Plastic Waste Classifier
Classifies plastic waste images into 6 resin types for recycling sorting.
Built for Hackniche 4.0.
Performance (Test Set, 5-view TTA)
| Metric | Value |
|---|---|
| Accuracy | 92.02% |
| Macro F1 | 0.9077 |
| Best Val F1 | 0.9177 |
Model Details
| Property | Value |
|---|---|
| Base Model | convnext_base.fb_in22k_ft_in1k_384 |
| Pretraining | ImageNet-22k then ImageNet-1k (86.8% top-1) |
| Input Size | 384 x 384 px |
| Parameters | ~88M |
| Fine-tuning | Full LLRD + EMA |
Classes
| Label | Code | Material | Recyclable |
|---|---|---|---|
| PET | #1 | Polyethylene Terephthalate | Yes |
| HDPE | #2 | High-Density Polyethylene | Yes |
| LDPE | #4 | Low-Density Polyethylene | Yes |
| PP | #5 | Polypropylene | Yes |
| PS | #6 | Polystyrene | No |
| Other | #7 | Mixed / Unknown | Depends |
Quick Start
import torch, timm, numpy as np
import albumentations as A
from albumentations.pytorch import ToTensorV2
from huggingface_hub import hf_hub_download
from PIL import Image
weights = hf_hub_download('Vansh180/plastic-waste-classifier', 'plastic_classifier_best.pt')
model = timm.create_model('convnext_base.fb_in22k_ft_in1k_384', pretrained=False, num_classes=6)
model.load_state_dict(torch.load(weights, map_location='cpu'))
model.eval()
transform = A.Compose([
A.Resize(416, 416), A.CenterCrop(384, 384),
A.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225]), ToTensorV2(),
])
CLASS_NAMES = ['HDPE','LDPE','Other','PET','PP','PS']
img = np.array(Image.open('plastic.jpg').convert('RGB'))
t = transform(image=img)['image'].unsqueeze(0)
with torch.no_grad():
probs = torch.softmax(model(t), dim=1)[0]
print(CLASS_NAMES[probs.argmax()], f'{probs.max():.1%}')
License
Apache 2.0