videomae-finetuned-nba-5-class-4-batch-8000-vid-multilabel

omermazig/videomae-finetuned-nba-5-class-4-batch-8000-vid-multilabel

videomae-finetuned-nba-5-class-4-batch-8000-vid-multilabel is a video classification model from omermazig. 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: Nov 5, 2023
Updated: Nov 5, 2023
SHA: 576ee15d

Base models

MCG-NJU/videomae-base-finetuned-kinetics

Spaces

omermazig/videomae-finetuned-nba-5-class-multilabel

Model card

From the Hugging Face model README

videomae-finetuned-nba-5-class-4-batch-8000-vid-multilabel-4

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

Loss: 0.4886
F1: 0.8863
Roc Auc: 0.9209
Accuracy: 0.7962

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: 1.5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
training_steps: 50000

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.771	0.04	2000	0.7660	0.6227	0.7324	0.2937
0.6225	1.04	4000	0.6446	0.7401	0.8162	0.4653
0.6093	2.04	6000	0.6517	0.7441	0.8223	0.5253
0.603	3.04	8000	0.5630	0.8151	0.8722	0.6558
0.6547	4.04	10000	0.5009	0.8425	0.8911	0.6695
0.6426	5.04	12000	0.5179	0.8422	0.9012	0.6642
0.4447	6.04	14000	0.5052	0.8537	0.8973	0.7147
0.6949	7.04	16000	0.5045	0.8548	0.8974	0.7337
0.509	8.04	18000	0.5262	0.8705	0.9089	0.7768
0.4341	9.04	20000	0.4731	0.8831	0.9163	0.7863
0.5037	10.04	22000	0.5040	0.8781	0.9154	0.7747
0.3592	11.04	24000	0.5091	0.8746	0.9101	0.7821
0.2829	12.04	26000	0.4709	0.8867	0.9230	0.7905
0.3599	13.04	28000	0.4722	0.8888	0.9230	0.7947
0.4152	14.04	30000	0.4744	0.8911	0.9247	0.8074
0.1167	15.04	32000	0.4817	0.8949	0.9241	0.8179
0.2721	16.04	34000	0.4627	0.9064	0.9337	0.8274
0.42	17.04	36000	0.4849	0.8992	0.9282	0.8295
0.5841	18.04	38000	0.4747	0.9026	0.9304	0.8379
0.509	19.04	40000	0.4779	0.8991	0.9273	0.8316
0.1599	20.04	42000	0.4945	0.9056	0.9319	0.8411
0.3645	21.04	44000	0.4875	0.8984	0.9277	0.8242
0.4402	22.04	46000	0.5156	0.9004	0.9283	0.8368
0.2769	23.04	48000	0.5063	0.9028	0.9296	0.8389
0.3009	24.04	50000	0.5122	0.8975	0.9257	0.8326

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1

Task: Video Classification
Library: transformers
Type: videomae
License: cc-by-nc-4.0
Created: Nov 5, 2023
Updated: Nov 5, 2023