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Joy28/
videomae-base-finetuned-ucf101-subset-finetuned-subset

Name: videomae-base-finetuned-ucf101-subset-finetuned-subset
Availability: InStock

generated_from_trainer

NiiCole/videomae-base-finetuned-ucf101-subset

finetune

cc-by-nc-4.0

endpoints_compatible

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videomae-base-finetuned-ucf101-subset-finetuned-subset

This model is a fine-tuned version of NiiCole/videomae-base-finetuned-ucf101-subset on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5670
Accuracy: 0.8165

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: 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: 3510

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.58	0.02	59	1.5476	0.3733
1.3617	1.02	118	1.2123	0.4977
1.1668	2.02	177	1.3331	0.3825
1.1751	3.02	236	1.2529	0.3687
0.9961	4.02	295	1.0008	0.6452
1.0562	5.02	354	1.0535	0.5392
1.0189	6.02	413	0.9321	0.6866
0.8553	7.02	472	0.8955	0.5853
0.8961	8.02	531	0.7889	0.7419
1.1626	9.02	590	0.9532	0.6175
0.7951	10.02	649	1.1165	0.5530
0.9042	11.02	708	0.7012	0.7650
0.8642	12.02	767	0.7589	0.6774
0.8017	13.02	826	0.7485	0.6959
0.7523	14.02	885	0.5617	0.7834
0.7223	15.02	944	1.0344	0.6129
0.6164	16.02	1003	0.7275	0.7419
0.6892	17.02	1062	0.6162	0.7788
0.7865	18.02	1121	0.7966	0.6590
0.6387	19.02	1180	0.7436	0.6774
0.6181	20.02	1239	0.7137	0.7373
0.6085	21.02	1298	0.9581	0.6682
0.7109	22.02	1357	0.7746	0.7097
0.7686	23.02	1416	0.7969	0.7097
0.5995	24.02	1475	1.0075	0.6129
0.5854	25.02	1534	0.7389	0.7419
0.575	26.02	1593	0.7198	0.7143
0.7478	27.02	1652	0.6098	0.7742
0.7204	28.02	1711	0.6459	0.7972
0.4325	29.02	1770	0.7268	0.7512
0.593	30.02	1829	0.5901	0.7880
0.6432	31.02	1888	0.5924	0.7880
0.4821	32.02	1947	0.5730	0.8157
0.9189	33.02	2006	0.6242	0.7834
0.6179	34.02	2065	0.5847	0.8018
0.5767	35.02	2124	0.5965	0.8249
0.5298	36.02	2183	0.7918	0.7235
0.5651	37.02	2242	0.8338	0.7327
0.9236	38.02	2301	0.8371	0.7143
0.4854	39.02	2360	0.7115	0.7696
0.4837	40.02	2419	0.6326	0.7696
0.4142	41.02	2478	0.6266	0.8203
0.3309	42.02	2537	0.5806	0.8111
0.3939	43.02	2596	0.5746	0.8249
0.4993	44.02	2655	0.6114	0.8387
0.3785	45.02	2714	0.6317	0.8018
0.5224	46.02	2773	0.6667	0.7972
0.5705	47.02	2832	0.6382	0.7926
0.3342	48.02	2891	0.5592	0.8157
0.5044	49.02	2950	0.5748	0.7926
0.3838	50.02	3009	0.6050	0.7788
0.5099	51.02	3068	0.6557	0.7604
0.4335	52.02	3127	0.7119	0.7512
0.4122	53.02	3186	0.6562	0.7788
0.4431	54.02	3245	0.6701	0.7650
0.4536	55.02	3304	0.6659	0.7696
0.3867	56.02	3363	0.6632	0.7788
0.3878	57.02	3422	0.6911	0.7742
0.3853	58.02	3481	0.6931	0.7788
0.3268	59.01	3510	0.6914	0.7788

Framework versions

Transformers 4.35.2
Pytorch 1.13.1
Datasets 2.15.0
Tokenizers 0.15.0

Joy28/videomae-base-finetuned-ucf101-subset-finetuned-subset

videomae-base-finetuned-ucf101-subset-finetuned-subset

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Joy28/
videomae-base-finetuned-ucf101-subset-finetuned-subset