File size: 1,392 Bytes
1c9b44b e0f4d55 1c9b44b 24fb2c3 1c9b44b e0f4d55 1c9b44b e0f4d55 1771de5 e0f4d55 1771de5 e0f4d55 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
---
title: "NOVIC: Unconstrained Open Vocabulary Image Classification"
short_description: "Prompt-free open vocabulary classification of any image"
license: gpl-3.0
emoji: 🖼️
colorFrom: green
colorTo: yellow
sdk: gradio
python_version: "3.10"
sdk_version: "5.35.0"
app_file: app.py
fullWidth: true
header: default
tags:
- prompt-free
- open vocabulary
- image classification
- computer vision
- identification
- generative
pinned: true
disable_embedding: false
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
To run the Gradio space locally, you first create a sandboxed Python 3.10 environment:
```bash
python -m venv .venv # <-- Python 3.10
source .venv/bin/activate
# OR...
conda create -y -n novic_spaces python=3.10
conda activate novic_spaces
```
Then install `gradio` and any further requirements (**Note:** A pip-based install of `torch` is actually numerically different than a conda-based install of `pytorch`, which matters for the conda-based pretrained models, but for Hugging Face spaces we do not really have another choice):
```bash
pip install gradio==5.35.0 -r requirements.txt
```
Now run the Gradio application:
```bash
./app.py
# OR...
gradio app.py
```
Open in a browser the URL that the application is running on (as shown in the CLI output), e.g. [http://127.0.0.1:7860](http://127.0.0.1:7860).
|