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Vit Resnet Mistral RSICD With 4 Captioning

This model is a fine-tuned version of []( 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...

Quellehuggingface.co/swadhindas324/vit-resnet-mistral-RSICD-with-4-captioning/tree/03297c02099a10e75e76cb27662789af81c3f3bb
ArtCLIP visionVersionmodelVerleger@swadhindas324CBewertung

Modellquelle

Art
CLIP vision
Dateigröße
1,12 GB
Version
vmodel
Aktualisiert
23. Okt. 2026
Verleger
@swadhindas324
Downloads
2
Quelle
Hugging Face

Quellenbeschreibung

vit-resnet-mistral-RSICD-with-4-captioning

This model is a fine-tuned version of 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 LossEpochStepValidation LossAccuracyBleu-1Bleu-2Bleu-3Bleu-4MeteorRouge-lCider
No log1.07680.689280.020.46680.30270.21150.15370.40850.39750.4800
0.96212.015360.662680.020.54740.39690.30250.23740.52250.48510.9514
0.46853.023040.625781.470.69520.57030.47700.40350.68080.63182.1988
0.34684.030720.676681.60.67920.54940.45530.38200.66110.61492.0585
0.34685.038400.735680.720.63860.50600.41400.34670.61890.56521.8487
0.23986.046080.780681.960.64550.51020.41750.34930.61180.58011.8529
0.18957.053760.833081.520.62660.49380.40000.33140.60790.56001.7289
0.16648.061440.850081.620.60710.47050.37690.30910.56470.53261.6445

Framework versions

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

Quellen

1 Quelle
Hugging FacePrimärVersion 03297c02099a10e75e76cb27662789af81c3f3bb
huggingface.co/swadhindas324/vit-resnet-mistral-RSICD-with-4-captioning/tree/03297c02099a10e75e76cb27662789af81c3f3bb

Verifiziert 23. Okt.

Modellartefakte

1 Artefakt

model.safetensors

safetensors · 1,12 GB · SHA-256 acf9a4499b0b…c5ce · Hugging Face

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Quellenauszüge

3 Auszüge
Readme11. Julihuggingface.co/swadhindas324/vit-resnet-mistral-RSICD-with-4-captioning

--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-resnet-mistral-RSICD-with-4-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-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 |...

Note23. Okt.huggingface.co/swadhindas324/vit-resnet-mistral-RSICD-with-4-captioning

Source context: 2 downloads · 0 likes · Library transformers · Repo swadhindas324/vit-resnet-mistral-RSICD-with-4-captioning

Note6. Aug.huggingface.co/swadhindas324/vit-resnet-mistral-RSICD-with-4-captioning

Source context: 11 downloads · 0 likes · Library transformers · Repo swadhindas324/vit-resnet-mistral-RSICD-with-4-captioning