Instructions to use Sigurdur/isl-img2text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sigurdur/isl-img2text with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="Sigurdur/isl-img2text")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Sigurdur/isl-img2text") model = AutoModelForImageTextToText.from_pretrained("Sigurdur/isl-img2text") - Notebooks
- Google Colab
- Kaggle
isl-img2text
Author: Sigurdur Haukur Birgisson
This model is a fine-tuned version of microsoft/git-base on Sigurdur/isl-image-captioning. It achieves the following results on the evaluation set:
- eval_loss: 0.0983
- eval_wer_score: 0.7295
- eval_runtime: 20.5346
- eval_samples_per_second: 7.792
- eval_steps_per_second: 0.974
- epoch: 15.0
- step: 150
It appears that the model heavilly overfitted to the dataset. Also, something I failed to consider was that the base model can't write any Icelandic characters and was thus not suited for this task. Future works might want to add the capability of writing Icelandic characters to the model.
repo: https://github.com/sigurdurhaukur/isl-img-cap
Model description
More information needed
Intended uses & limitations
Image captioning in Icelandic
Training and evaluation data
Scraped images and their descriptions/captions from the Icelandic wikipedia.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Metrics
| Epoch | Training Loss | Validation Loss | Wer Score |
|---|---|---|---|
| 1 | 10.096300 | 8.690205 | 102.247536 |
| 2 | 8.268200 | 7.655295 | 97.659365 |
| 3 | 7.298000 | 6.679112 | 95.714129 |
| 4 | 6.319800 | 5.673368 | 2.136911 |
| 5 | 5.317500 | 4.656871 | 22.439211 |
| 6 | 4.315600 | 3.667494 | 1.001095 |
| 7 | 3.340000 | 2.722741 | 1.063527 |
| 8 | 2.417700 | 1.852253 | 0.944140 |
| 9 | 1.593900 | 1.136962 | 0.949617 |
| 10 | 0.944900 | 0.638581 | 0.933187 |
| 11 | 0.516200 | 0.355187 | 0.955093 |
| 12 | 0.281600 | 0.215951 | 0.822563 |
| 13 | 0.167500 | 0.148763 | 0.773275 |
| 14 | 0.111700 | 0.116783 | 0.792990 |
| 15 | 0.080800 | 0.098261 | 0.729463 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.0.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for Sigurdur/isl-img2text
Base model
microsoft/git-base