README.md

metadata

license: cc-by-4.0
datasets:
  - LEI-QI-233/MicroG-4M
  - LEI-QI-233/MicroG-HAR-train-ready
metrics:
  - mAP
  - F1-score
  - Recall
  - AUROC
pipeline_tag: video-classification

Here stores all fine-tuned weights of our dataset.

Please view our paper, GitHub and dataset firstly:

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Performance comparison of models fine-tuned on MicroG-4M for HAR

Arch	TC	Backbone	#Params (M)	mAP (%)	F1-score (%)	Recall (%)	AUROC (%)
C2D	8×8	R50	23.61	29.51	8.09	6.58	83.49
C2D NLN	8×8	R50	30.97	44.64	28.30	24.86	89.40
I3D	8×8	R50	27.33	46.41	26.37	22.25	88.79
I3D NLN	8×8	R50	34.68	47.12	28.07	24.65	88.52
Slow	8×8	R50	31.74	45.19	26.13	22.77	88.49
Slow	4×16	R50	31.74	46.37	28.72	25.38	88.30
SlowFast	8×8	R50	33.76	43.02	22.63	18.98	88.51
SlowFast	4×16	R50	33.76	42.10	23.69	20.18	87.54
MViTv1	16×4	B-CONV	36.34	12.86	5.54	4.66	74.63
MViTv2	16×4	S	34.27	15.14	8.16	7.17	78.61
X3D	13×6	S	2.02	14.07	5.77	4.52	78.23
X3D	16×5	L	4.37	18.70	9.15	7.47	78.27

Note:

• All models has been pretrained on Kinetics400 dataset and continually trained on MicroG-4M.
• TC denotes the temporal configuration (frame length × sampling rate).
• #Params indicates the number of parameters (in millions, M).