Update README.md

README.md

@@ -1,11 +1,157 @@
 ---
 license: mit
-language:
-- en
+language: en
 pipeline_tag: image-classification
+library_name: pytorch
+datasets: mostafaabla/garbage-classification
 tags:
 - CNN
-- computer-vision
-- image-classificaion
-- waste-classification
----
+---
+
+# Model Card for CNN Waste Classification (PyTorch & OpenCV)
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+A PyTorch Convolutional Neural Network (CNN) for multi-class waste classification using images. Predicts 10 types of waste from static images or real-time webcam streams, supporting applications in smart recycling, education, and research. Uses OpenCV for image handling. Trained on the modified Kaggle Garbage Classification dataset.
+
+## Model Details
+
+### Model Description
+
+A deep learning model for classifying waste into 10 categories: Battery, Cardboard, Clothes, Food Waste, Glass, Metal, Paper, Plastic, Shoes, and Trash. The model uses 6 convolutional layers with batch normalization, dropout, and two fully connected layers. Developed for learning, prototyping, and proof-of-concept smart recycling systems.
+
+* **Developed by:** Gokul Seetharaman
+* **Model type:** Convolutional Neural Network (CNN)
+* **License:** MIT
+* **Finetuned from model \[optional]:** Trained from scratch
+
+### Model Sources \[optional]
+
+* **Repository:** [https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch](https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch)
+* **Dataset:** [https://www.kaggle.com/datasets/mostafaabla/garbage-classification](https://www.kaggle.com/datasets/mostafaabla/garbage-classification)
+
+## Uses
+
+### Direct Use
+
+* Image-based waste detection for smart recycling prototypes
+* Educational demonstrations of CNNs, OpenCV, and PyTorch
+* Research baselines for waste/material classification
+
+
+
+### Recommendations
+
+Users should evaluate model performance on their own data and consider retraining or fine-tuning for domain-specific use. It is not recommended to use the model for high-stakes applications without further testing.
+
+## How to Get Started with the Model
+
+1. Download `best_model.pth` and `object-detection.py` from this repo or [GitHub](https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch).
+2. Run `python object-detection.py` for webcam or image predictions.
+3. Modify `object-detection.py` to use your own image or video source.
+
+## Training Details
+
+### Training Data
+
+* [Kaggle Garbage Classification dataset](https://www.kaggle.com/datasets/mostafaabla/garbage-classification)
+* 10 classes, \~1200 images (split 80/20 train/val)
+* Preprocessing: resized to 224x224, normalized, data augmentation (crop, flip, rotation, color jitter, affine)
+
+### Training Procedure
+
+* 6 Conv layers, 2 FC layers, dropout, batchnorm
+* CrossEntropyLoss, AdamW optimizer, 50 epochs, batch size 8
+
+#### Preprocessing \[optional]
+
+* Images resized to 224x224
+* Normalized with ImageNet means/std
+* Random data augmentation on train set
+
+#### Training Hyperparameters
+
+* Training regime: fp32
+* Epochs: 50, batch size: 8, optimizer: AdamW, LR: 5e-4
+
+#### Speeds, Sizes, Times \[optional]
+
+* Training time: \~90 minutes on a modern GPU (varies)
+* Checkpoint size: \~46MB (`best_model.pth`)
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+* 20% validation split from the Kaggle dataset (stratified)
+
+#### Factors
+
+* Performance measured per-class (precision, recall, F1-score, support)
+
+#### Metrics
+
+* Overall accuracy, confusion matrix, precision/recall/F1-score per class
+
+### Results
+
+* Validation accuracy: **89.56%**
+* Most class F1-scores >0.85, with "Plastic" lower due to visual ambiguity
+* Full confusion matrix and metrics in [GitHub README](https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch#results)
+
+#### Summary
+
+The model reliably classifies 10 types of waste in standard settings. See GitHub for sample images and live demo outputs.
+
+## Model Examination \[optional]
+
+* No explicit interpretability/visualization methods (e.g., GradCAM) included yet.
+
+## Environmental Impact
+
+* Estimated training: <1.5 GPU-hour, carbon footprint minimal for local or single-GPU cloud runs
+* Hardware: NVIDIA GeForce GTX 4060 Laptop GPU
+* Hours used: \~1.5
+
+
+## Technical Specifications \[optional]
+
+### Model Architecture and Objective
+
+* See "Model Details" and [GitHub repo](https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch#model-architecture) for the full PyTorch code.
+
+### Compute Infrastructure
+
+* Local training with NVIDIA GTX 4060 Laptop GPU, 8GB VRAM, 16GB RAM, Windows 11, Python 3.10
+
+#### Hardware
+
+* GPU: GTX 4060 (or equivalent, optional CPU)
+* RAM: 16GB
+
+#### Software
+
+* Python 3.10, PyTorch, OpenCV, NumPy
+
+## Citation 
+
+**BibTeX:**
+
+```bibtex
+@misc{gokulseetharaman2025wastecnn,
+  title={CNN Waste Classification with OpenCV and PyTorch},
+  author={Gokul Seetharaman},
+  year={2025},
+  url={https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch}
+}
+```
+
+**APA:**
+Gokul Seetharaman. (2025). CNN Waste Classification with OpenCV and PyTorch. [https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch](https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch)
+
+
+## Model Card Contact
+
+[GitHub Issues](https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch/issues)