Library Launched on PyPI

Author: RohitXJ

LICENSE

@@ -0,0 +1,20 @@
+Custom License for fewshotlib
+
+Copyright (c) 2025 Rohit Gomes
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the “Software”), to use,
+copy, modify, and distribute the Software for research and educational purposes only,
+subject to the following conditions:
+
+1. The Software may not be used, copied, modified, or distributed for
+   any commercial purpose without explicit written permission from the author.
+
+2. The above copyright notice and this permission notice shall be included in
+   all copies or substantial portions of the Software.
+
+3. Any use of the Software must give appropriate credit to the author.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND.
+
+For licensing or commercial use inquiries, contact: [email protected]

LICENSE.txt

@@ -0,0 +1,90 @@
+# Few-Shot Classifier Library
+
+A lightweight Python library for image classification using few-shot learning. Given a small number of support images per class, this library classifies query images based on visual similarity using learnable embeddings.
+
+## Features
+
+- No training required — works directly with pretrained backbones
+- Supports multiple popular models: ResNet, MobileNet, EfficientNet, DenseNet, and more
+- Accepts individual images or batches for prediction
+- **Automatically detects image type:** handles RGB and Grayscale images as required by the model
+- Returns class indices or original labels (if provided)
+- Easy integration with PyTorch pipelines
+
+## Installation
+
+Install directly from PyPI:
+
+```
+
+pip install fewshotlib
+
+```
+
+Or, if not yet published, install locally from source:
+
+```
+
+git clone https://github.com/RohitXJ/few-shot-lib.git
+cd fewshotlib
+pip install .
+
+```
+
+## How to Use
+
+```
+
+from fewshotlib import FewShotClassifier
+from PIL import Image
+
+# Load a model checkpoint (must be generated using our model creation utility)
+
+model_path = 'model.pt'
+classifier = FewShotClassifier(model_path)
+
+# Predict from a single image file
+
+result = classifier.predict('test_image.png')
+print(result)
+
+# Predict from a batch of image files
+
+results = classifier.predict(['img1.png', 'img2.png'])
+print(results)
+
+# Predict and return the original class label (if available)
+
+result = classifier.predict('test_image.png')
+print(result['label'])  \# outputs the original label, if present
+
+```
+
+## Notes
+
+- If your `model.pt` file contains labels from the prototype creation step, you will receive the corresponding original label for each prediction.
+- The library automatically handles input images in RGB or Grayscale formats and applies the necessary preprocessing for the chosen backbone model.
+- Supports file paths to images (recommended). For power users, you may load a PIL image and pass it directly, but file path input is default.
+
+## Generate `model.pt`
+
+To create a compatible `model.pt` file for this library, use our companion tool (coming soon):
+
+```
+
+https://github.com/RohitXJ/few-shot-web
+
+```
+
+This tool lets you select a few images per class and generates a `.pt` file storing class prototypes and labels.
+
+## License
+
+This project is licensed under a **Custom Research and Educational Use License**.
+
+You are permitted to use, copy, modify, and distribute this software **only for research and educational purposes**. **Commercial use is strictly prohibited** without prior written permission from the author.
+
+See the [LICENSE](./LICENSE) file for full terms.
+
+For commercial inquiries, contact: [[email protected]](mailto:[email protected])
+

README.md

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+from .fewshotlib import FewShotClassifier

__init__.py

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+[build-system]
+requires = ["setuptools>=61.0.0", "wheel"]
+build-backend = "setuptools.build_meta"

fewshotlib/__init__.py

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+from setuptools import setup, find_packages
+
+with open("README.md", "r", encoding="utf-8") as fh:
+    long_description = fh.read()
+
+setup(
+    name="fewshotlib",
+    version="0.1.1",
+    author="Rohit Gomes",
+    author_email="[email protected]",
+    description="A flexible few-shot classifier toolkit.",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    packages=find_packages(include=["fewshotlib"]),
+    install_requires=[
+        "torch>=1.10.0",
+        "torchvision>=0.11.0",
+        "Pillow",
+    ],
+    python_requires=">=3.8",
+)

fewshotlib.py renamed to fewshotlib/fewshotlib.py

@@ -9,7 +9,7 @@
 def test_model_loading():
     try:
         classifier = FewShotClassifier(model_path=MODEL_PATH)
-        print("✅ Model loaded successfully.")
+        print("Model loaded successfully.")
 
         print(f"Encoder: {type(classifier.encoder)}")
         print(f"Prototypes shape: {classifier.prototypes.shape}")
@@ -19,7 +19,7 @@ def test_model_loading():
             print("No labels found in checkpoint.")
 
     except Exception as e:
-        print(f"❌ Model loading failed: {e}")
+        print(f"Model loading failed: {e}")
 
 if __name__ == "__main__":
     test_model_loading()