vivit-surf-analytics-runpod

This model is a fine-tuned version of google/vivit-b-16x2-kinetics400 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.7609
• Accuracy: 0.9163
• F1: 0.9154

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: 1
• eval_batch_size: 1
• 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: 22230

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.0	0.0333	741	0.9082	0.9070	0.9068
0.5976	1.0333	1482	2.5471	0.7302	0.7286
0.1188	2.0333	2223	1.0145	0.8698	0.8695
0.0001	3.0333	2964	1.1956	0.8465	0.8384
0.5026	4.0333	3705	0.8190	0.8651	0.8608
0.0	5.0333	4446	1.1466	0.8372	0.8377
0.0	6.0333	5187	1.0804	0.8419	0.8358
0.0	7.0333	5928	0.8535	0.8930	0.8909
0.0	8.0333	6669	0.6512	0.9070	0.9070
0.0001	9.0333	7410	0.8475	0.8884	0.8887
0.0001	10.0333	8151	0.7335	0.8977	0.8972
0.0	11.0333	8892	0.7774	0.9070	0.9054
0.0	12.0333	9633	0.7346	0.9116	0.9107
0.0	13.0333	10374	0.7609	0.9163	0.9154
0.0	14.0333	11115	0.7560	0.9070	0.9074
0.0	15.0333	11856	0.8036	0.9163	0.9151
0.0	16.0333	12597	0.7962	0.9163	0.9151
0.0	17.0333	13338	0.7821	0.9163	0.9147
0.0	18.0333	14079	0.7898	0.9163	0.9149
0.0	19.0333	14820	1.0166	0.8791	0.8748
0.0	20.0333	15561	0.8697	0.8977	0.8968
0.0	21.0333	16302	0.7670	0.9023	0.9017
0.0	22.0333	17043	0.7399	0.9116	0.9107
0.0	23.0333	17784	0.7458	0.9116	0.9107
0.0	24.0333	18525	0.7701	0.8977	0.8969
0.0	25.0333	19266	0.7924	0.9023	0.9014
0.0	26.0333	20007	0.7955	0.9023	0.9014
0.0	27.0333	20748	0.8675	0.8977	0.8969
0.0	28.0333	21489	0.8671	0.8977	0.8969
0.0	29.0333	22230	0.8665	0.8977	0.8969

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.1+cu121
• Datasets 2.19.2
• Tokenizers 0.19.1