You can deploy this model and start earning money today!

faldeus0092/
tyre-classification-efficientnetb7

Views

No views yet

image_classification

(this model was not trained using Trainer API) This model is a fine-tuned version of EfficientNetB7 on the Tyre-Quality-Classification dataset. It achieves the following results on the evaluation set:

Loss: 0.2341
Accuracy: 91.9355%

Intended uses & limitations

Can be used for quality control to identify the condition of tyres

Training and evaluation data

Data can be seen at Weights and Biases

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.001
train_batch_size: 16
eval_batch_size: 16
train_set: 1434
test_set: 372
optimizer: SGD with momentum = 0.9
num_epochs: 5

Example usage

from efficientnet_pytorch import EfficientNet
import torch
import torchvision.transforms as transforms

model = EfficientNet.from_name('efficientnet-b7')
model._fc= torch.nn.Linear(in_features=model._fc.in_features, out_features=len(annotations_map), bias=True)
model.load_state_dict(torch.load('/content/efficientnetb7_tyrequality_classifier.pth'))

model.eval()
img = Image.open('/content/defective-tires-cause-accidents-min.jpg')
test_transform = transforms.Compose([
    transforms.Resize(224),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406],
                          [0.229, 0.224, 0.225])
])
input_data = test_transform(img).unsqueeze(0)

with torch.no_grad():
    output = model(input_data)

_, predicted_class = torch.max(output, 1)

probs = torch.nn.functional.softmax(output, dim=1)
conf, _ = torch.max(probs, 1)

print('Predicted Class:', predicted_class.item())
print('Predicted Label:', id2label[predicted_class.item()])
print(f'Confidence: {conf.item()*100}%')

plt.title(id2label[predicted_class.item()])
plt.axis("off")
plt.imshow(img)
plt.show()

faldeus0092/tyre-classification-efficientnetb7