': 1370.0}	0.6225	0.6956	0.6570	1370.0	{'precision': 0.6186666666666667, 'recall': 0.34017595307917886, 'f1-score': 0.4389782403027436, 'support': 1364.0}	0.6187	0.3402	0.4390	1364.0	{'precision': 0.7378472222222222, 'recall': 0.9306569343065694, 'f1-score': 0.8231116849580373, 'support': 1370.0}	0.7378	0.9307	0.8231	1370.0	0.6559	0.6205	0.6086	6847.0	0.6559	0.6207	0.6087	6847.0
0.8655	2.0	856	0.8769	0.6648	0.6646	0.5874	[[1007 78 82 82 123]
[ 328 794 78 54 117]
[ 162 36 972 182 18]
[ 398 50 313 536 67]
[ 74 15 30 8 1243]]	{'precision': 0.5114271203656678, 'recall': 0.7339650145772595, 'f1-score': 0.6028135288835678, 'support': 1372.0}	0.5114	0.7340	0.6028	1372.0	{'precision': 0.816032887975334, 'recall': 0.5791393143690736, 'f1-score': 0.6774744027303755, 'support': 1371.0}	0.8160	0.5791	0.6775	1371.0	{'precision': 0.6589830508474577, 'recall': 0.7094890510948905, 'f1-score': 0.6833040421792619, 'support': 1370.0}	0.6590	0.7095	0.6833	1370.0	{'precision': 0.6218097447795824, 'recall': 0.39296187683284456, 'f1-score': 0.48158131176999097, 'support': 1364.0}	0.6218	0.3930	0.4816	1364.0	{'precision': 0.7927295918367347, 'recall': 0.9072992700729927, 'f1-score': 0.8461538461538461, 'support': 1370.0}	0.7927	0.9073	0.8462	1370.0	0.6802	0.6646	0.6583	6847.0	0.6802	0.6648	0.6584	6847.0
0.8065	3.0	1284	0.7639	0.7037	0.7035	0.6356	[[1046 89 119 82 36]
[ 271 906 104 47 43]
[ 106 21 1116 126 1]
[ 266 35 408 646 9]
[ 141 51 60 14 1104]]	{'precision': 0.571584699453552, 'recall': 0.7623906705539358, 'f1-score': 0.6533416614615865, 'support': 1372.0}	0.5716	0.7624	0.6533	1372.0	{'precision': 0.822141560798548, 'recall': 0.6608315098468271, 'f1-score': 0.7327133036797413, 'support': 1371.0}	0.8221	0.6608	0.7327	1371.0	{'precision': 0.6175982291090205, 'recall': 0.8145985401459854, 'f1-score': 0.7025495750708216, 'support': 1370.0}	0.6176	0.8146	0.7025	1370.0	{'precision': 0.7060109289617487, 'recall': 0.4736070381231672, 'f1-score': 0.566915313734094, 'support': 1364.0}	0.7060	0.4736	0.5669	1364.0	{'precision': 0.9253981559094719, 'recall': 0.8058394160583942, 'f1-score': 0.8614904408895825, 'support': 1370.0}	0.9254	0.8058	0.8615	1370.0	0.7285	0.7035	0.7034	6847.0	0.7285	0.7037	0.7035	6847.0
0.6598	4.0	1712	0.7694	0.6994	0.6992	0.6319	[[1106 42 82 80 62]
[ 379 735 117 60 80]
[ 133 17 1053 159 8]
[ 293 28 340 671 32]
[ 98 16 21 11 1224]]	{'precision': 0.5505226480836237, 'recall': 0.8061224489795918, 'f1-score': 0.6542443064182195, 'support': 1372.0}	0.5505	0.8061	0.6542	1372.0	{'precision': 0.8770883054892601, 'recall': 0.5361050328227571, 'f1-score': 0.6654594839293798, 'support': 1371.0}	0.8771	0.5361	0.6655	1371.0	{'precision': 0.652820830750155, 'recall': 0.7686131386861313, 'f1-score': 0.7060006704659737, 'support': 1370.0}	0.6528	0.7686	0.7060	1370.0	{'precision': 0.6839959225280327, 'recall': 0.49193548387096775, 'f1-score': 0.5722814498933901, 'support': 1364.0}	0.6840	0.4919	0.5723	1364.0	{'precision': 0.8705547652916074, 'recall': 0.8934306569343066, 'f1-score': 0.8818443804034583, 'support': 1370.0}	0.8706	0.8934	0.8818	1370.0	0.7270	0.6992	0.6960	6847.0	0.7270	0.6994	0.6961	6847.0
