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--- |
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library_name: transformers |
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tags: |
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- medical |
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license: bsd-3-clause |
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language: |
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- en |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** Umar Igan |
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- **Model type:** VLM |
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- **Language(s) (NLP):** English |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** Salesforce/blip-image-captioning-base |
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### Model Sources [optional] |
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- **Repository:** [More Information Needed] |
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## Uses |
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This is a fine-tuned VLM on chest xray medicald dataset, the result shouldn't be used as an advice!! |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Example usage: |
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```python |
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from transformers import BlipForConditionalGeneration, AutoProcessor |
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model = BlipForConditionalGeneration.from_pretrained("umarigan/blip-image-captioning-base-chestxray-finetuned").to(device) |
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processor = AutoProcessor.from_pretrained("umarigan/blip-image-captioning-base-chestxray-finetuned") |
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inputs = processor(images=image, return_tensors="pt").to(device) |
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pixel_values = inputs.pixel_values |
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generated_ids = model.generate(pixel_values=pixel_values, max_length=50) |
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generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(generated_caption) |
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``` |
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### Training Data |
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https://huggingface.co/datasets/Shrey-1329/cxiu_hf_dataset |
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#### Training Hyperparameters |
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- lr: 5e-5 |
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- Epoch: 10 |
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- Dataset size: 1k |
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#### Summary |
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A simple blip fine-tuned model on medical imaging |
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## Environmental Impact |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** GPU |
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- **Hours used:** 1 |
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- **Cloud Provider:** Google |
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- **Compute Region:** Frankfurt |
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- **Carbon Emitted:** |
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### Compute Infrastructure |
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Google Colab L4 GPU |
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#### Hardware |
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Google Colab L4 GPU |
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## Model Card Contact |
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Umar Igan |
