Videomae Base Finetuned Kinetics 0313 Clip Duration Abnormal02 Shortsidescale

--- 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-base-finetuned-kinetics-0313-clip_duration-abnormal02_shortsidescale 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-kinetics-0313-clip_duration-abnormal02_shortsidescale 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: 0.2529 - Accuracy: 0.9137 ## 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: 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: 0.05 - training_steps: 35000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3968 | 0.01 | 350 | 0.2910 | 0.9072 | | 0.5595 | 1.01 | 700 | 0.4113 | 0.8806 | | 0.0738 | 2.01 | 1050 | 0.4882 | 0.8554 | | 0.034 | 3.01 | 1400 | 0.3137 | 0.8966 | | 0.0038 | 4.01 | 1750 | 0.3382 | 0.8846 | | 0.2912 | 5.01 | 2100 | 0.4349 | 0.8753 | | 0.0587 | 6.01 | 2450 | 0.3697 | 0.8833 | | 0.001 | 7.01 | 2800 | 0.3930 | 0.9019 | | 0.0011 | 8.01 | 3150 | 0.4435 | 0.9045 | | 0.0009 | 9.01 | 3500 | 0.5117 | 0.8820 | | 0.0012 | 10.01 | 3850 | 0.4376 | 0.8939 | ### Framework versions - Transformers 4.48.0 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0

Source context: 3 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo d2o2ji/videomae-base-finetuned-kinetics-0313-clip_duration-abnormal02_shortsidescale