--- library_name: transformers license: cc-by-nc-4.0 base_model: facebook/timesformer-base-finetuned-k400 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: Timesformer_WLASL_100_200_epochs_p20_SR_16 results: [] --- # Timesformer_WLASL_100_200_epochs_p20_SR_16 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: 2.2599 - Top 1 Accuracy: 0.5828 - Top 5 Accuracy: 0.7899 - Top 10 Accuracy: 0.8698 - Accuracy: 0.5828 - Precision: 0.5806 - Recall: 0.5828 - F1: 0.5510 ## 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: 36000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Top 1 Accuracy | Top 5 Accuracy | Top 10 Accuracy | Accuracy | Precision | Recall | F1 | |:-------------:|:-------:|:----:|:---------------:|:--------------:|:--------------:|:---------------:|:--------:|:---------:|:------:|:------:| | 19.1155 | 0.005 | 180 | 4.6927 | 0.0089 | 0.0414 | 0.0888 | 0.0089 | 0.0155 | 0.0089 | 0.0105 | | 18.5538 | 1.0050 | 360 | 4.5821 | 0.0266 | 0.0769 | 0.1302 | 0.0266 | 0.0137 | 0.0266 | 0.0116 | | 17.5848 | 2.0050 | 540 | 4.3988 | 0.0562 | 0.1450 | 0.2633 | 0.0562 | 0.0486 | 0.0562 | 0.0390 | | 15.8283 | 3.0050 | 721 | 4.0516 | 0.1302 | 0.2959 | 0.4645 | 0.1302 | 0.1012 | 0.1302 | 0.0976 | | 13.3102 | 4.005 | 901 | 3.6150 | 0.2249 | 0.4704 | 0.6154 | 0.2249 | 0.1781 | 0.2249 | 0.1741 | | 11.2113 | 5.0050 | 1081 | 3.2389 | 0.2604 | 0.6065 | 0.7367 | 0.2604 | 0.2422 | 0.2604 | 0.2215 | | 8.898 | 6.0050 | 1261 | 2.8714 | 0.3757 | 0.6775 | 0.8166 | 0.3757 | 0.3584 | 0.3757 | 0.3324 | | 6.715 | 7.0050 | 1442 | 2.6518 | 0.4231 | 0.7249 | 0.8402 | 0.4231 | 0.3828 | 0.4231 | 0.3730 | | 4.8442 | 8.005 | 1622 | 2.3294 | 0.4645 | 0.7929 | 0.8876 | 0.4645 | 0.5077 | 0.4645 | 0.4377 | | 3.3825 | 9.0050 | 1802 | 2.1747 | 0.4911 | 0.7899 | 0.8964 | 0.4911 | 0.5436 | 0.4911 | 0.4654 | | 2.0471 | 10.0050 | 1982 | 1.9990 | 0.5148 | 0.8107 | 0.9053 | 0.5178 | 0.5871 | 0.5178 | 0.5057 | | 1.3242 | 11.0050 | 2163 | 1.8964 | 0.5473 | 0.8166 | 0.8935 | 0.5473 | 0.5822 | 0.5473 | 0.5199 | | 0.8746 | 12.005 | 2343 | 1.8222 | 0.5562 | 0.8254 | 0.9083 | 0.5562 | 0.5796 | 0.5562 | 0.5320 | | 0.5537 | 13.0050 | 2523 | 1.7525 | 0.5769 | 0.8343 | 0.9142 | 0.5769 | 0.5813 | 0.5769 | 0.5468 | | 0.4081 | 14.0050 | 2703 | 1.7351 | 0.5947 | 0.8136 | 0.8964 | 0.5947 | 0.6684 | 0.5947 | 0.5834 | | 0.17 | 15.0050 | 2884 | 1.6998 | 0.5592 | 0.8225 | 0.9083 | 0.5592 | 0.5763 | 0.5592 | 0.5342 | | 0.2053 | 16.005 | 3064 | 1.7340 | 0.5651 | 0.8343 | 0.9083 | 