initial commit

09_preptrained_effnetb2_20_percent (2).pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7e8e5bbde000377e5b344db2fb6a136c33820becbda8796ab59a7afd3796bb63
+size 31288122

app.py

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+import gradio as gr
+import os
+import torch
+
+#from demos.foodvision_mini.model import create_effnetb2_model
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+#setup classnames
+
+class_names = ['pizza', 'steak', 'sushi']
+
+# model and trandorms preparations
+effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=3)
+
+#load save weights
+
+effnetb2.load_state_dict(
+    torch.load(f = "09_preptrained_effnetb2_20_percent (2).pth",
+               map_location = torch.device('cpu'))
+
+)
+
+# make predictions
+
+def predict(img) -> Tuple[Dict,float] :
+  start_time = timer()
+  # this returns the prediction, and then, time
+  #start a timers
+  # transform the input image for use with effnetb2
+  #put model into eval mode
+  # create a prediction label and prediction probability dictionary
+  img =  effnetb2_transforms(img).unsqueeze(0)
+  effnetb2.eval()
+
+  with torch.inference_mode():
+    pred_probs = torch.softmax(effnetb2(img), dim = 1)
+
+  pred_labels_and_probs = {class_names[i]:float(pred_probs[0][i]) for i in range(len(class_names))}
+
+  end_time = timer()
+  pred_time = round(end_time - start_time, 4)
+
+  return pred_labels_and_probs, pred_time
+
+import os
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+title = "FoodVision Mini"
+Description = "An EfficientNetB2 feature computer vision model to classify images as pizza, steak or sushi"
+article = "Cretated at......"
+
+demo = gr.Interface(fn=predict,inputs=gr.Image(type='pil'),
+                    outputs =[gr.Label(num_top_classes=3, label = "Predictions"),
+                              gr.Number(label="Prediction time (s)")],
+                    examples= example_list,
+                    title = title,
+                    description=Description,
+                    article=article)
+
+demo.launch(debug=False,share = True) # print errors locally, generate a publically shareable URL

model.py

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+
+import torch
+import torchvision
+from torch import nn
+
+def create_effnetb2_model(num_classes:int=3, seed:int=42):
+
+  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+
+  transforms = weights.transforms()
+
+  #setup pretrained model instance
+
+  model = torchvision.models.efficientnet_b2(weights=weights)
+
+  # free base layer in the model
+
+  for param in model.parameters():
+    param.requires_grad = False
+
+  torch.manual_seed(seed)
+  model.classifier = nn.Sequential(
+      nn.Dropout(p = 0.3, inplace= True),
+      nn.Linear(in_features = 1408,out_features =num_classes, bias = True)
+
+  )
+
+  return model, transforms

requirements.txt

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+torch ==2.2.0
+torchvision ==0.17.0
+gradio==4.26.0