0.5968	5.0	2140	0.7820	0.6991	0.6989	0.6335	[[1140 50 77 59 46]
[ 360 834 85 32 60]
[ 186 26 1007 140 11]
[ 384 56 293 593 38]
[ 129 19 6 3 1213]]	{'precision': 0.5184174624829468, 'recall': 0.8309037900874635, 'f1-score': 0.6384766171940633, 'support': 1372.0}	0.5184	0.8309	0.6385	1372.0	{'precision': 0.8467005076142132, 'recall': 0.6083150984682714, 'f1-score': 0.7079796264855689, 'support': 1371.0}	0.8467	0.6083	0.7080	1371.0	{'precision': 0.6859673024523161, 'recall': 0.7350364963503649, 'f1-score': 0.7096546863988723, 'support': 1370.0}	0.6860	0.7350	0.7097	1370.0	{'precision': 0.717049576783555, 'recall': 0.4347507331378299, 'f1-score': 0.5413053400273847, 'support': 1364.0}	0.7170	0.4348	0.5413	1364.0	{'precision': 0.8866959064327485, 'recall': 0.8854014598540146, 'f1-score': 0.8860482103725348, 'support': 1370.0}	0.8867	0.8854	0.8860	1370.0	0.7310	0.6989	0.6967	6847.0	0.7309	0.6991	0.6968	6847.0
0.5675	6.0	2568	0.7603	0.7159	0.7157	0.6515	[[1135 54 69 73 41]
[ 333 828 92 50 68]
[ 161 23 1008 165 13]
[ 306 34 292 705 27]
[ 102 17 16 9 1226]]	{'precision': 0.5571919489445263, 'recall': 0.827259475218659, 'f1-score': 0.665884423584629, 'support': 1372.0}	0.5572	0.8273	0.6659	1372.0	{'precision': 0.8661087866108786, 'recall': 0.6039387308533917, 'f1-score': 0.7116458960034379, 'support': 1371.0}	0.8661	0.6039	0.7116	1371.0	{'precision': 0.6824644549763034, 'recall': 0.7357664233576642, 'f1-score': 0.7081138040042149, 'support': 1370.0}	0.6825	0.7358	0.7081	1370.0	{'precision': 0.7035928143712575, 'recall': 0.5168621700879765, 'f1-score': 0.5959425190194421, 'support': 1364.0}	0.7036	0.5169	0.5959	1364.0	{'precision': 0.8916363636363637, 'recall': 0.8948905109489051, 'f1-score': 0.8932604735883425, 'support': 1370.0}	0.8916	0.8949	0.8933	1370.0	0.7402	0.7157	0.7150	6847.0	0.7402	0.7159	0.7151	6847.0
0.4824	7.0	2996	0.8064	0.6958	0.6956	0.6308	[[1178 37 62 69 26]
[ 396 787 80 57 51]
[ 188 14 993 172 3]
[ 378 32 287 650 17]
[ 173 16 17 8 1156]]	{'precision': 0.5092952875054042, 'recall': 0.858600583090379, 'f1-score': 0.639348710990502, 'support': 1372.0}	0.5093	0.8586	0.6393	1372.0	{'precision': 0.8882618510158014, 'recall': 0.574033552151714, 'f1-score': 0.6973859105006646, 'support': 1371.0}	0.8883	0.5740	0.6974	1371.0	{'precision': 0.6900625434329395, 'recall': 0.7248175182481752, 'f1-score': 0.7070131719473122, 'support': 1370.0}	0.6901	0.7248	0.7070	1370.0	{'precision': 0.6799163179916318, 'recall': 0.47653958944281527, 'f1-score': 0.560344827586207, 'support': 1364.0}	0.6799	0.4765	0.5603	1364.0	{'precision': 0.922585794094174, 'recall': 0.8437956204379562, 'f1-score': 0.881433473122379, 'support': 1370.0}	0.9226	0.8438	0.8814	1370.0	0.7380	0.6956	0.6971	6847.0	0.7380	0.6958	0.6972	6847.0
0.6574	8.0	3424	0.7998	0.7035	0.7033	0.6385	[[1141 55 85 65 26]
