assignment-bp-roma-cherniak-2026 0.1.1

make my own ASL clasifier on A , B , C , D , nothing latters using Pytorch and OpenCV

MY Simple ASL project with OpenCV

Installation - pip install assignment_bp_roma_cherniak_2026

Quick Start

Predict from image file

from asl_classifier import load_model, predict
from PIL import Image

model = load_model()
image = Image.open("hand.jpg")
label, confidence = predict(model, image)
print(f"Prediction: {label} ({confidence}%)")

Try it live with your webcam

from asl_classifier import load_model, run_webcam

model = load_model()
run_webcam(model)  # press Q to quit

API Reference

load_model()

Loads the ASL classifier model with pretrained weights.

predict(model, image)

Runs inference on a single PIL image.

• model — loaded model from load_model()
• image — PIL Image object

run_webcam(model)

Opens webcam and runs live prediction in real time.

• model — loaded model from load_model()
• Press Q to quit

Evaluation Proposal

The model is trained on the ASL Alphabet dataset, a collection of 87,000 200×200 RGB images across 29 classes. For this project a 5-class subset (A, B, C, D, nothing) is used, split 80/10/10 into train/validation/test sets with stratified sampling to preserve class balance (~3,500 images per class in training).

Metrics

• Per-epoch validation accuracy (primary signal for early stopping)
• Per-class precision, recall, and F1-score on the held-out test set
• Confusion matrix to identify which sign pairs are most often confused

Pipeline

• Training: Adam optimizer, cross-entropy loss, 20 epochs, early stopping on validation loss plateau
• Preprocessing: Resize((128, 128)), per-channel mean/std normalization (0.5/0.5)
• Evaluation: the test set is never seen during training or hyperparameter tuning; final metrics are reported once against this set only
• Real-world sanity check: qualitative webcam testing across different lighting conditions and hand positions