---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base-finetuned-ssv2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-ssv2-finetuned-sports-videos-in-the-wild
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# videomae-base-finetuned-ssv2-finetuned-sports-videos-in-the-wild

This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-ssv2](https://huggingface.co/MCG-NJU/videomae-base-finetuned-ssv2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1496
- Accuracy: 0.7594
- Macro Precision: 0.6910
- Macro Recall: 0.7199
- Macro F1: 0.6905
- Weighted Precision: 0.7833
- Weighted Recall: 0.7594
- Weighted F1: 0.7598

## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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: 8400

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | Macro Precision | Macro Recall | Macro F1 | Weighted Precision | Weighted Recall | Weighted F1 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
| 1.6313        | 0.0501  | 421  | 1.6631          | 0.5297   | 0.5192          | 0.4451       | 0.4010   | 0.6102             | 0.5297          | 0.4954      |
| 1.8344        | 1.0501  | 842  | 2.3963          | 0.3274   | 0.4950          | 0.3302       | 0.2925   | 0.6196             | 0.3274          | 0.3216      |
| 1.9713        | 2.0501  | 1263 | 1.6678          | 0.5111   | 0.5007          | 0.4767       | 0.4215   | 0.6097             | 0.5111          | 0.5061      |
| 1.7598        | 3.0501  | 1684 | 1.5902          | 0.5275   | 0.5426          | 0.4819       | 0.4417   | 0.6160             | 0.5275          | 0.5152      |
| 1.5432        | 4.0501  | 2105 | 1.4812          | 0.5603   | 0.5400          | 0.4867       | 0.4513   | 0.6482             | 0.5603          | 0.5514      |
| 1.3945        | 5.0501  | 2526 | 1.5876          | 0.5286   | 0.5659          | 0.5077       | 0.4697   | 0.6529             | 0.5286          | 0.5421      |
| 1.2047        | 6.0501  | 2947 | 1.2964          | 0.6376   | 0.5846          | 0.5892       | 0.5527   | 0.6834             | 0.6376          | 0.6361      |
| 1.2434        | 7.0501  | 3368 | 1.4268          | 0.5793   | 0.5920          | 0.5630       | 0.5116   | 0.6897             | 0.5793          | 0.5842      |
| 1.1157        | 8.0501  | 3789 | 1.1934          | 0.6533   | 0.6153          | 0.6177       | 0.5837   | 0.7081             | 0.6533          | 0.6504      |
| 0.9056        | 9.0501  | 4210 | 1.1501          | 0.6551   | 0.6268          | 0.6337       | 0.5984   | 0.7202             | 0.6551          | 0.6658      |
| 0.7085        | 10.0501 | 4631 | 1.1881          | 0.6810   | 0.6295          | 0.6458       | 0.6101   | 0.7221             | 0.6810          | 0.6826      |
| 0.6789        | 11.0501 | 5052 | 1.1540          | 0.7043   | 0.6744          | 0.6577       | 0.6396   | 0.7304             | 0.7043          | 0.6959      |
| 0.5957        | 12.0501 | 5473 | 1.2473          | 0.6832   | 0.6210          | 0.6377       | 0.6077   | 0.7028             | 0.6832          | 0.6759      |
| 0.4533        | 13.0501 | 5894 | 1.1441          | 0.6927   | 0.6495          | 0.6450       | 0.6278   | 0.7211             | 0.6927          | 0.6924      |
| 0.4165        | 14.0501 | 6315 | 1.2567          | 0.7116   | 0.6708          | 0.6839       | 0.6468   | 0.7622             | 0.7116          | 0.7094      |
| 0.2661        | 15.0501 | 6736 | 1.1229          | 0.7204   | 0.6526          | 0.6809       | 0.6417   | 0.7598             | 0.7204          | 0.7226      |
| 0.2679        | 16.0501 | 7157 | 1.1790          | 0.7382   | 0.6838          | 0.7055       | 0.6717   | 0.7792             | 0.7382          | 0.7397      |
| 0.1875        | 17.0501 | 7578 | 1.1689          | 0.7506   | 0.6983          | 0.7060       | 0.6894   | 0.7746             | 0.7506          | 0.7529      |
| 0.2501        | 18.0501 | 7999 | 1.1802          | 0.7557   | 0.6911          | 0.7190       | 0.6883   | 0.7802             | 0.7557          | 0.7537      |
| 0.1072        | 19.0477 | 8400 | 1.1496          | 0.7594   | 0.6910          | 0.7199       | 0.6905   | 0.7833             | 0.7594          | 0.7598      |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.1.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1