--- license: mit datasets: - competitions/aiornot language: - en metrics: - accuracy - f1 pipeline_tag: image-classification --- Fatima 2023 Application This project is about an image classification task of artificial and natural classes. Setup: pip install -r requirements.txt Inference: from torchvision import transforms from PIL import Image import torch inference_transform = transforms.Compose([ transforms.Resize(128), transforms.ToTensor(), transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010]), ]) #load image and model img_example = Image.open("image_example.png").convert('RGB') print("image loaded!") model_loaded = torch.load("fatima_challenge_model_exp3.pt") model_loaded.eval() print("model loaded!") img_example_transformed = inference_transform(img_example) out = model_loaded(img_example_transformed.to(torch.device("cuda:0")).unsqueeze(0)) # Generate predictions _, outs = torch.max(out, 1) prediction = "natural" if int(outs.cpu().numpy())==0 else "artificial" print("prediction = ",prediction)