---
library_name: transformers
license: other
base_model: google/medsiglip-448
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: medsiglip-448-ft-crc100k
  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. -->

# medsiglip-448-ft-crc100k

This model is a fine-tuned version of [google/medsiglip-448](https://huggingface.co/google/medsiglip-448) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2713

## 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 adamw_torch 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.3465        | 0.3556 | 50   | 1.3722          |
| 1.3101        | 0.7111 | 100  | 1.3356          |
| 1.3028        | 1.064  | 150  | 1.2855          |
| 1.2389        | 1.4196 | 200  | 1.3003          |
| 1.2522        | 1.7751 | 250  | 1.2713          |


### Framework versions

- Transformers 4.55.4
- Pytorch 2.5.1+cu121
- Datasets 4.0.0
- Tokenizers 0.21.0