Skip to main content

Add your description here

Project description

VLM-RECog

A Python library for zero-shot object detection and segmentation using Vision-Language Models (VLMs), built on Google's Gemini 2.5 Flash model.

Installation

pip install vlm-recog
export GEMINI_API_KEY=<your_api_key>

Requirements

  • Python 3.11+
  • Google Gemini API key (set as GEMINI_API_KEY environment variable)

Quick Start

from PIL import Image
from vlm_recog.detection import detect
from vlm_recog.visualization import draw_detections

# Load an image
image = Image.open("path/to/image.jpg")

# Detect objects by text labels
result = detect(image, ["dog", "bicycle", "person"])

# Visualize results
output_image = draw_detections(image, result)
output_image.show()

Demo

output

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

vlm_recog-0.1.8.tar.gz (1.0 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

vlm_recog-0.1.8-py3-none-any.whl (7.1 kB view details)

Uploaded Python 3

File details

Details for the file vlm_recog-0.1.8.tar.gz.

File metadata

  • Download URL: vlm_recog-0.1.8.tar.gz
  • Upload date:
  • Size: 1.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.5.13

File hashes

Hashes for vlm_recog-0.1.8.tar.gz
Algorithm Hash digest
SHA256 56aba210986e5adf0fc25e613040ca5096bea081fcda4bf184f2646b2b2c2c25
MD5 3fc6df47d2846245ac2430cc51e0f1ca
BLAKE2b-256 8f5cd68a5b87cfb8c1b910c20d8ffd2b33c22887f5bf45ce28df21dee18eb049

See more details on using hashes here.

File details

Details for the file vlm_recog-0.1.8-py3-none-any.whl.

File metadata

  • Download URL: vlm_recog-0.1.8-py3-none-any.whl
  • Upload date:
  • Size: 7.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.5.13

File hashes

Hashes for vlm_recog-0.1.8-py3-none-any.whl
Algorithm Hash digest
SHA256 52f860966184d98914b334c28ccd7ba7055ac449b926eef888997b15916ab81d
MD5 69411df41c2fbc51307962f9145f4fd1
BLAKE2b-256 0009e534638f5a93ebb7d7f2e9d698f8bbf8d4646085443e397e75a2ea39c942

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page