Vit Resnet Mistral UCM With 6 Captioning

--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-resnet-mistral-UCM-with-6-captioning results: [] --- # vit-resnet-mistral-UCM-with-6-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: 0.4134 - Accuracy: 74.75 - Bleu-1: 0.8578 - Bleu-2: 0.8009 - Bleu-3: 0.7539 - Bleu-4: 0.7105 - Meteor: 0.8301 - Rouge-l: 0.8174 - Cider: 3.4426 ## 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 | 0.6525 | 72.44 | 0.4161 | 0.2862 | 0.2100 | 0.1579 | 0.3185 | 0.3524 | 0.3543 | | No log | 2.0 | 296 | 0.4699 | 72.04 | 0.3006 | 0.2063 | 0.1356 | 0.0928 | 0.2190 | 0.2487 | 0.3328 | | No log | 3.0 | 444 | 0.4241 | 69.49 | 0.6099 | 0.5201 | 0.4547 | 0.4062 | 0.5566 | 0.5662 | 1.8487 | | No log | 4.0 | 592 | 0.3574 | 73.82 | 0.8252 | 0.7590 | 0.7044 | 0.6550 | 0.7922 | 0.7774 | 3.1653 | | No log | 5.0 | 740 | 0.3450 | 74.32 | 0.8303 | 0.7708 | 0.7234 | 0.6839 | 0.8120 | 0.7997 | 3.3390 | | No log | 6.0 | 888 | 0.3526 | 73.73 | 0.8354 | 0.7809 | 0.7320 | 0.6873 | 0.8240 | 0.8097 | 3.3430 | | 0.4526 | 7.0 | 1036 | 0.3386 | 75.3 | 0.8675 | 0.8149 | 0.7701 | 0.7265 | 0.8324 | 0.8221 | 3.5520 | | 0.4526 | 8.0 | 1184 | 0.3406 | 74.36 | 0.8483 | 0.7916 | 0.7413 | 0.6939 | 0.8240 | 0.8143 | 3.4696 | | 0.4526 | 9.0 | 1332 | 0.3469 | 75.03 | 0.8722 | 0.8238 | 0.7791 | 0.7372 | 0.8516 | 0.8399 | 3.6168 | | 0.4526 | 10.0 | 1480 | 0.3508 | 74.49 | 0.8306 | 0.7708 | 0.7189 | 0.6695 | 0.8017 | 0.7883 | 3.3304 | | 0.4526 | 11.0 | 1628 | 0.3615 | 73.88 | 0.8335 | 0.7750 | 0.7273 | 0.6856 | 0.8285 | 0.8037 | 3.4000 | | 0.4526 | 12.0 | 1776 | 0.3587 | 74.48 | 0.8575 | 0.8056 | 0.7610 | 0.7200 | 0.8391 | 0.8285 | 3.5049 | | 0.4526 | 13.0 | 1924 | 0.3813 | 73.39 | 0.8329 | 0.7704 | 0.7217 | 0.6773 | 0.8120 | 0.7901 | 3.3758 | | 0.1651 | 14.0 | 2072 | 0.3911 | 74.63 | 0.8251 | 0.7637 | 0.7121 | 0.6630 | 0.7868 | 0.7718 | 3.1622 | | 0.1651 | 15.0 | 2220 | 0.3852 | 75.56 | 0.8487 | 0.7913 | 0.7427 | 0.6978 | 0.8102 | 0.8043 | 3.5665 | | 0.1651 | 16.0 | 2368 | 0.3852 | 74.65 | 0.8612 | 0.8114 | 0.7680 | 0.7275 | 0.8469 | 0.8302 | 3.6155 | | 0.1651 | 17.0 | 2516 | 0.3938 | 74.52 | 0.8553 | 0.7969 | 0.7477 | 0.7016 | 0.8381 | 0.8160 | 3.4153 | | 0.1651 | 18.0 | 2664 | 0.3973 | 74.71 | 0.8535 | 0.7968 | 0.7480 | 0.7030 | 0.8326 | 0.8204 | 3.4378 | | 0.1651 | 19.0 | 2812 | 0.4134 | 74.75 | 0.8578 | 0.8009 | 0.7539 | 0.7105 | 0.8301 | 0.8174 | 3.4426 | ### Framework versions - Transformers 5.12.1 - Pytorch 2.12.1+cu130 - Datasets 5.0.0 - Tokenizers 0.22.2