videomae-base-finetuned-ASBD_ESBD

OckerGui/videomae-base-finetuned-ASBD_ESBD

videomae-base-finetuned-ASBD_ESBD is a video classification model from OckerGui. 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 21, 2023
Updated: Sep 5, 2023
SHA: e666de90

Model card

From the Hugging Face model README

videomae-base-finetuned-ASBD_ESBD

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:

Loss: 1.4178
Accuracy: 0.5714

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: 12
eval_batch_size: 12
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: 500

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.504	0.02	11	1.3929	0.3433
1.3539	1.02	22	1.3380	0.1940
1.2957	2.02	33	1.3519	0.2239
1.2368	3.02	44	1.3220	0.3881
1.1561	4.02	55	1.2803	0.3134
1.0195	5.02	66	1.2588	0.5373
0.8594	6.02	77	1.1591	0.5075
0.8756	7.02	88	0.9532	0.6119
0.6488	8.02	99	1.1922	0.5373
0.4427	9.02	110	0.9780	0.6567
0.3975	10.02	121	1.3228	0.5373
0.3978	11.02	132	1.2083	0.6418
0.2859	12.02	143	1.0027	0.7463
0.3441	13.02	154	1.3718	0.5821
0.2239	14.02	165	1.4324	0.5821
0.2275	15.02	176	1.1823	0.6418
0.1734	16.02	187	1.5484	0.6119
0.2451	17.02	198	1.3764	0.5821
0.1317	18.02	209	1.3731	0.6716
0.0778	19.02	220	1.3567	0.7164
0.1963	20.02	231	1.0905	0.7164
0.1474	21.02	242	2.1361	0.4627
0.0487	22.02	253	1.2189	0.7164
0.0699	23.02	264	1.7618	0.5970
0.1576	24.02	275	1.1939	0.7463
0.0377	25.02	286	1.2287	0.7313
0.0674	26.02	297	1.5247	0.6567
0.0188	27.02	308	1.7585	0.6567
0.0681	28.02	319	1.7868	0.6567
0.0341	29.02	330	1.3745	0.6567
0.05	30.02	341	1.8781	0.6418
0.0269	31.02	352	1.9228	0.5970
0.0213	32.02	363	1.8014	0.6119
0.0061	33.02	374	1.4477	0.6866
0.0338	34.02	385	1.5303	0.6567
0.0086	35.02	396	1.5219	0.7015
0.0891	36.02	407	1.8414	0.5821
0.0032	37.02	418	1.8731	0.5821
0.0028	38.02	429	1.6881	0.6418
0.0434	39.02	440	1.7288	0.6567
0.0018	40.02	451	1.8235	0.6119
0.0232	41.02	462	1.8903	0.6119
0.0016	42.02	473	1.9292	0.6119
0.0016	43.02	484	1.9059	0.6119
0.0029	44.02	495	1.8093	0.6418
0.0519	45.01	500	1.8045	0.6418

Framework versions

Transformers 4.28.0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3

Task: Video Classification
Library: transformers
Type: videomae
License: cc-by-nc-4.0
Created: Jun 21, 2023
Updated: Sep 5, 2023