---
library_name: transformers
license: other
base_model: google/medsiglip-448
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
model-index:
- name: medsiglip-appendicitis-binary
  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-appendicitis-binary

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: 2.4233
- Accuracy: 0.8143
- F1: 0.8056
- Roc Auc: 0.8840
- Sensitivity: 0.8007
- Specificity: 0.8225

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_bnb_8bit 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: 100
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Accuracy | F1     | Validation Loss | Roc Auc | Sensitivity | Specificity |
|:-------------:|:------:|:----:|:--------:|:------:|:---------------:|:-------:|:-----------:|:-----------:|
| 2.1622        | 0.5253 | 500  | 0.7215   | 0.7215 | 0.6635          | 0.8816  | 0.9502      | 0.5836      |
| 1.2676        | 1.0504 | 1000 | 0.8161   | 0.8071 | 0.6515          | 0.9006  | 0.7976      | 0.8273      |
| 1.1505        | 1.5758 | 1500 | 0.8164   | 0.8026 | 0.7922          | 0.8917  | 0.7345      | 0.8658      |
| 0.6511        | 2.1009 | 2000 | 0.8130   | 0.7991 | 1.0340          | 0.8875  | 0.7314      | 0.8622      |
| 0.8176        | 2.6262 | 2500 | 0.7980   | 0.7809 | 1.2022          | 0.8752  | 0.6889      | 0.8639      |
| 0.6471        | 3.1513 | 3000 | 0.8291   | 0.8159 | 1.6653          | 0.8943  | 0.7463      | 0.8790      |
| 0.3691        | 3.6766 | 3500 | 0.8076   | 0.8007 | 1.6310          | 0.8846  | 0.8268      | 0.7960      |
| 0.4232        | 4.2017 | 4000 | 0.8079   | 0.7995 | 1.8503          | 0.8832  | 0.8021      | 0.8113      |
| 0.5992        | 4.7271 | 4500 | 0.8151   | 0.7937 | 2.6899          | 0.8863  | 0.6557      | 0.9111      |
| 0.2965        | 5.2522 | 5000 | 0.8225   | 0.8084 | 2.4061          | 0.8953  | 0.7317      | 0.8773      |
| 0.1884        | 5.7775 | 5500 | 0.8157   | 0.7996 | 2.4848          | 0.8841  | 0.7070      | 0.8813      |
| 0.1886        | 6.3026 | 6000 | 0.8083   | 0.7906 | 2.7710          | 0.8766  | 0.6889      | 0.8803      |
| 0.1329        | 6.8279 | 6500 | 2.8092   | 0.7984 | 0.7733          | 0.8743  | 0.6192      | 0.9065      |
| 0.1503        | 7.3530 | 7000 | 3.1173   | 0.8021 | 0.7780          | 0.8720  | 0.6286      | 0.9067      |
| 0.1636        | 7.8784 | 7500 | 2.9567   | 0.7937 | 0.7653          | 0.8740  | 0.5923      | 0.9151      |
| 0.2007        | 8.4035 | 8000 | 2.3409   | 0.8210 | 0.8082          | 0.8828  | 0.7481      | 0.8649      |
| 0.2111        | 8.9288 | 8500 | 2.4233   | 0.8143 | 0.8056          | 0.8840  | 0.8007      | 0.8225      |


### Framework versions

- Transformers 5.15.0
- Pytorch 2.13.0+cu130
- Datasets 5.0.1
- Tokenizers 0.22.2