Timesformer Base Finetuned K400 Finetuned Yt Short Classification Sample Rate16

--- library_name: transformers license: cc-by-nc-4.0 base_model: facebook/timesformer-base-finetuned-k400 tags: - generated_from_trainer metrics: - accuracy model-index: - name: timesformer-base-finetuned-k400-finetuned-yt_short_classification-sample_rate16 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. --> # timesformer-base-finetuned-k400-finetuned-yt_short_classification-sample_rate16 This model is a fine-tuned version of [facebook/timesformer-base-finetuned-k400](https://huggingface.co/facebook/timesformer-base-finetuned-k400) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2786 - Accuracy: 0.9220 - 0 Precision: 0.8978 - 0 Recall: 0.9432 - 0 F1-score: 0.9200 - 0 Support: 5796.0 - 1 Precision: 0.9460 - 1 Recall: 0.9026 - 1 F1-score: 0.9238 - 1 Support: 6389.0 - Accuracy F1-score: 0.9220 - Macro avg Precision: 0.9219 - Macro avg Recall: 0.9229 - Macro avg F1-score: 0.9219 - Macro avg Support: 12185.0 - Weighted avg Precision: 0.9231 - Weighted avg Recall: 0.9220 - Weighted avg F1-score: 0.9220 - Weighted avg Support: 12185.0 ## 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: 0.1 - training_steps: 19800 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | 0 Precision | 0 Recall | 0 F1-score | 0 Support | 1 Precision | 1 Recall | 1 F1-score | 1 Support | Accuracy F1-score | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Macro avg Support | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score | Weighted avg Support | |:-------------:|:-------:|:-----:|:---------------:|:--------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------------:|:-------------------:|:----------...