---
library_name: transformers
license: bsd-3-clause
base_model: Salesforce/blip-image-captioning-large
tags:
- generated_from_trainer
model-index:
- name: blip-finetuned-gradcam-optimized
  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. -->

# blip-finetuned-gradcam-optimized

This model is a fine-tuned version of [Salesforce/blip-image-captioning-large](https://huggingface.co/Salesforce/blip-image-captioning-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.6748

## 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: 2e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 9.8985        | 0.4444  | 10   | 9.8608          |
| 9.7965        | 0.8889  | 20   | 9.7389          |
| 9.7009        | 1.3111  | 30   | 9.6374          |
| 9.539         | 1.7556  | 40   | 9.5101          |
| 8.2904        | 2.1778  | 50   | 9.1547          |
| 8.7882        | 2.6222  | 60   | 8.7516          |
| 7.6249        | 3.0444  | 70   | 8.4009          |
| 8.2185        | 3.4889  | 80   | 8.1483          |
| 8.0129        | 3.9333  | 90   | 7.9948          |
| 7.8556        | 4.3556  | 100  | 7.8385          |
| 7.7265        | 4.8     | 110  | 7.6990          |
| 7.6183        | 5.2222  | 120  | 7.5757          |
| 7.5           | 5.6667  | 130  | 7.4770          |
| 6.6711        | 6.0889  | 140  | 7.3812          |
| 7.3249        | 6.5333  | 150  | 7.3007          |
| 7.2369        | 6.9778  | 160  | 7.2224          |
| 7.1824        | 7.4     | 170  | 7.1548          |
| 7.1173        | 7.8444  | 180  | 7.0876          |
| 7.0568        | 8.2667  | 190  | 7.0291          |
| 7.0033        | 8.7111  | 200  | 6.9772          |
| 6.2525        | 9.1333  | 210  | 6.9274          |
| 6.9084        | 9.5778  | 220  | 6.8845          |
| 6.1803        | 10.0    | 230  | 6.8465          |
| 6.8372        | 10.4444 | 240  | 6.8137          |
| 6.807         | 10.8889 | 250  | 6.7842          |
| 6.7844        | 11.3111 | 260  | 6.7578          |
| 6.7499        | 11.7556 | 270  | 6.7369          |
| 6.0823        | 12.1778 | 280  | 6.7185          |
| 6.7188        | 12.6222 | 290  | 6.7038          |
| 6.036         | 13.0444 | 300  | 6.6918          |
| 6.7017        | 13.4889 | 310  | 6.6834          |
| 6.7003        | 13.9333 | 320  | 6.6776          |
| 6.6899        | 14.3556 | 330  | 6.6748          |


### Framework versions

- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1