inference pipeline

README.md

@@ -8,4 +8,40 @@ metrics:
 - accuracy
 - f1
 pipeline_tag: image-classification
----
+---
+
+Image Classification
+
+This project is about an image classification task of artificial and natural classes.
+
+Setup:
+
+  pip install -r requirements.txt
+
+
+Inference:
+
+
+  from torchvision import transforms
+  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)
+