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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-UCF-Crime |
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results: [] |
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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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# vivit-b-16x2-kinetics400-UCF-Crime |
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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 UCF-Crime dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9757 |
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- Accuracy: 0.6149 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 3132 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 1.2072 | 0.06 | 196 | 1.6400 | 0.5518 | |
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| 1.5513 | 1.06 | 392 | 1.4988 | 0.5634 | |
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| 1.1038 | 2.06 | 588 | 1.5328 | 0.5861 | |
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| 0.9462 | 3.06 | 784 | 1.3932 | 0.6178 | |
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| 0.7387 | 4.06 | 980 | 1.5449 | 0.6060 | |
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| 0.5085 | 5.06 | 1176 | 1.3075 | 0.6287 | |
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| 0.4443 | 6.06 | 1372 | 1.6743 | 0.6001 | |
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| 0.4695 | 7.06 | 1568 | 1.5287 | 0.6172 | |
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| 0.4409 | 8.06 | 1764 | 1.7749 | 0.6089 | |
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| 0.1158 | 9.06 | 1960 | 1.9027 | 0.6076 | |
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| 0.1183 | 10.06 | 2156 | 1.9622 | 0.6085 | |
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| 0.1322 | 11.06 | 2352 | 2.0872 | 0.6152 | |
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| 0.1881 | 12.06 | 2548 | 2.0095 | 0.6094 | |
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| 0.0932 | 13.06 | 2744 | 1.9398 | 0.6232 | |
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| 0.0303 | 14.06 | 2940 | 1.9994 | 0.6134 | |
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| 0.0513 | 15.06 | 3132 | 1.9757 | 0.6149 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 1.10.0+cu113 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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