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--- |
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library_name: transformers |
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license: cc-by-nc-4.0 |
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base_model: MCG-NJU/videomae-base |
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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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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: videomae-base-finetuned-cricket_shot_detection_18 |
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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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# videomae-base-finetuned-cricket_shot_detection_18 |
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This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4708 |
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- Accuracy: 0.5789 |
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- F1: 0.5987 |
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- Recall: 0.5789 |
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- Precision: 0.7316 |
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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: 6e-06 |
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- train_batch_size: 12 |
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- eval_batch_size: 12 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 288 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
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| 1.7521 | 0.1701 | 49 | 1.7640 | 0.1579 | 0.1971 | 0.1579 | 0.2632 | |
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| 1.6025 | 1.1701 | 98 | 1.6926 | 0.2105 | 0.2205 | 0.2105 | 0.2782 | |
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| 1.3708 | 2.1701 | 147 | 1.6164 | 0.3158 | 0.3062 | 0.3158 | 0.3544 | |
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| 1.1786 | 3.1701 | 196 | 1.5217 | 0.4211 | 0.4042 | 0.4211 | 0.4158 | |
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| 1.0709 | 4.1701 | 245 | 1.4708 | 0.5789 | 0.5987 | 0.5789 | 0.7316 | |
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| 1.0057 | 5.1493 | 288 | 1.4721 | 0.5789 | 0.5982 | 0.5789 | 0.7246 | |
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### Framework versions |
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- Transformers 4.47.0 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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