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medsiglip_finetuned
dokster/medsiglip_finetuned
medsiglip_finetuned is a zero-shot image classification model from dokster. Use it for the zero-shot image classification task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Zero-Shot Image Classification
- Library
- transformers
- License
- other
- Model type
- siglip
- Access
- Public
- Created
- Feb 6, 2026
- Updated
- Feb 7, 2026
- SHA
- 3a0de0e2
Base models
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. -->
medsiglip_finetuned
This model is a fine-tuned version of google/medsiglip-448 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.7197
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- 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: cosine
- lr_scheduler_warmup_steps: 5
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.9384 | 0.7080 | 10 | 3.0041 |
| 2.9760 | 1.3540 | 20 | 2.8563 |
| 2.7202 | 2.0 | 30 | 2.7197 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2
- Task
- Zero-Shot Image Classification
- Library
- transformers
- Type
- siglip
- License
- other
- Created
- Feb 6, 2026
- Updated
- Feb 7, 2026