coil100-vision-sorting-svm

COIL-100 Object Classification & Sorting Model (Classical ML Pipeline)

Model Description

This repository contains a complete perception pipeline for object classification trained on the COIL-100 dataset, designed to simulate industrial robotic sorting / pick-and-place tasks using hand-crafted vision features and classical machine learning classifiers.

The pipeline demonstrates that — in controlled environments with limited object categories, stable lighting, and single-object-per-image scenarios — carefully engineered features combined with interpretable models can achieve very high accuracy, often rivaling lightweight deep learning approaches while being much more explainable and computationally lightweight.

Key Techniques

• Feature Engineering (OpenCV + scikit-image):

  • Color: HSV histogram (4×4×4 bins → 64 dims)
  • Shape: Hu moments (7 values) + normalized bounding box size & position (4 values)
  • Texture: Local Binary Patterns – uniform (59 bins)
  • Texture statistics: GLCM contrast, homogeneity, energy
  • Gradient structure: Histogram of Oriented Gradients (HOG) with 16×16 pixels/cell

• Dimensionality Reduction (hybrid approach):

  • Supervised feature selection: SelectKBest (ANOVA F-value) → top 500 features
  • Unsupervised compression: PCA preserving 95% variance on the selected features → Final feature vector: typically 150–350 dimensions

• Classifiers compared via RandomizedSearchCV + cross-validation:

  • Support Vector Machine (RBF & linear kernels)
  • Random Forest
  • k-Nearest Neighbors

• Best performing model (as of training): usually SVM (RBF) or Random Forest with test set accuracy in the range 96–99%

• Evaluation:

  • Stratified train/test split (80/20)
  • 5-fold RandomizedSearchCV for hyperparameter tuning
  • 10-fold cross-validation on final model
  • Learning curve analysis (training vs validation accuracy vs dataset size)

Dataset

• Name: COIL-100 (Columbia Object Image Library)
• Source: Official Columbia University page
• Size: 7,200 images
• Classes: 100 distinct everyday objects
• Images per class: 72 (one every 5° rotation: 0°–355°)
• Resolution: 128 × 128 pixels
• Properties: Single centered object, black homogeneous background, controlled lighting → ideal for classical feature-based methods
• License: Commonly used for academic/research purposes (non-commercial)

Performance

• Final test accuracy (best model): ~98.0–99.2% (exact value depends on random seed and final hyperparameter draw)
• Cross-validation mean accuracy (10-fold on training set): ~97.5–98.8%
• Inference speed: very fast — typically <10 ms per image on CPU (feature extraction dominates)

Learning Curve

The learning curve shows excellent generalization behavior:

• Training accuracy reaches ~100% very quickly (SVM perfectly fits even small subsets due to high separability)
• Validation accuracy rapidly improves with more data and converges close to training accuracy
• Very small gap between train and validation → minimal overfitting

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Feature Extraction Visualization

This pipeline includes detailed visualization of every extracted feature type to help understand what information each descriptor captures.

Example visualization outputs:

Github

Repo: https://github.com/itz-Amethyst/AI_Space/projects/ML/object-sorting

How to Use the Model

Prerequisites

pip install opencv-python scikit-learn scikit-image joblib huggingface_hub numpy

Usage

from huggingface_hub import hf_hub_download
import joblib
import cv2
import numpy as np
# ... import your extract_features function ...

repo_id = "Itz-Amethyst/coil100-vision-sorting-svm"

# Load pipeline components
model     = joblib.load(hf_hub_download(repo_id, "model.joblib"))
scaler    = joblib.load(hf_hub_download(repo_id, "scaler.joblib"))
selector  = joblib.load(hf_hub_download(repo_id, "selector.joblib"))
pca       = joblib.load(hf_hub_download(repo_id, "pca.joblib"))
le        = joblib.load(hf_hub_download(repo_id, "label_encoder.joblib"))

# Example inference on a new image
img = cv2.imread("path/to/new_object.png")
features = extract_features(img)                      # your feature function
features_scaled = scaler.transform([features])
features_selected = selector.transform(features_scaled)
features_reduced = pca.transform(features_selected)

pred_idx = model.predict(features_reduced)[0]
predicted_class = le.inverse_transform([pred_idx])[0]

print(f"Predicted object class: {predicted_class}")

Citation

@article{nene1996columbia,
  title={Columbia object image library (coil-100)},
  author={Nene, Sameer A and Nayar, Shree K and Murase, Hiroshi},
  journal={Technical Report CUCS-005-96},
  year={1996},
  institution={Columbia University}
}```