Vit Resnet Mistral UCM Without Captioning

--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-resnet-Mistral-UCM-without-captioning results: [] --- # vit-resnet-Mistral-UCM-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: 0.7723 - Accuracy: 74.72 - Bleu-1: 0.8542 - Bleu-2: 0.8011 - Bleu-3: 0.7571 - Bleu-4: 0.7146 - Meteor: 0.8359 - Rouge-l: 0.8142 - Cider: 3.4768 ## 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 | 148 | 0.8708 | 72.22 | 0.4211 | 0.2916 | 0.2159 | 0.1619 | 0.3291 | 0.3586 | 0.3669 | | No log | 2.0 | 296 | 0.7341 | 72.27 | 0.4540 | 0.3400 | 0.2660 | 0.2165 | 0.3409 | 0.3912 | 0.9591 | | No log | 3.0 | 444 | 0.5835 | 73.39 | 0.8091 | 0.7497 | 0.7010 | 0.6580 | 0.7778 | 0.7731 | 3.1833 | | No log | 4.0 | 592 | 0.5842 | 73.43 | 0.8425 | 0.7781 | 0.7243 | 0.6759 | 0.8107 | 0.7959 | 3.3423 | | No log | 5.0 | 740 | 0.6087 | 72.81 | 0.8297 | 0.7712 | 0.7224 | 0.6793 | 0.8108 | 0.7996 | 3.3063 | | No log | 6.0 | 888 | 0.6218 | 74.11 | 0.8368 | 0.7832 | 0.7366 | 0.6943 | 0.8268 | 0.8166 | 3.4069 | | 0.6500 | 7.0 | 1036 | 0.6235 | 74.98 | 0.8882 | 0.8454 | 0.8049 | 0.7684 | 0.8426 | 0.8398 | 3.6889 | | 0.6500 | 8.0 | 1184 | 0.6569 | 73.84 | 0.8362 | 0.7828 | 0.7366 | 0.6927 | 0.8170 | 0.8073 | 3.4078 | | 0.6500 | 9.0 | 1332 | 0.6680 | 74.68 | 0.8602 | 0.8159 | 0.7755 | 0.7382 | 0.8410 | 0.8288 | 3.5358 | | 0.6500 | 10.0 | 1480 | 0.6681 | 74.36 | 0.8586 | 0.8006 | 0.7512 | 0.7072 | 0.8380 | 0.8178 | 3.4580 | | 0.6500 | 11.0 | 1628 | 0.6793 | 74.5 | 0.8436 | 0.7810 | 0.7303 | 0.6836 | 0.8207 | 0.8042 | 3.4041 | | 0.6500 | 12.0 | 1776 | 0.7174 | 74.67 | 0.8538 | 0.7997 | 0.7542 | 0.7126 | 0.8395 | 0.8242 | 3.4757 | | 0.6500 | 13.0 | 1924 | 0.7296 | 73.88 | 0.8275 | 0.7718 | 0.7216 | 0.6750 | 0.8194 | 0.8010 | 3.4220 | | 0.2971 | 14.0 | 2072 | 0.7463 | 74.55 | 0.8506 | 0.7966 | 0.7507 | 0.7072 | 0.8293 | 0.8148 | 3.4686 | | 0.2971 | 15.0 | 2220 | 0.7314 | 74.73 | 0.8516 | 0.7928 | 0.7409 | 0.6905 | 0.8305 | 0.8164 | 3.5680 | | 0.2971 | 16.0 | 2368 | 0.7657 | 74.19 | 0.8514 | 0.8010 | 0.7539 | 0.7081 | 0.8363 | 0.8183 | 3.4412 | | 0.2971 | 17.0 | 2516 | 0.7723 | 74.72 | 0.8542 | 0.8011 | 0.7571 | 0.7146 | 0.8359 | 0.8142 | 3.4768 | ### Framework versions - Transformers 5.12.1 - Pytorch 2.12.1+cu130 - Datasets 5.0.0 - Tokenizers 0.22.2