FastViT model for use with Autodistill
Project description
Autodistill FastViT Module
This repository contains the code supporting the FastViT base model for use with Autodistill.
FastViT, developed by Apple, is a classification model that supports zero-shot classification.
Read the full Autodistill documentation.
Read the FastViT Autodistill documentation.
Installation
To use FastViT with autodistill, you need to install the following dependency:
pip3 install autodistill-fastvit
Quickstart
FastViT works using the ImageNet-1k class list. This class list is available in the FASTVIT_IMAGENET_1K_CLASSES
variable.
You can provide classes from the list to retrieve predictions for a specific class in the list. You can also provide a custom ontology to map classes from the list to your own classes.
from autodistill_fastvit import FastViT, FASTVIT_IMAGENET_1K_CLASSES
from autodistill.detection import CaptionOntology
# zero shot with no prompts
base_model = FastViT(None)
# zero shot with prompts from FASTVIT_IMAGENET_1K_CLASSES
base_model = FastViT(
ontology=CaptionOntology(
{
"coffeemaker": "coffeemaker",
"ice cream": "ice cream"
}
)
)
predictions = base_model.predict("./example.png")
labels = [FASTVIT_IMAGENET_1K_CLASSES[i] for i in predictions.class_id.tolist()]
print(labels)
License
See LICENSE for the model license.
🏆 Contributing
We love your input! Please see the core Autodistill contributing guide to get started. Thank you 🙏 to all our contributors!
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for autodistill_fastvit-0.1.2.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2a661d941e802dce473774abd8cdcb413c43a88426578c008d4e7ef0d48060b9 |
|
MD5 | ad773957833e77f82830f94181ed3f82 |
|
BLAKE2b-256 | bb0af21e7f0ed4379e1a74ca27dcfb0a097acb48e939408c31b5bb5c927f676d |
Hashes for autodistill_fastvit-0.1.2-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f478c93ee934dfd07b4542fb3aec43d9f765766d40f061debb7e36afab9cbbdf |
|
MD5 | 5b8ee26ed012733cfd2f9af3e372a0ec |
|
BLAKE2b-256 | d5bd39cd9567c3212195f3f46eaa0bba0da31dce1acfb520d4c8fc6c933a0fd7 |