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vivit-b-16x2-kinetics400-ft-92397

Video Classification
Transformers
Safetensors
vivit
Generated from Trainer
Inference Endpoints
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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: google/vivit-b-16x2-kinetics400
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: vivit-b-16x2-kinetics400-ft-92397
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # vivit-b-16x2-kinetics400-ft-92397
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+
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+ This model is a fine-tuned version of [google/vivit-b-16x2-kinetics400](https://huggingface.co/google/vivit-b-16x2-kinetics400) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.1332
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+ - Accuracy: 0.3386
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - training_steps: 5500
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|
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+ | 1.1076 | 0.0202 | 111 | 1.1240 | 0.3333 |
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+ | 1.1466 | 1.0202 | 222 | 1.1166 | 0.3333 |
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+ | 1.1022 | 2.0202 | 333 | 1.1221 | 0.3333 |
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+ | 1.109 | 3.0202 | 444 | 1.1170 | 0.3333 |
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+ | 1.1007 | 4.0202 | 555 | 1.1375 | 0.3333 |
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+ | 1.1146 | 5.0202 | 666 | 1.1004 | 0.3386 |
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+ | 1.1575 | 6.0202 | 777 | 1.1249 | 0.3333 |
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+ | 1.0851 | 7.0202 | 888 | 1.1254 | 0.3439 |
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+ | 1.1069 | 8.0202 | 999 | 1.1136 | 0.3333 |
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+ | 1.0998 | 9.0202 | 1110 | 1.0945 | 0.3545 |
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+ | 1.1289 | 10.0202 | 1221 | 1.0992 | 0.3439 |
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+ | 1.0674 | 11.0202 | 1332 | 1.0957 | 0.3545 |
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+ | 1.1144 | 12.0202 | 1443 | 1.1139 | 0.3228 |
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+ | 1.0971 | 13.0202 | 1554 | 1.1089 | 0.3228 |
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+ | 1.0704 | 14.0202 | 1665 | 1.1031 | 0.3333 |
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+ | 1.1064 | 15.0202 | 1776 | 1.1003 | 0.3492 |
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+ | 1.0782 | 16.0202 | 1887 | 1.1026 | 0.3386 |
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+ | 1.1086 | 17.0202 | 1998 | 1.1091 | 0.3175 |
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+ | 1.0911 | 18.0202 | 2109 | 1.0965 | 0.3386 |
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+ | 1.0961 | 19.0202 | 2220 | 1.1108 | 0.3333 |
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+ | 1.0967 | 20.0202 | 2331 | 1.1029 | 0.3175 |
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+ | 1.0746 | 21.0202 | 2442 | 1.1127 | 0.3333 |
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+ | 1.1076 | 22.0202 | 2553 | 1.0996 | 0.3492 |
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+ | 1.0786 | 23.0202 | 2664 | 1.1138 | 0.3333 |
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+ | 1.0819 | 24.0202 | 2775 | 1.0970 | 0.3651 |
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+ | 1.1031 | 25.0202 | 2886 | 1.1135 | 0.3333 |
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+ | 1.092 | 26.0202 | 2997 | 1.1050 | 0.3439 |
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+ | 1.103 | 27.0202 | 3108 | 1.1039 | 0.3598 |
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+ | 1.0903 | 28.0202 | 3219 | 1.1149 | 0.3333 |
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+ | 1.1232 | 29.0202 | 3330 | 1.1062 | 0.3333 |
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+ | 1.106 | 30.0202 | 3441 | 1.1124 | 0.3175 |
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+ | 1.0607 | 31.0202 | 3552 | 1.1095 | 0.3333 |
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+ | 1.0839 | 32.0202 | 3663 | 1.1083 | 0.3386 |
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+ | 1.0867 | 33.0202 | 3774 | 1.1007 | 0.3545 |
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+ | 1.0913 | 34.0202 | 3885 | 1.0996 | 0.3598 |
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+ | 1.0567 | 35.0202 | 3996 | 1.0946 | 0.3386 |
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+ | 1.0877 | 36.0202 | 4107 | 1.1004 | 0.3280 |
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+ | 1.0828 | 37.0202 | 4218 | 1.1074 | 0.3228 |
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+ | 1.131 | 38.0202 | 4329 | 1.0992 | 0.3122 |
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+ | 1.0299 | 39.0202 | 4440 | 1.1035 | 0.3280 |
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+ | 1.0864 | 40.0202 | 4551 | 1.0947 | 0.3386 |
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+ | 1.0643 | 41.0202 | 4662 | 1.1006 | 0.3545 |
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+ | 1.0687 | 42.0202 | 4773 | 1.1056 | 0.3280 |
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+ | 1.0978 | 43.0202 | 4884 | 1.0907 | 0.3598 |
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+ | 1.0273 | 44.0202 | 4995 | 1.0969 | 0.3439 |
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+ | 1.0459 | 45.0202 | 5106 | 1.1021 | 0.3492 |
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+ | 1.0561 | 46.0202 | 5217 | 1.1003 | 0.3386 |
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+ | 1.0482 | 47.0202 | 5328 | 1.1028 | 0.3386 |
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+ | 1.0916 | 48.0202 | 5439 | 1.1053 | 0.3545 |
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+ | 1.0729 | 49.0111 | 5500 | 1.1055 | 0.3545 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.2
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+ - Pytorch 1.13.0+cu117
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
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