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cont-vvt-gs-rot-flip-wtoken-f198-4.4-h768-t8.16.16

Video Classification
Transformers
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vivit
Generated from Trainer
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  2. model.safetensors +1 -1
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: cont-vvt-gs-rot-flip-wtoken-f198-4.4-h768-t8.16.16
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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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+ # cont-vvt-gs-rot-flip-wtoken-f198-4.4-h768-t8.16.16
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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: 0.6569
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+ - Accuracy: 0.7407
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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: 2e-06
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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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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+ | 0.868 | 0.0402 | 221 | 0.7216 | 0.6931 |
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+ | 0.715 | 1.0402 | 442 | 0.6954 | 0.7037 |
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+ | 0.7041 | 2.0402 | 663 | 0.7269 | 0.7460 |
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+ | 0.5228 | 3.0402 | 884 | 0.7158 | 0.7407 |
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+ | 0.7336 | 4.0402 | 1105 | 0.6833 | 0.7196 |
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+ | 0.5987 | 5.0402 | 1326 | 0.6155 | 0.7725 |
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+ | 0.8574 | 6.0402 | 1547 | 0.6601 | 0.7302 |
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+ | 0.6805 | 7.0402 | 1768 | 0.6374 | 0.7460 |
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+ | 0.8086 | 8.0402 | 1989 | 0.6896 | 0.6984 |
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+ | 0.6552 | 9.0402 | 2210 | 0.6535 | 0.7090 |
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+ | 0.7846 | 10.0402 | 2431 | 0.6646 | 0.7354 |
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+ | 0.6114 | 11.0402 | 2652 | 0.6111 | 0.7619 |
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+ | 0.7435 | 12.0402 | 2873 | 0.6779 | 0.7354 |
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+ | 0.7742 | 13.0402 | 3094 | 0.7390 | 0.6878 |
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+ | 0.6558 | 14.0402 | 3315 | 0.6284 | 0.7354 |
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+ | 0.4822 | 15.0402 | 3536 | 0.7071 | 0.7196 |
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+ | 0.7686 | 16.0402 | 3757 | 0.6982 | 0.7302 |
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+ | 0.7945 | 17.0402 | 3978 | 0.6336 | 0.7566 |
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+ | 0.5755 | 18.0402 | 4199 | 0.5924 | 0.7460 |
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+ | 0.6895 | 19.0402 | 4420 | 0.6227 | 0.7513 |
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+ | 0.4775 | 20.0402 | 4641 | 0.5846 | 0.7672 |
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+ | 0.8137 | 21.0402 | 4862 | 0.6724 | 0.7354 |
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+ | 0.4226 | 22.0402 | 5083 | 0.6772 | 0.7460 |
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+ | 0.6616 | 23.0402 | 5304 | 0.6856 | 0.7407 |
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+ | 0.5246 | 24.0356 | 5500 | 0.6569 | 0.7407 |
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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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