VideoMAEv2 Huge Finetuned Deception Dataset Mae Huge

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
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.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: 300

Training results

Framework versions

• Transformers 4.48.0
• Pytorch 2.1.0+cu121
• Datasets 2.14.4
• Tokenizers 0.21.1

--- library_name: transformers license: cc-by-nc-4.0 base_model: OpenGVLab/VideoMAEv2-Huge tags: - generated_from_trainer metrics: - accuracy model-index: - name: VideoMAEv2-Huge-finetuned-deception-dataset-mae-huge 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. --> # VideoMAEv2-Huge-finetuned-deception-dataset-mae-huge This model is a fine-tuned version of [OpenGVLab/VideoMAEv2-Huge](https://huggingface.co/OpenGVLab/VideoMAEv2-Huge) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3499 - Accuracy: 0.4074 ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.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: 300 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.7736 | 1.0 | 38 | 0.9148 | 0.7037 | | 0.6591 | 2.0 | 76 | 0.8406 | 0.3333 | | 0.7471 | 3.0 | 114 | 0.9379 | 0.3086 | | 0.702 | 4.0 | 152 | 0.9688 | 0.2840 | | 0.6571 | 5.0 | 190 | 1.1333 | 0.3086 | | 0.5764 | 6.0 | 228 | 1.3130 | 0.3457 | | 0.5255 | 7.0 | 266 | 1.3296 | 0.3210 | | 0.4883 | 7.8947 | 300 | 1.3499 | 0.4074 | ### Framework versions - Transformers 4.48.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.4 - Tokenizers 0.21.1