| Name | autodistill-bioclip JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | |
| Summary | BioCLIP model for use with Autodistill |
| upload_time | 2024-02-08 09:09:54 |
| maintainer | |
| docs_url | None |
| author | Roboflow |
| requires_python | >=3.7 |
| license | |
| keywords |
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| requirements |
No requirements were recorded.
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<div align="center">
<p>
<a align="center" href="" target="_blank">
<img
width="850"
src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
>
</a>
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# Autodistill BioCLIP Module
This repository contains the code supporting the BioCLIP base model for use with [Autodistill](https://github.com/autodistill/autodistill).
[BioCLIP](https://github.com/Imageomics/BioCLIP) is a CLIP model trained on the [TreeOfLife-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M) dataset, created by the researchers who made BioCLIP. The dataset on which BioCLIP was trained included more than 450,000 classes.
You can use BioCLIP to auto-label natural organisms (i.e. animals, plants) in images for use in training a classification model. You can combine this model with a grounded detection model to identify the exact region in which a given class is present in an image. [Learn more about combining models with Autodistill](https://docs.autodistill.com/utilities/combine-models/).
Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).
Read the [BioCLIP Autodistill documentation](https://autodistill.github.io/autodistill/base_models/bioclip/).
## Installation
To use BioCLIP with autodistill, you need to install the following dependency:
```bash
pip3 install autodistill-bioclip
```
## Quickstart
```python
from autodistill_bioclip import BioCLIP
# define an ontology to map class names to our BioCLIP prompt
# the ontology dictionary has the format {caption: class}
# where caption is the prompt sent to the base model, and class is the label that will
# be saved for that caption in the generated annotations
# then, load the model
classes = ["arabica", "robusta"]
base_model = BioCLIP(
ontology=CaptionOntology(
{
item: item for item in classes
}
)
)
results = base_model.predict("../arabica.jpeg")
top = results.get_top_k(1)
top_class = classes[top[0][0]]
print(f"Predicted class: {top_class}")
```
## License
This project is licensed under an [MIT license](LICENSE).
The underlying [BioCLIP model](https://huggingface.co/imageomics/bioclip) is also licensed under an MIT license.
## 🏆 Contributing
We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
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"description": "<div align=\"center\">\n <p>\n <a align=\"center\" href=\"\" target=\"_blank\">\n <img\n width=\"850\"\n src=\"https://media.roboflow.com/open-source/autodistill/autodistill-banner.png\"\n >\n </a>\n </p>\n</div>\n\n# Autodistill BioCLIP Module\n\nThis repository contains the code supporting the BioCLIP base model for use with [Autodistill](https://github.com/autodistill/autodistill).\n\n[BioCLIP](https://github.com/Imageomics/BioCLIP) is a CLIP model trained on the [TreeOfLife-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M) dataset, created by the researchers who made BioCLIP. The dataset on which BioCLIP was trained included more than 450,000 classes.\n\nYou can use BioCLIP to auto-label natural organisms (i.e. animals, plants) in images for use in training a classification model. You can combine this model with a grounded detection model to identify the exact region in which a given class is present in an image. [Learn more about combining models with Autodistill](https://docs.autodistill.com/utilities/combine-models/).\n\nRead the full [Autodistill documentation](https://autodistill.github.io/autodistill/).\n\nRead the [BioCLIP Autodistill documentation](https://autodistill.github.io/autodistill/base_models/bioclip/).\n\n## Installation\n\nTo use BioCLIP with autodistill, you need to install the following dependency:\n\n\n```bash\npip3 install autodistill-bioclip\n```\n\n## Quickstart\n\n```python\nfrom autodistill_bioclip import BioCLIP\n\n# define an ontology to map class names to our BioCLIP prompt\n# the ontology dictionary has the format {caption: class}\n# where caption is the prompt sent to the base model, and class is the label that will\n# be saved for that caption in the generated annotations\n# then, load the model\nclasses = [\"arabica\", \"robusta\"]\n\nbase_model = BioCLIP(\n ontology=CaptionOntology(\n {\n item: item for item in classes\n }\n )\n)\n\nresults = base_model.predict(\"../arabica.jpeg\")\n\ntop = results.get_top_k(1)\ntop_class = classes[top[0][0]]\n\nprint(f\"Predicted class: {top_class}\")\n```\n\n\n## License\n\nThis project is licensed under an [MIT license](LICENSE).\n\nThe underlying [BioCLIP model](https://huggingface.co/imageomics/bioclip) is also licensed under an MIT license.\n\n## \ud83c\udfc6 Contributing\n\nWe love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you \ud83d\ude4f to all our contributors!\n",
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