VideoMAE Default Fold 5 10 Epoch Aug Batch 2 4 BdSLW60 SR 8 P2 KineticsFinetuned 18 25

--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: VideoMAE_default_fold__5__10_epoch_Aug_batch_2_4_BdSLW60_SR_8_p2_kineticsFinetuned_18_25 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_default_fold__5__10_epoch_Aug_batch_2_4_BdSLW60_SR_8_p2_kineticsFinetuned_18_25 This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3601 - Accuracy: 0.9287 ## 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: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - 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: 9030 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 1.2337 | 0.1 | 903 | 0.2782 | 0.9539 | | 0.129 | 1.1001 | 1807 | 0.0212 | 0.9950 | | 0.0209 | 2.1 | 2710 | 0.0318 | 0.9938 | | 0.2046 | 3.1001 | 3614 | 0.0567 | 0.9888 | | 0.0605 | 4.1 | 4517 | 0.0598 | 0.9851 | | 0.0857 | 5.1001 | 5421 | 0.0642 | 0.9888 | | 0.0104 | 6.1 | 6324 | 0.0218 | 0.9963 | | 0.0043 | 7.1001 | 7228 | 0.0372 | 0.9925 | | 0.0002 | 8.1 | 8131 | 0.0150 | 0.9963 | | 0.0002 | 9.0995 | 9030 | 0.0223 | 0.9938 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.1

Source context: 2 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Shawon16/VideoMAE_default_fold__5__10_epoch_Aug_batch_2_4_BdSLW60_SR_8_p2_kineticsFinetuned_18_25