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Vit Convnext Mistral NWPU Without Captioning

This model is a fine-tuned version of []( on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.7152 Accuracy: 86.03 Bleu-1: 0.8517 Bleu-2: 0.7622 Bleu-3: 0.6907 Bleu-4: 0.6330...

Quellehuggingface.co/swadhindas324/vit-convnext-Mistral-NWPU-without-captioning/tree/c9fc9e52c524f9e92d41983fe1577d52fdda4315
ArtCLIP visionVersionmodelBBewertung

Modellquelle

Art
CLIP vision
Dateigröße
1,63 GB
Version
vmodel
Aktualisiert
3. Okt. 2026
Downloads
4
Aufgaben
Beschriftung
Quelle
Hugging Face

Quellenbeschreibung

vit-convnext-Mistral-NWPU-without-captioning

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

  • Loss: 0.7152
  • Accuracy: 86.03
  • Bleu-1: 0.8517
  • Bleu-2: 0.7622
  • Bleu-3: 0.6907
  • Bleu-4: 0.6330
  • Meteor: 0.7662
  • Rouge-l: 0.7519
  • Cider: 1.7528

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
0.91271.022150.866384.260.54140.37510.26080.17400.42770.39310.1700
0.70812.044300.657384.620.82060.71510.63300.56700.71870.70531.6150
0.61603.066450.617684.80.82630.72770.64920.58550.74640.73081.6939
0.56954.088600.604985.460.85770.77200.70110.64210.76900.75681.7658
0.53645.0110750.599285.630.86320.77590.70520.64710.77280.76181.7980
0.50726.0132900.602685.680.85530.76600.69280.63340.77910.76091.8060
0.49677.0155050.618785.970.85450.76700.69640.63950.78010.76101.8101
0.47508.0177200.629886.110.85680.77040.70010.64270.77000.76061.8042
0.45379.0199350.637186.130.86050.77330.69960.63890.78200.76611.7836
0.435410.0221500.656486.120.85620.76890.69750.63940.76880.75841.8225
0.417311.0243650.667486.180.86030.77200.70070.64270.78090.76591.8056
0.400412.0265800.688286.10.85400.76440.69270.63520.76460.75431.7460
0.413513.0287950.696886.070.84910.75930.68630.62740.76620.75031.7297
0.394214.0310100.715286.030.85170.76220.69070.63300.76620.75191.7528

Framework versions

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

Quellen

1 Quelle
Hugging FacePrimärVersion c9fc9e52c524f9e92d41983fe1577d52fdda4315
huggingface.co/swadhindas324/vit-convnext-Mistral-NWPU-without-captioning/tree/c9fc9e52c524f9e92d41983fe1577d52fdda4315

Verifiziert 3. Okt.

Modellartefakte

1 Artefakt

model.safetensors

safetensors · 1,63 GB · SHA-256 d66d88aec1c4…13b3 · Hugging Face

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Quellenauszüge

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Readme17. Julihuggingface.co/swadhindas324/vit-convnext-Mistral-NWPU-without-captioning

--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-convnext-Mistral-NWPU-without-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-convnext-Mistral-NWPU-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: 0.7152 - Accuracy: 86.03 - Bleu-1: 0.8517 - Bleu-2: 0.7622 - Bleu-3: 0.6907 - Bleu-4: 0.6330 - Meteor: 0.7662 - Rouge-l: 0.7519 - Cider: 1.7528 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:------:|:-------:|:------:| | 0.9127 | 1.0 | 2215 | 0.8663 | 84.26 | 0.5414 | 0.3751 | 0.2608 | 0.1740 | 0.4277 | 0.3931 | 0.1700 | | 0.7081 | 2.0 | 4430 | 0.6573 | 84.62 | 0.8206 | 0.7151 | 0.6330 | 0.5670 | 0.7187 | 0.7053 | 1.6150 | | 0.6160 | 3.0 | 6645 | 0.6176 | 84.8 | 0.8263 | 0.7277 | 0.6492 | 0.5855 | 0.7464 | 0.7308 | 1.6939 | | 0.5695 | 4.0 | 8860 | 0.6049 | 85.46 | 0.8577 | 0.7720 | 0.7011 | 0.6421 | 0.7690 | 0.7568 | 1.7658 | | 0.5364 | 5.0 | 11075 | 0.5992 | 85.63 | 0.8632 | 0.7759 | 0.7052 | 0.6471 | 0.7728 | 0.7618 | 1.7980 | | 0.5072 | 6.0 | 13290 | 0.6026 | 85.68 | 0.8553 | 0.7660 | 0.6928 | 0.6334 | 0.7791 | 0.7609 | 1.8060 | | 0.4967 | 7.0 | 15505 | 0.6187 | 85.97 | 0.8545 | 0.7670 | 0.6964 | 0.6395 | 0.7801 | 0.7610 | 1.8101 | | 0.4750 | 8.0 | 17720 | 0.6298 | 86.11 | 0.8568 | 0.7704 | 0.7001 | 0.6427 |...

Note3. Okt.huggingface.co/swadhindas324/vit-convnext-Mistral-NWPU-without-captioning

Source context: 4 downloads · 0 likes · Library transformers · Repo swadhindas324/vit-convnext-Mistral-NWPU-without-captioning

Note17. Julihuggingface.co/swadhindas324/vit-convnext-Mistral-NWPU-without-captioning

Source context: 233 downloads · 0 likes · Library transformers · Repo swadhindas324/vit-convnext-Mistral-NWPU-without-captioning