[ 341 827 113 50 40]
[ 150 19 1084 113 4]
[ 321 47 353 624 19]
[ 166 32 25 6 1141]]	{'precision': 0.5384615384615384, 'recall': 0.8316326530612245, 'f1-score': 0.6536808937267259, 'support': 1372.0}	0.5385	0.8316	0.6537	1372.0	{'precision': 0.8438775510204082, 'recall': 0.6032093362509118, 'f1-score': 0.7035304125903871, 'support': 1371.0}	0.8439	0.6032	0.7035	1371.0	{'precision': 0.653012048192771, 'recall': 0.7912408759124088, 'f1-score': 0.7155115511551154, 'support': 1370.0}	0.6530	0.7912	0.7155	1370.0	{'precision': 0.7272727272727273, 'recall': 0.4574780058651026, 'f1-score': 0.5616561656165616, 'support': 1364.0}	0.7273	0.4575	0.5617	1364.0	{'precision': 0.9276422764227642, 'recall': 0.8328467153284671, 'f1-score': 0.8776923076923077, 'support': 1370.0}	0.9276	0.8328	0.8777	1370.0	0.7381	0.7033	0.7024	6847.0	0.7380	0.7035	0.7025	6847.0
0.4709	9.0	3852	0.8032	0.7024	0.7021	0.6373	[[1161 47 70 68 26]
[ 365 794 98 62 52]
[ 177 16 1019 155 3]
[ 353 39 297 654 21]
[ 149 19 16 5 1181]]	{'precision': 0.5265306122448979, 'recall': 0.8462099125364432, 'f1-score': 0.6491473301649426, 'support': 1372.0}	0.5265	0.8462	0.6491	1372.0	{'precision': 0.8677595628415301, 'recall': 0.5791393143690736, 'f1-score': 0.6946631671041119, 'support': 1371.0}	0.8678	0.5791	0.6947	1371.0	{'precision': 0.6793333333333333, 'recall': 0.7437956204379562, 'f1-score': 0.7101045296167248, 'support': 1370.0}	0.6793	0.7438	0.7101	1370.0	{'precision': 0.6927966101694916, 'recall': 0.47947214076246336, 'f1-score': 0.5667244367417678, 'support': 1364.0}	0.6928	0.4795	0.5667	1364.0	{'precision': 0.9204988308651598, 'recall': 0.862043795620438, 'f1-score': 0.8903128533735394, 'support': 1370.0}	0.9205	0.8620	0.8903	1370.0	0.7374	0.7021	0.7022	6847.0	0.7374	0.7024	0.7023	6847.0
0.3689	10.0	4280	0.8093	0.7082	0.7079	0.6447	[[1160 58 65 58 31]
[ 343 852 86 40 50]
[ 191 23 1015 136 5]
[ 383 52 284 624 21]
[ 130 24 13 5 1198]]	{'precision': 0.5256003624830086, 'recall': 0.8454810495626822, 'f1-score': 0.648225761385862, 'support': 1372.0}	0.5256	0.8455	0.6482	1372.0	{'precision': 0.844400396432111, 'recall': 0.6214442013129103, 'f1-score': 0.7159663865546219, 'support': 1371.0}	0.8444	0.6214	0.7160	1371.0	{'precision': 0.69377990430622, 'recall': 0.7408759124087592, 'f1-score': 0.7165548888104484, 'support': 1370.0}	0.6938	0.7409	0.7166	1370.0	{'precision': 0.7230590961761297, 'recall': 0.4574780058651026, 'f1-score': 0.5603951504265828, 'support': 1364.0}	0.7231	0.4575	0.5604	1364.0	{'precision': 0.918007662835249, 'recall': 0.8744525547445255, 'f1-score': 0.8957009345794392, 'support': 1370.0}	0.9180	0.8745	0.8957	1370.0	0.7410	0.7079	0.7074	6847.0	0.7409	0.7082	0.7075	6847.0

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+git8bfa463
Datasets 2.13.1
Tokenizers 0.13.3

videomae-small-finetuned-kinetics-finetuned-SNchunks-5c-a40

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

NSDT 3DConvert

UnrealSynth

DreamTexture.js