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Vit Resnet Mistral RSICD With 3 Captioning

This model is a fine-tuned version of []( on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.8332 Accuracy: 81.61 Bleu-1: 0.6172 Bleu-2: 0.4856 Bleu-3: 0.3941 Bleu-4: 0.3277...

Fontehuggingface.co/swadhindas324/vit-resnet-mistral-RSICD-with-3-captioning/tree/6081f110ed76f3ae447155216e723d71cf0f83d2
TipoCLIP visionVersãomodelPublicador@swadhindas324Cnota

Fonte do modelo

Tipo
CLIP vision
Tamanho do arquivo
1,12 GB
Versão
vmodel
Atualizado
4 de out. de 2026
Publicador
@swadhindas324
Downloads
3
Tarefas
Legendas
Fonte
Hugging Face

Descrição da fonte

vit-resnet-mistral-RSICD-with-3-captioning

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8332
  • Accuracy: 81.61
  • Bleu-1: 0.6172
  • Bleu-2: 0.4856
  • Bleu-3: 0.3941
  • Bleu-4: 0.3277
  • Meteor: 0.5872
  • Rouge-l: 0.5473
  • Cider: 1.7764

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 LossEpochStepValidation LossAccuracyBleu-1Bleu-2Bleu-3Bleu-4MeteorRouge-lCider
No log1.07680.687579.760.46680.30270.21150.15370.40850.39750.4800
0.96132.015360.661779.950.54080.38650.29000.22510.50630.47250.8810
0.46883.023040.620381.410.69670.57420.48190.40760.68300.63622.2115
0.34764.030720.662281.630.67030.54440.45420.38560.65070.60732.1154
0.34765.038400.726680.530.62060.48860.39890.33440.60060.55501.7074
0.23866.046080.769182.470.65870.52820.43520.36550.62620.58961.9587
0.18757.053760.812481.710.62560.49360.40240.33500.59910.55421.7928
0.16428.061440.833281.610.61720.48560.39410.32770.58720.54731.7764

Framework versions

  • Transformers 5.12.1
  • Pytorch 2.12.1+cu130
  • Datasets 5.0.0
  • Tokenizers 0.22.2

Fontes

1 fonte
Hugging FacePrimárioVersão 6081f110ed76f3ae447155216e723d71cf0f83d2
huggingface.co/swadhindas324/vit-resnet-mistral-RSICD-with-3-captioning/tree/6081f110ed76f3ae447155216e723d71cf0f83d2

Verificado 4 de out.

Artefatos de modelo

1 artefato

model.safetensors

safetensors · 1,12 GB · SHA-256 e2f3359cee90…75f9 · Hugging Face

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Trechos de fonte

3 trechos
Readme11 de jul.huggingface.co/swadhindas324/vit-resnet-mistral-RSICD-with-3-captioning

--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-resnet-mistral-RSICD-with-3-captioning results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-resnet-mistral-RSICD-with-3-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: 0.8332 - Accuracy: 81.61 - Bleu-1: 0.6172 - Bleu-2: 0.4856 - Bleu-3: 0.3941 - Bleu-4: 0.3277 - Meteor: 0.5872 - Rouge-l: 0.5473 - Cider: 1.7764 ## 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 | 0.6875 | 79.76 | 0.4668 | 0.3027 | 0.2115 | 0.1537 | 0.4085 | 0.3975 | 0.4800 | | 0.9613 | 2.0 | 1536 | 0.6617 | 79.95 | 0.5408 | 0.3865 | 0.2900 | 0.2251 | 0.5063 | 0.4725 | 0.8810 | | 0.4688 | 3.0 | 2304 | 0.6203 | 81.41 | 0.6967 | 0.5742 | 0.4819 | 0.4076 | 0.6830 | 0.6362 | 2.2115 | | 0.3476 | 4.0 | 3072 | 0.6622 | 81.63 | 0.6703 | 0.5444 | 0.4542 | 0.3856 | 0.6507 | 0.6073 | 2.1154 | | 0.3476 | 5.0 | 3840 | 0.7266 | 80.53 | 0.6206 | 0.4886 | 0.3989 | 0.3344 | 0.6006 | 0.5550 | 1.7074 | | 0.2386 | 6.0 | 4608 | 0.7691 | 82.47 | 0.6587 | 0.5282 | 0.4352 | 0.3655 | 0.6262 | 0.5896 | 1.9587 | | 0.1875 | 7.0 | 5376 | 0.8124 | 81.71 | 0.6256 | 0.4936 | 0.4024 | 0.3350 | 0.5991 | 0.5542 | 1.7928 | | 0.1642 | 8.0 | 6144 | 0.8332 | 81.61 | 0.6172 | 0.4856 | 0.3941 | 0.3277 | 0.5872 |...

Note4 de out.huggingface.co/swadhindas324/vit-resnet-mistral-RSICD-with-3-captioning

Source context: 3 downloads · 0 likes · Library transformers · Repo swadhindas324/vit-resnet-mistral-RSICD-with-3-captioning

Note18 de jul.huggingface.co/swadhindas324/vit-resnet-mistral-RSICD-with-3-captioning

Source context: 72 downloads · 0 likes · Library transformers · Repo swadhindas324/vit-resnet-mistral-RSICD-with-3-captioning