Vit Convnext Mistral SYDNEY Without Captioning

--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-convnext-Mistral-SYDNEY-without-captioning results: [] --- # vit-convnext-Mistral-SYDNEY-without-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: 0.8367 - Accuracy: 67.63 - Bleu-1: 0.7928 - Bleu-2: 0.7220 - Bleu-3: 0.6579 - Bleu-4: 0.6002 - Meteor: 0.7417 - Rouge-l: 0.7053 - Cider: 2.3978 ## 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: 0.1 - 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 | 1.2376 | 65.66 | 0.5431 | 0.4284 | 0.3465 | 0.2760 | 0.4575 | 0.4786 | 0.9774 | | No log | 2.0 | 88 | 0.6974 | 66.27 | 0.6151 | 0.5148 | 0.4371 | 0.3711 | 0.5162 | 0.5474 | 1.3648 | | No log | 3.0 | 132 | 0.6614 | 66.3 | 0.7438 | 0.6503 | 0.5595 | 0.4773 | 0.6512 | 0.6327 | 1.9478 | | No log | 4.0 | 176 | 0.6721 | 67.75 | 0.8124 | 0.7212 | 0.6343 | 0.5552 | 0.7269 | 0.7223 | 2.3770 | | No log | 5.0 | 220 | 0.6521 | 66.13 | 0.7990 | 0.7235 | 0.6458 | 0.5690 | 0.7283 | 0.6924 | 2.2489 | | No log | 6.0 | 264 | 0.6639 | 68.09 | 0.7866 | 0.7020 | 0.6197 | 0.5439 | 0.7180 | 0.7114 | 2.2763 | | No log | 7.0 | 308 | 0.6893 | 68.15 | 0.8273 | 0.7658 | 0.7041 | 0.6454 | 0.7746 | 0.7526 | 2.9048 | | No log | 8.0 | 352 | 0.6894 | 68.74 | 0.8286 | 0.7575 | 0.6900 | 0.6243 | 0.7834 | 0.7400 | 2.6251 | | No log | 9.0 | 396 | 0.7343 | 67.58 | 0.8253 | 0.7407 | 0.6564 | 0.5829 | 0.7493 | 0.7258 | 2.5931 | | No log | 10.0 | 440 | 0.7883 | 67.22 | 0.7913 | 0.7041 | 0.6284 | 0.5625 | 0.7276 | 0.7012 | 2.5651 | | No log | 11.0 | 484 | 0.7554 | 66.74 | 0.7693 | 0.6905 | 0.6187 | 0.5521 | 0.7231 | 0.6736 | 2.4674 | | No log | 12.0 | 528 | 0.7862 | 68.92 | 0.7901 | 0.7107 | 0.6425 | 0.5814 | 0.7274 | 0.7146 | 2.5855 | | No log | 13.0 | 572 | 0.8002 | 68.02 | 0.8016 | 0.7256 | 0.6549 | 0.5922 | 0.7456 | 0.7283 | 2.5073 | | No log | 14.0 | 616 | 0.7962 | 68.65 | 0.8116 | 0.7431 | 0.6754 | 0.6153 | 0.7547 | 0.7385 | 2.8192 | | No log | 15.0 | 660 | 0.8634 | 67.96 | 0.7866 | 0.7026 | 0.6258 | 0.5549 | 0.7320 | 0.6936 | 2.5139 | | No log | 16.0 | 704 | 0.8357 | 68.35 | 0.7945 | 0.7224 | 0.6631 | 0.6119 | 0.7787 | 0.7421 | 2.7519 | | No log | 17.0 | 748 | 0.8367 | 67.63 | 0.7928 | 0.7220 | 0.6579 | 0.6002 | 0.7417 | 0.7053 | 2.3978 | ### Framework versions - Transformers 5.12.1 - Pytorch 2.12.1+cu130 - Datasets 5.0.0 - Tokenizers 0.22.2