RALL RGBCROP Aug16F 1DO1

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-06
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• 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: 3462

Training results

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.1

--- 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: RALL_RGBCROP_Aug16F-1DO1 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. --> # RALL_RGBCROP_Aug16F-1DO1 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: 0.6522 - Accuracy: 0.8173 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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: 3462 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.5263 | 0.0835 | 289 | 0.6351 | 0.6135 | | 0.2545 | 1.0835 | 578 | 0.4965 | 0.7812 | | 0.1261 | 2.0835 | 867 | 0.5261 | 0.8098 | | 0.0753 | 3.0835 | 1156 | 0.6260 | 0.8160 | | 0.0246 | 4.0835 | 1445 | 0.7591 | 0.8160 | | 0.0587 | 5.0835 | 1734 | 0.8468 | 0.8221 | | 0.0351 | 6.0835 | 2023 | 0.8952 | 0.8180 | | 0.001 | 7.0835 | 2312 | 1.0094 | 0.8098 | | 0.0038 | 8.0835 | 2601 | 1.0583 | 0.8200 | | 0.0011 | 9.0835 | 2890 | 1.0632 | 0.8119 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1