0.5651 | 0.6215 | 0.5651 | 0.5390 | | 0.1434 | 17.0050 | 3244 | 1.7350 | 0.6006 | 0.8432 | 0.9142 | 0.6006 | 0.6347 | 0.6006 | 0.5806 | | 0.1957 | 18.0050 | 3424 | 1.8179 | 0.5621 | 0.8373 | 0.9142 | 0.5621 | 0.6060 | 0.5621 | 0.5350 | | 0.1636 | 19.0050 | 3605 | 1.7831 | 0.6154 | 0.8225 | 0.8905 | 0.6154 | 0.6401 | 0.6154 | 0.5917 | | 0.0908 | 20.005 | 3785 | 1.7552 | 0.6213 | 0.8402 | 0.9053 | 0.6213 | 0.6504 | 0.6213 | 0.6014 | | 0.058 | 21.0050 | 3965 | 1.8422 | 0.6243 | 0.8254 | 0.9112 | 0.6213 | 0.6392 | 0.6213 | 0.5962 | | 0.0924 | 22.0050 | 4145 | 1.8347 | 0.6006 | 0.8225 | 0.9201 | 0.6006 | 0.6218 | 0.6006 | 0.5735 | | 0.0799 | 23.0050 | 4326 | 1.9650 | 0.6036 | 0.8107 | 0.8846 | 0.6036 | 0.6182 | 0.6036 | 0.5724 | | 0.176 | 24.005 | 4506 | 1.9326 | 0.5858 | 0.8402 | 0.9142 | 0.5858 | 0.6240 | 0.5858 | 0.5671 | | 0.0786 | 25.0050 | 4686 | 1.7753 | 0.6124 | 0.8491 | 0.9142 | 0.6124 | 0.6607 | 0.6124 | 0.5998 | | 0.242 | 26.0050 | 4866 | 2.0219 | 0.5769 | 0.7722 | 0.8876 | 0.5769 | 0.6337 | 0.5769 | 0.5552 | | 0.1767 | 27.0050 | 5047 | 1.9744 | 0.5828 | 0.8166 | 0.9024 | 0.5828 | 0.6330 | 0.5828 | 0.5721 | | 0.14 | 28.005 | 5227 | 2.1996 | 0.5769 | 0.7811 | 0.8609 | 0.5769 | 0.5983 | 0.5769 | 0.5430 | | 0.104 | 29.0050 | 5407 | 2.0881 | 0.5769 | 0.8166 | 0.8876 | 0.5769 | 0.6146 | 0.5769 | 0.5641 | | 0.1454 | 30.0050 | 5587 | 2.3394 | 0.5621 | 0.7959 | 0.8905 | 0.5621 | 0.6280 | 0.5621 | 0.5448 | | 0.2221 | 31.0050 | 5768 | 1.9360 | 0.5947 | 0.8225 | 0.9024 | 0.5947 | 0.6606 | 0.5947 | 0.5881 | | 0.1026 | 32.005 | 5948 | 2.0920 | 0.6036 | 0.8107 | 0.8935 | 0.6036 | 0.6376 | 0.6036 | 0.5832 | | 0.0968 | 33.0050 | 6128 | 2.2746 | 0.5740 | 0.8047 | 0.8846 | 0.5740 | 0.6308 | 0.5740 | 0.5542 | | 0.1864 | 34.0050 | 6308 | 2.2081 | 0.5888 | 0.8047 | 0.8698 | 0.5888 | 0.6394 | 0.5888 | 0.5704 | | 0.1353 | 35.0050 | 6489 | 2.1853 | 0.5799 | 0.8254 | 0.8935 | 0.5799 | 0.6133 | 0.5799 | 0.5636 | | 0.1618 | 36.005 | 6669 | 2.2661 | 0.5710 | 0.7959 | 0.8698 | 0.5710 | 0.6243 | 0.5710 | 0.5515 | | 0.259 | 37.0050 | 6849 | 2.3163 | 0.5740 | 0.7870 | 0.8580 | 0.5740 | 0.6088 | 0.5740 | 0.5459 | | 0.3394 | 38.0050 | 7029 | 2.0984 | 0.5769 | 0.7988 | 0.8905 | 0.5769 | 0.6154 | 0.5769 | 0.5614 | | 0.0833 | 39.0050 | 7210 | 2.2811 | 0.5533 | 0.8047 | 0.8698 | 0.5533 | 0.6051 | 0.5533 | 0.5328 | | 0.1259 | 40.005 | 7390 | 2.2599 | 0.5828 | 0.7899 | 0.8698 | 0.5828 | 0.5806 | 0.5828 | 0.5510 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.1