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---
library_name: transformers
license: other
base_model: google/medsiglip-448
tags:
- generated_from_trainer
model-index:
- name: medsiglip-448-ft-tb-screening
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-tb-screening
This model is a fine-tuned version of [google/medsiglip-448](https://huggingface.co/google/medsiglip-448) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6822
## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- 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: 10
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.5064 | 0.2140 | 25 | 2.4667 |
| 1.9406 | 0.4280 | 50 | 2.5449 |
| 1.9175 | 0.6421 | 75 | 2.5669 |
| 1.8659 | 0.8561 | 100 | 2.7958 |
| 1.9603 | 1.0685 | 125 | 2.6281 |
| 1.8811 | 1.2825 | 150 | 2.5601 |
| 1.8955 | 1.4965 | 175 | 2.5833 |
| 1.8982 | 1.7105 | 200 | 2.6373 |
| 1.825 | 1.9246 | 225 | 2.6426 |
| 1.88 | 2.1370 | 250 | 2.8641 |
| 1.851 | 2.3510 | 275 | 2.6415 |
| 1.8619 | 2.5650 | 300 | 2.5749 |
| 1.8365 | 2.7790 | 325 | 2.6245 |
| 1.8783 | 2.9930 | 350 | 2.5929 |
| 1.8693 | 3.2055 | 375 | 2.5986 |
| 1.8605 | 3.4195 | 400 | 2.6601 |
| 1.8759 | 3.6335 | 425 | 2.5904 |
| 1.8731 | 3.8475 | 450 | 2.6054 |
| 1.8536 | 4.0599 | 475 | 2.6441 |
| 1.8509 | 4.2739 | 500 | 2.6678 |
| 1.8609 | 4.4880 | 525 | 2.6946 |
| 1.8478 | 4.7020 | 550 | 2.6386 |
| 1.8492 | 4.9160 | 575 | 2.6799 |
| 1.8549 | 5.1284 | 600 | 2.6355 |
| 1.88 | 5.3424 | 625 | 2.7021 |
| 1.8569 | 5.5564 | 650 | 2.6380 |
| 1.862 | 5.7705 | 675 | 2.6349 |
| 1.8486 | 5.9845 | 700 | 2.6843 |
| 1.8503 | 6.1969 | 725 | 2.6926 |
| 1.8503 | 6.4109 | 750 | 2.6962 |
| 1.84 | 6.6249 | 775 | 2.6286 |
| 1.8466 | 6.8390 | 800 | 2.6278 |
| 1.8584 | 7.0514 | 825 | 2.6274 |
| 1.8633 | 7.2654 | 850 | 2.6308 |
| 1.8744 | 7.4794 | 875 | 2.6365 |
| 1.8522 | 7.6934 | 900 | 2.6514 |
| 1.8578 | 7.9074 | 925 | 2.6701 |
| 1.8661 | 8.1199 | 950 | 2.6817 |
| 1.8301 | 8.3339 | 975 | 2.6813 |
| 1.8499 | 8.5479 | 1000 | 2.6841 |
| 1.8484 | 8.7619 | 1025 | 2.6832 |
| 1.8815 | 8.9759 | 1050 | 2.6814 |
| 1.8082 | 9.1883 | 1075 | 2.6836 |
| 1.8302 | 9.4024 | 1100 | 2.6839 |
| 1.8822 | 9.6164 | 1125 | 2.6824 |
| 1.8648 | 9.8304 | 1150 | 2.6822 |
### Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
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