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Shawon16/
timesformer_wlasl_100_200ep_coR_

Name: timesformer_wlasl_100_200ep_coR_
Availability: InStock

generated_from_trainer

facebook/timesformer-base-finetuned-k400

finetune

cc-by-nc-4.0

endpoints_compatible

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timesformer_wlasl_100_200ep_coR_

This model is a fine-tuned version of facebook/timesformer-base-finetuned-k400 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.5784
Accuracy: 0.5592

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
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
training_steps: 36000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
18.9003	0.005	180	4.6868	0.0089
18.3693	1.0050	360	4.5353	0.0414
17.2476	2.0050	540	4.2835	0.0828
15.3051	3.0050	721	3.8992	0.1598
12.9592	4.005	901	3.4829	0.2456
10.4934	5.0050	1081	3.1537	0.3491
8.2588	6.0050	1261	2.8115	0.4172
6.1765	7.0050	1442	2.5671	0.4438
4.321	8.005	1622	2.2792	0.5030
2.874	9.0050	1802	2.1045	0.5296
1.7634	10.0050	1982	1.9738	0.5414
1.0891	11.0050	2163	1.8931	0.5592
0.6992	12.005	2343	1.7606	0.5651
0.429	13.0050	2523	1.6879	0.5799
0.327	14.0050	2703	1.7075	0.5976
0.1959	15.0050	2884	1.6486	0.5917
0.1588	16.005	3064	1.8440	0.5621
0.1563	17.0050	3244	1.7394	0.5888
0.1913	18.0050	3424	1.9017	0.5710
0.1603	19.0050	3605	1.9527	0.5828
0.1007	20.005	3785	1.9093	0.6124
0.0952	21.0050	3965	1.8047	0.6036
0.0833	22.0050	4145	1.9631	0.5680
0.1537	23.0050	4326	1.9207	0.6154
0.2198	24.005	4506	1.8979	0.5976
0.1316	25.0050	4686	1.9179	0.5917
0.1503	26.0050	4866	1.9743	0.5769
0.1429	27.0050	5047	2.0455	0.5740
0.0988	28.005	5227	2.0844	0.5710
0.1819	29.0050	5407	2.0516	0.5947
0.1184	30.0050	5587	2.4084	0.5621
0.1113	31.0050	5768	2.2745	0.5473
0.1382	32.005	5948	2.5505	0.5385
0.1375	33.0050	6128	2.2980	0.5592
0.2414	34.0050	6308	2.3081	0.5325
0.1255	35.0050	6489	2.1351	0.5858
0.1024	36.005	6669	2.1904	0.6006
0.1639	37.0050	6849	2.2539	0.5740
0.1625	38.0050	7029	2.1330	0.5858
0.2216	39.0050	7210	2.4128	0.5503
0.1648	40.005	7390	2.4292	0.5680
0.1276	41.0050	7570	2.3737	0.6036
0.0872	42.0050	7750	2.2201	0.6183
0.1366	43.0050	7931	2.3584	0.5917
0.0697	44.005	8111	2.2536	0.5858
0.109	45.0050	8291	2.1399	0.6095
0.1114	46.0050	8471	2.2859	0.5740
0.1043	47.0050	8652	2.4237	0.5592
0.0999	48.005	8832	2.4805	0.5385
0.2184	49.0050	9012	2.3002	0.5740
0.1776	50.0050	9192	2.3414	0.5533
0.1222	51.0050	9373	2.3839	0.5888
0.0687	52.005	9553	2.5990	0.5592
0.086	53.0050	9733	2.6002	0.5799
0.0403	54.0050	9913	2.3152	0.5947
0.0603	55.0050	10094	2.4402	0.5858
0.044	56.005	10274	2.3451	0.6124
0.1193	57.0050	10454	2.5141	0.5680
0.1068	58.0050	10634	2.6438	0.5828
0.0725	59.0050	10815	2.5138	0.5858
0.119	60.005	10995	2.5138	0.5888
0.1927	61.0050	11175	2.7776	0.5503
0.1458	62.0050	11355	2.5784	0.5592

Framework versions

Transformers 4.46.1
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.20.1

Shawon16/timesformer_wlasl_100_200ep_coR_

timesformer_wlasl_100_200ep_coR_

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Shawon16/
timesformer_wlasl_100_200ep_coR_