--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: swin-resnet-mistral-SYDNEY-with-all-captioning results: [] --- # swin-resnet-mistral-SYDNEY-with-all-captioning This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3232 - Accuracy: 65.7 - Bleu-1: 0.5949 - Bleu-2: 0.5339 - Bleu-3: 0.4914 - Bleu-4: 0.4557 - Meteor: 0.5436 - Rouge-l: 0.6098 - Cider: 1.6017 ## 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: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 50 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1024 - num_epochs: 128 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Bleu-1 | Bleu-2 | Bleu-3 | Bleu-4 | Meteor | Rouge-l | Cider | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:------:|:-------:|:------:| | No log | 1.0 | 44 | 1.2803 | 40.4 | 0.3525 | 0.2768 | 0.2257 | 0.1881 | 0.5016 | 0.4202 | 0.5907 | | No log | 2.0 | 88 | 1.0514 | 61.77 | 0.4774 | 0.3355 | 0.2522 | 0.1907 | 0.4166 | 0.4002 | 0.4287 | | No log | 3.0 | 132 | 0.9772 | 63.06 | 0.4879 | 0.3441 | 0.2625 | 0.2029 | 0.4153 | 0.4023 | 0.4082 | | No log | 4.0 | 176 | 0.9050 | 63.2 | 0.4597 | 0.3120 | 0.2307 | 0.1715 | 0.3604 | 0.3639 | 0.3364 | | No log | 5.0 | 220 | 0.8187 | 63.37 | 0.5164 | 0.3760 | 0.2936 | 0.2326 | 0.4161 | 0.4183 | 0.5877 | | No log | 6.0 | 264 | 0.7221 | 64.99 | 0.4858 | 0.3419 | 0.2627 | 0.2076 | 0.3554 | 0.3831 | 0.6604 | | No log | 7.0 | 308 | 0.6234 | 65.16 | 0.5295 | 0.3903 | 0.3082 | 0.2448 | 0.4427 | 0.4342 | 0.7510 | | No log | 8.0 | 352 | 0.5437 | 65.47 | 0.5267 | 0.3811 | 0.2972 | 0.2365 | 0.4509 | 0.4387 | 0.7940 | | No log | 9.0 | 396 | 0.5210 | 66.25 | 0.5267 | 0.4112 | 0.3427 | 0.2956 | 0.4322 | 0.4488 | 1.1293 | | No log | 10.0 | 440 | 0.5277 | 66.31 | 0.6364 | 0.5407 | 0.4771 | 0.4297 | 0.5541 | 0.5557 | 1.8319 | | No log | 11.0 | 484 | 0.5085 | 65.06 | 0.6104 | 0.5088 | 0.4397 | 0.3882 | 0.5494 | 0.5520 | 1.7389 | | No log | 12.0 | 528 | 0.5123 | 66.97 | 0.6496 | 0.5495 | 0.4797 | 0.4301 | 0.5773 | 0.5768 | 1.7341 | | No log | 13.0 | 572 | 0.5340 | 66.23 | 0.4950 | 0.3817 | 0.3181 | 0.2718 | 0.4101 | 0.4507 | 1.2149 | | No log | 14.0 | 616 | 0.5329 | 65.39 | 0.6253 | 0.5224 | 0.4452 | 0.3868 | 0.5576 | 0.5502 | 1.5926 | | No log | 15.0 | 660 | 0.5461 | 65.92 | 0.6656 | 0.5754 | 0.5075 | 0.4546 | 0.5780 | 0.5894 | 1.8762 | | No log | 16.0 | 704 | 0.5435 | 64.68 | 0.6365 | 0.5344 | 0.4565 | 0.3999 | 0.5685 | 0.5655 | 1.8068 | | No log | 17.0 | 748 | 0.5619 | 66.19 | 0.6833 | 0.5911 | 0.5134 | 0.4465 | 0.5917 | 0.6082 | 1.7530 | | No log | 18.0 | 792 | 0.5653 | 67.21 | 0.6432 | 0.5915 | 0.5493 | 0.5167 | 0.6103 | 0.6437 | 2.0025 | | No log | 19.0 | 836 | 0.5855 | 63.68 | 0.6954 | 0.5975 | 0.5215 | 0.4622 | 0.6169 | 0.6120 | 1.9900 | | No log | 20.0 | 880 | 0.6408 | 65.66 | 0.6691 | 0.5775 | 0.5106 | 0.4595 | 0.6005 | 0.6201 | 1.6515 | | No log | 21.0 | 924 | 0.6872 | 67.74 | 0.6715 | 0.5886 | 0.5357 | 0.4988 | 0.5834 | 0.6151 | 1.9363 | | No log | 22.0 | 968 | 0.6886 | 67.71 | 0.6965 | 0.6232 | 0.5719 | 0.5328 | 0.6193 | 0.6512 | 1.9162 | | No log | 23.0 | 1012 | 0.7542 | 68.1 | 0.6502 | 0.5819 | 0.5336 | 0.4944 | 0.5734 | 0.6080 | 1.7041 | | 0.6311 | 24.0 | 1056 | 0.8377 | 68.45 | 0.6886 | 0.6151 | 0.5662 | 0.5214 | 0.5968 | 0.6452 | 1.8615 | | 0.6311 | 25.0 | 1100 | 1.1727 | 66.68 | 0.6665 | 0.5867 | 0.5296 | 0.4833 | 0.5548 | 0.6124 | 1.4923 | | 0.6311 | 26.0 | 1144 | 1.2276 | 65.85 | 0.6264 | 0.5559 | 0.5134 | 0.4719 | 0.5668 | 0.6141 | 1.6275 | | 0.6311 | 27.0 | 1188 | 1.3551 | 66.24 | 0.5980 | 0.5307 | 0.4856 | 0.4470 | 0.5345 | 0.6031 | 1.4574 | | 0.6311 | 28.0 | 1232 | 1.2643 | 67.33 | 0.6410 | 0.5789 | 0.5327 | 0.4950 | 0.5766 | 0.6416 | 1.6530 | | 0.6311 | 29.0 | 1276 | 1.4213 | 65.98 | 0.4811 | 0.3962 | 0.3426 | 0.2991 | 0.4590 | 0.5586 | 0.9854 | | 0.6311 | 30.0 | 1320 | 1.3364 | 65.73 | 0.5691 | 0.4999 | 0.4555 | 0.4207 | 0.5231 | 0.5991 | 1.3969 | | 0.6311 | 31.0 | 1364 | 1.3737 | 65.49 | 0.5799 | 0.5158 | 0.4759 | 0.4416 | 0.5276 | 0.6097 | 1.4832 | | 0.6311 | 32.0 | 1408 | 1.3232 | 65.7 | 0.5949 | 0.5339 | 0.4914 | 0.4557 | 0.5436 | 0.6098 | 1.6017 | ### Framework versions - Transformers 5.12.1 - Pytorch 2.12.1+cu130 - Datasets 5.0.0 - Tokenizers 0.22.2