Image-to-Text
Transformers
Safetensors
vision-encoder-decoder
image-text-to-text
Generated from Trainer
Instructions to use fatehmujtaba/image-caption-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fatehmujtaba/image-caption-generator 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="fatehmujtaba/image-caption-generator")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("fatehmujtaba/image-caption-generator") model = AutoModelForImageTextToText.from_pretrained("fatehmujtaba/image-caption-generator") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: image-caption-generator | |
| results: [] | |
| license: mit | |
| pipeline_tag: image-to-text | |
| <!-- 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. --> | |
| # image-caption-generator | |
| This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3393 | |
| ## 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: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | No log | 1.0 | 203 | 0.4971 | | |
| | No log | 2.0 | 406 | 0.4143 | | |
| | 0.6123 | 3.0 | 609 | 0.3731 | | |
| | 0.6123 | 4.0 | 812 | 0.3481 | | |
| | 0.4078 | 5.0 | 1015 | 0.3393 | | |
| ### Framework versions | |
| - Transformers 4.36.2 | |
| - Pytorch 2.0.0 | |
| - Datasets 2.1.0 | |
| - Tokenizers 0.15.0 |