--- base_model: microsoft/git-large-r-coco datasets: - imagefolder library_name: transformers license: mit tags: - generated_from_trainer model-index: - name: git-large-r-coco-IDB_ADv1_COCOv6-r results: [] --- # git-large-r-coco-IDB_ADv1_COCOv6-r This model is a fine-tuned version of [microsoft/git-large-r-coco](https://huggingface.co/microsoft/git-large-r-coco) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0568 - Meteor Score: {'meteor': 0.7213608939819159} ## 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: 3e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 15 - num_epochs: 150 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Meteor Score | |:-------------:|:--------:|:----:|:---------------:|:--------------------------------:| | 91.9877 | 1.4815 | 5 | 11.4608 | {'meteor': 0.046723581008416726} | | 84.6168 | 2.9630 | 10 | 9.8170 | {'meteor': 0.043886991456607405} | | 77.4092 | 4.4444 | 15 | 9.1821 | {'meteor': 0.06849055586443556} | | 71.3318 | 5.9259 | 20 | 8.3193 | {'meteor': 0.061564918769236095} | | 65.2006 | 7.4074 | 25 | 7.6103 | {'meteor': 0.0754162033563066} | | 60.1084 | 8.8889 | 30 | 7.0968 | {'meteor': 0.1078676449812926} | | 56.3449 | 10.3704 | 35 | 6.6624 | {'meteor': 0.16409628582839803} | | 53.0194 | 11.8519 | 40 | 6.2707 | {'meteor': 0.17640875038111917} | | 49.885 | 13.3333 | 45 | 5.9076 | {'meteor': 0.20852663016362413} | | 47.1535 | 14.8148 | 50 | 5.5612 | {'meteor': 0.21135425345585648} | | 44.3251 | 16.2963 | 55 | 5.2211 | {'meteor': 0.25163567787871793} | | 41.4631 | 17.7778 | 60 | 4.8845 | {'meteor': 0.36102118410159373} | | 38.8862 | 19.2593 | 65 | 4.5516 | {'meteor': 0.40677731182809435} | | 36.2603 | 20.7407 | 70 | 4.2228 | {'meteor': 0.42841296399868456} | | 33.5914 | 22.2222 | 75 | 3.8960 | {'meteor': 0.4442706614076698} | | 31.0193 | 23.7037 | 80 | 3.5716 | {'meteor': 0.44790686728484286} | | 28.4252 | 25.1852 | 85 | 3.2513 | {'meteor': 0.4634788207419503} | | 25.927 | 26.6667 | 90 | 2.9379 | {'meteor': 0.46009853575600396} | | 23.4406 | 28.1481 | 95 | 2.6312 | {'meteor': 0.4702576268818571} | | 21.0641 | 29.6296 | 100 | 2.3319 | {'meteor': 0.4724561426359612} | | 18.6236 | 31.1111 | 105 | 2.0445 | {'meteor': 0.4759864205500001} | | 16.4033 | 32.5926 | 110 | 1.7706 | {'meteor': 0.4905426072931855} | | 14.1917 | 34.0741 | 115 | 1.5119 | {'meteor': 0.48899836960829424} | | 12.1305 | 35.5556 | 120 | 1.2729 | {'meteor': 0.5241780827869041} | | 10.2674 | 37.0370 | 125 | 1.0567 | {'meteor': 0.5097833762351499} | | 8.5593 | 38.5185 | 130 | 0.8637 | {'meteor': 0.4997929450485001} | | 7.013 | 40.0 | 135 | 0.6974 | {'meteor': 0.5105554674131357} | | 5.666 | 41.4815 | 140 | 0.5584 | {'meteor': 0.5152670866288012} | | 4.5962 | 42.9630 | 145 | 0.4444 | {'meteor': 0.5396604542862918} | | 3.6125 | 44.4444 | 150 | 0.3520 | {'meteor': 0.5617093146804203} | | 2.9293 | 45.9259 | 155 | 0.2796 | {'meteor': 0.5886004681488384} | | 2.259 | 47.4074 | 160 | 0.2219 | {'meteor': 0.553105842439049} | | 1.8309 | 48.8889 | 165 | 0.1785 | {'meteor': 0.5814978557330325} | | 1.4494 | 50.3704 | 170 | 0.1458 | {'meteor': 0.6231566268972263} | | 1.1158 | 51.8519 | 175 | 0.1248 | {'meteor': 0.6099620120504717} | | 0.9661 | 53.3333 | 180 | 0.1060 | {'meteor': 0.6546451210304227} | | 0.7271 | 54.8148 | 185 | 0.0880 | {'meteor': 0.6338177371529071} | | 0.6177 | 56.2963 | 190 | 0.0775 | {'meteor': 0.6518057184133086} | | 0.4962 | 57.7778 | 195 | 0.0698 | {'meteor': 0.6193332334649128} | | 0.4415 | 59.2593 | 200 | 0.0652 | {'meteor': 0.7079004093008636} | | 0.3629 | 