Vit Resnet Mistral RSICD With 4 Captioning | Sweet Tea Studio
Resources / Vit Resnet Mistral RSICD With 4 Captioning Vit Resnet Mistral RSICD With 4 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: 0.8500 Accuracy: 81.62 Bleu-1: 0.6071 Bleu-2: 0.4705 Bleu-3: 0.3769 Bleu-4: 0.3091 Meteor: 0.5647 Rouge-l: 0.5326 Cider: 1.6445 Model description
Verified source
Kind Other Version v03297c02099a10e75e76cb27662789af81c3f3bb Publisher @swadhindas324 C grade Model source
Kind Other
Version v03297c02099a10e75e76cb27662789af81c3f3bb
Tasks Captioning
Source Hugging Face --- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-resnet-mistral-RSICD-with-4-captioning results: [] --- # vit-resnet-mistral-RSICD-with-4-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: 0.8500 - Accuracy: 81.62 - Bleu-1: 0.6071 - Bleu-2: 0.4705 - Bleu-3: 0.3769 - Bleu-4: 0.3091 - Meteor: 0.5647 - Rouge-l: 0.5326 - Cider: 1.6445 ## 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 | 768 | 0.6892 | 80.02 | 0.4668 | 0.3027 | 0.2115 | 0.1537 | 0.4085 | 0.3975 | 0.4800 | | 0.9621 | 2.0 | 1536 | 0.6626 | 80.02 | 0.5474 | 0.3969 | 0.3025 | 0.2374 | 0.5225 | 0.4851 | 0.9514 | | 0.4685 | 3.0 | 2304 | 0.6257 | 81.47 | 0.6952 | 0.5703 | 0.4770 | 0.4035 | 0.6808 | 0.6318 | 2.1988 | | 0.3468 | 4.0 | 3072 | 0.6766 | 81.6 | 0.6792 | 0.5494 | 0.4553 | 0.3820 | 0.6611 | 0.6149 | 2.0585 | | 0.3468 | 5.0 | 3840 | 0.7356 | 80.72 | 0.6386 | 0.5060 | 0.4140 | 0.3467 | 0.6189 | 0.5652 | 1.8487 | | 0.2398 | 6.0 | 4608 | 0.7806 | 81.96 | 0.6455 | 0.5102 | 0.4175 | 0.3493 | 0.6118 | 0.5801 | 1.8529 | | 0.1895 | 7.0 | 5376 | 0.8330 | 81.52 | 0.6266 | 0.4938 | 0.4000 | 0.3314 | 0.6079 | 0.5600 | 1.7289 | | 0.1664 | 8.0 | 6144 | 0.8500 | 81.62 | 0.6071 | 0.4705 | 0.3769 | 0.3091 | 0.5647 | 0.5326 | 1.6445 | ### Framework versions - Transformers 5.12.1 - Pytorch 2.12.1+cu130 - Datasets 5.0.0 - Tokenizers 0.22.2
Sources & provenance
1 active source Source evidence
3 excerpts 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: 0.8500 Accuracy: 81.62 Bleu-1: 0.6071 Bleu-2: 0.4705 Bleu-3: 0.3769 Bleu-4: 0.3091 Meteor: 0.5647 Rouge-l:…
libraryname: transformers tags: generatedfromtrainer metrics: accuracy model-index: name: vit-resnet-mistral-RSICD-with-4-captioning results: []
swadhindas324/vit-resnet-mistral-RSICD-with-4-captioning
