metadata

license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-large
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: videomae-large_5class_UCFCrime
    results: []

videomae-large_5class_UCFCrime

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

Loss: 1.6826
Accuracy: 0.7523
Precision: 0.7853
Recall: 0.7523
F1 Score: 0.7364

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: 2
eval_batch_size: 2
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: 2850

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1 Score
1.5794	0.03	95	1.4947	0.4128	0.2370	0.4128	0.2975
1.7243	1.03	190	1.3362	0.5321	0.4612	0.5321	0.4143
1.6818	2.03	285	1.3317	0.3119	0.4356	0.3119	0.2364
1.0727	3.03	380	1.1666	0.5046	0.5546	0.5046	0.4337
1.1121	4.03	475	1.3545	0.4587	0.6563	0.4587	0.3678
1.0062	5.03	570	1.4656	0.5963	0.5376	0.5963	0.5325
1.2532	6.03	665	1.6516	0.5780	0.5596	0.5780	0.4982
1.9184	7.03	760	1.5020	0.5872	0.7283	0.5872	0.5449
1.0223	8.03	855	1.4417	0.5872	0.5636	0.5872	0.5027
0.9406	9.03	950	1.9402	0.5780	0.5800	0.5780	0.4906
1.3058	10.03	1045	1.7611	0.4679	0.6914	0.4679	0.4463
0.9196	11.03	1140	1.0373	0.6330	0.5648	0.6330	0.5625
0.4191	12.03	1235	0.9139	0.6789	0.7553	0.6789	0.6824
0.4816	13.03	1330	1.0840	0.7248	0.8001	0.7248	0.7176
1.0577	14.03	1425	0.9822	0.7339	0.7724	0.7339	0.7216
0.719	15.03	1520	1.4597	0.6789	0.6954	0.6789	0.6715
0.3427	16.03	1615	1.4807	0.6789	0.7114	0.6789	0.6818
0.6303	17.03	1710	1.9664	0.6881	0.7482	0.6881	0.6604
0.0025	18.03	1805	1.5750	0.7156	0.7403	0.7156	0.7067
0.2404	19.03	1900	2.2045	0.6606	0.7090	0.6606	0.6292
0.0313	20.03	1995	1.6007	0.7248	0.7776	0.7248	0.7091
0.3372	21.03	2090	1.6536	0.7156	0.7811	0.7156	0.6864
0.5431	22.03	2185	2.1961	0.6514	0.7559	0.6514	0.6161
0.0003	23.03	2280	1.6826	0.7523	0.7853	0.7523	0.7364
0.0013	24.03	2375	1.8359	0.7431	0.7906	0.7431	0.7291
0.0025	25.03	2470	2.0891	0.6881	0.7433	0.6881	0.6611
0.0153	26.03	2565	1.8442	0.7431	0.7855	0.7431	0.7296
0.0147	27.03	2660	1.8483	0.7431	0.7816	0.7431	0.7243
0.0002	28.03	2755	1.9301	0.7339	0.7765	0.7339	0.7150
0.0004	29.03	2850	1.9169	0.7339	0.7765	0.7339	0.7150

Framework versions

Transformers 4.39.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2