60.7407 | 205 | 0.0632 | {'meteor': 0.6940804620155375} | | 0.3219 | 62.2222 | 210 | 0.0595 | {'meteor': 0.6912091264820688} | | 0.2679 | 63.7037 | 215 | 0.0545 | {'meteor': 0.7030913652832623} | | 0.234 | 65.1852 | 220 | 0.0537 | {'meteor': 0.6676437817167338} | | 0.2026 | 66.6667 | 225 | 0.0534 | {'meteor': 0.6811270764539213} | | 0.1843 | 68.1481 | 230 | 0.0515 | {'meteor': 0.6779206905106651} | | 0.1662 | 69.6296 | 235 | 0.0511 | {'meteor': 0.715690856984691} | | 0.1432 | 71.1111 | 240 | 0.0502 | {'meteor': 0.7156957872649886} | | 0.1331 | 72.5926 | 245 | 0.0495 | {'meteor': 0.6543089583737782} | | 0.1156 | 74.0741 | 250 | 0.0494 | {'meteor': 0.6594307549550437} | | 0.1021 | 75.5556 | 255 | 0.0485 | {'meteor': 0.6576803078677578} | | 0.092 | 77.0370 | 260 | 0.0487 | {'meteor': 0.6811188428011016} | | 0.0809 | 78.5185 | 265 | 0.0498 | {'meteor': 0.7035441241921582} | | 0.0723 | 80.0 | 270 | 0.0506 | {'meteor': 0.7038905922785134} | | 0.0649 | 81.4815 | 275 | 0.0502 | {'meteor': 0.7421547726024114} | | 0.0608 | 82.9630 | 280 | 0.0507 | {'meteor': 0.730981066765409} | | 0.0491 | 84.4444 | 285 | 0.0518 | {'meteor': 0.7443890531705978} | | 0.0458 | 85.9259 | 290 | 0.0534 | {'meteor': 0.7176754184986865} | | 0.0388 | 87.4074 | 295 | 0.0523 | {'meteor': 0.7176410382626139} | | 0.0334 | 88.8889 | 300 | 0.0534 | {'meteor': 0.7026151957465668} | | 0.026 | 90.3704 | 305 | 0.0520 | {'meteor': 0.7362294347533668} | | 0.0241 | 91.8519 | 310 | 0.0531 | {'meteor': 0.7236102999038347} | | 0.0211 | 93.3333 | 315 | 0.0542 | {'meteor': 0.7152270797326922} | | 0.0182 | 94.8148 | 320 | 0.0547 | {'meteor': 0.7175103144573044} | | 0.0175 | 96.2963 | 325 | 0.0547 | {'meteor': 0.7140333983909092} | | 0.0158 | 97.7778 | 330 | 0.0558 | {'meteor': 0.7230279458318826} | | 0.0153 | 99.2593 | 335 | 0.0560 | {'meteor': 0.7216506562988318} | | 0.0133 | 100.7407 | 340 | 0.0558 | {'meteor': 0.7089953285495003} | | 0.0137 | 102.2222 | 345 | 0.0557 | {'meteor': 0.7050918755935003} | | 0.0127 | 103.7037 | 350 | 0.0561 | {'meteor': 0.7140423279140877} | | 0.0118 | 105.1852 | 355 | 0.0563 | {'meteor': 0.716693515553002} | | 0.0121 | 106.6667 | 360 | 0.0559 | {'meteor': 0.7213321747823379} | | 0.0115 | 108.1481 | 365 | 0.0559 | {'meteor': 0.7244157912903824} | | 0.0107 | 109.6296 | 370 | 0.0559 | {'meteor': 0.7258048026400388} | | 0.0115 | 111.1111 | 375 | 0.0562 | {'meteor': 0.7249978244263323} | | 0.0107 | 112.5926 | 380 | 0.0564 | {'meteor': 0.7261256725269803} | | 0.0103 | 114.0741 | 385 | 0.0565 | {'meteor': 0.7222910388476055} | | 0.0102 | 115.5556 | 390 | 0.0567 | {'meteor': 0.7209011950646473} | | 0.0099 | 117.0370 | 395 | 0.0567 | {'meteor': 0.7221559565497234} | | 0.0101 | 118.5185 | 400 | 0.0567 | {'meteor': 0.7234189947956161} | | 0.0096 | 120.0 | 405 | 0.0567 | {'meteor': 0.7231619412067093} | | 0.0097 | 121.4815 | 410 | 0.0567 | {'meteor': 0.7224063072061223} | | 0.0103 | 122.9630 | 415 | 0.0567 | {'meteor': 0.7209850209026843} | | 0.0101 | 124.4444 | 420 | 0.0567 | {'meteor': 0.7208576227726795} | | 0.0095 | 125.9259 | 425 | 0.0567 | {'meteor': 0.7215621466948617} | | 0.0093 | 127.4074 | 430 | 0.0568 | {'meteor': 0.7221874032851583} | | 0.01 | 128.8889 | 435 | 0.0568 | {'meteor': 0.7207488272286875} | | 0.0097 | 130.3704 | 440 | 0.0568 | {'meteor': 0.7213694074696705} | | 0.0096 | 131.8519 | 445 | 0.0568 | {'meteor': 0.7213044564985146} | | 0.0099 | 133.3333 | 450 | 0.0568 | {'meteor': 0.7213608939819159} | ### Framework versions - Transformers 4.46.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.20.2