videomae-base-finetuned-good-gesturePhaseV5

alexgrigore/videomae-base-finetuned-good-gesturePhaseV5

videomae-base-finetuned-good-gesturePhaseV5 is a video classification model from alexgrigore. Use it for the video classification task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as cc-by-nc-4.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Video Classification
Library: transformers
License: cc-by-nc-4.0
Model type: videomae
Access: Public
Created: Jun 8, 2024
Updated: Jun 8, 2024
SHA: 318e9f7b

Base models

MCG-NJU/videomae-base

Model card

From the Hugging Face model README

videomae-base-finetuned-good-gesturePhaseV5

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

Accuracy: 0.6871
Loss: 0.9822
Accuracy Hold: 0.0
Accuracy Stroke: 0.0
Accuracy Recovery: 0.0
Accuracy Preparation: 0.9221
Accuracy Unknown: 0.8571

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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
training_steps: 1380

Training results

Training Loss	Epoch	Step	Accuracy	Validation Loss	Accuracy Hold	Accuracy Recovery	Accuracy Preparation	Accuracy Unknown
1.1475	0.0507	70	0.5597	1.2558	0.0	0.0	1.0	0.0
1.2103	1.0507	140	0.5597	1.2704	0.0	0.0	1.0	0.0
0.9964	2.0507	210	0.5597	1.2142	0.0	0.0	1.0	0.0
0.9975	3.0507	280	0.5970	1.0747	0.0	0.0	0.9733	0.2692
1.0538	4.0507	350	0.6642	0.9622	0.0	0.0	0.88	0.8846
1.0321	5.0507	420	0.6567	0.9451	0.0	0.0	0.8533	0.9231
0.7822	6.0507	490	0.7164	0.8797	0.0	0.0833	0.96	0.8846
0.8743	7.0507	560	0.6791	0.9399	0.0	0.0833	0.8533	1.0
0.7515	8.0507	630	0.6791	0.9290	0.0	0.0	0.8667	1.0
0.8525	9.0507	700	0.7090	0.8447	0.0	0.1667	0.9467	0.8462
0.7661	10.0507	770	0.7090	0.7857	0.0	0.1667	0.9067	0.9615
0.8363	11.0507	840	0.6866	0.8165	0.0	0.0833	0.92	0.8462
0.659	12.0507	910	0.7164	0.7951	0.0	0.1667	0.9067	1.0
0.6274	13.0507	980	0.7015	0.7754	0.0	0.0833	0.8933	1.0
0.7292	14.0507	1050	0.6791	0.8128	0.0	0.25	0.8267	1.0
0.7447	15.0507	1120	0.6866	0.7860	0.0	0.25	0.84	1.0
0.5512	16.0507	1190	0.7015	0.7839	0.0625	0.1667	0.8667	1.0
0.3404	17.0507	1260	0.7015	0.8055	0.0	0.3333	0.8533	1.0
0.4406	18.0507	1330	0.6866	0.7800	0.0	0.1667	0.8533	1.0
0.6358	19.0362	1380	0.7015	0.7816	0.0	0.1667	0.88	1.0

Framework versions

Transformers 4.41.1
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Task: Video Classification
Library: transformers
Type: videomae
License: cc-by-nc-4.0
Created: Jun 8, 2024
Updated: Jun 8, 2024