Instructions to use YukiTashiro/medsiglip-448-ft-en-hyper-kvasir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YukiTashiro/medsiglip-448-ft-en-hyper-kvasir with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="YukiTashiro/medsiglip-448-ft-en-hyper-kvasir") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("YukiTashiro/medsiglip-448-ft-en-hyper-kvasir") model = AutoModelForZeroShotImageClassification.from_pretrained("YukiTashiro/medsiglip-448-ft-en-hyper-kvasir") - Notebooks
- Google Colab
- Kaggle
medsiglip-448-ft-en-20250908_232210
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2445
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
- 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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.4691 | 0.6281 | 500 | 1.2445 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.22.0
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