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vit-resnet-Mistral-RSICD-without-captioning
swadhindas324/vit-resnet-Mistral-RSICD-without-captioning
vit-resnet-Mistral-RSICD-without-captioning is a machine learning model from swadhindas324. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- vision-encoder-decoder
- Access
- Public
- Created
- Jul 15, 2026
- Updated
- Jul 15, 2026
- SHA
- 1c3b1d25
Model card
From the Hugging Face model README
<!-- 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-without-captioning
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6545
- Accuracy: 81.45
- Bleu-1: 0.6169
- Bleu-2: 0.4800
- Bleu-3: 0.3863
- Bleu-4: 0.3200
- Meteor: 0.5836
- Rouge-l: 0.5397
- Cider: 1.7724
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 | 1.2887 | 79.76 | 0.4667 | 0.3026 | 0.2114 | 0.1536 | 0.4084 | 0.3974 | 0.4796 |
| 1.5992 | 2.0 | 1536 | 1.2363 | 79.46 | 0.6192 | 0.4797 | 0.3803 | 0.3051 | 0.6187 | 0.5594 | 1.4971 |
| 0.8374 | 3.0 | 2304 | 1.2232 | 81.12 | 0.6701 | 0.5450 | 0.4535 | 0.3812 | 0.6651 | 0.6151 | 2.0863 |
| 0.6004 | 4.0 | 3072 | 1.3637 | 81.23 | 0.6497 | 0.5194 | 0.4264 | 0.3566 | 0.6317 | 0.5895 | 1.9473 |
| 0.6004 | 5.0 | 3840 | 1.4562 | 80.42 | 0.6183 | 0.4829 | 0.3908 | 0.3225 | 0.5940 | 0.5471 | 1.6977 |
| 0.4218 | 6.0 | 4608 | 1.5248 | 81.13 | 0.6281 | 0.5004 | 0.4114 | 0.3448 | 0.6080 | 0.5694 | 1.8229 |
| 0.3469 | 7.0 | 5376 | 1.6350 | 81.47 | 0.6318 | 0.5015 | 0.4077 | 0.3377 | 0.6178 | 0.5670 | 1.7947 |
| 0.3105 | 8.0 | 6144 | 1.6545 | 81.45 | 0.6169 | 0.4800 | 0.3863 | 0.3200 | 0.5836 | 0.5397 | 1.7724 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.12.1+cu130
- Datasets 5.0.0
- Tokenizers 0.22.2
- Library
- transformers
- Type
- vision-encoder-decoder
- Created
- Jul 15, 2026
- Updated
- Jul 15, 2026