first commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+effieciet_b0_model_pretrained.pth filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,79 @@
+### 1. Imports and class names setup ###
+import gradio as gr
+import os
+import torch
+
+from model import create_effnetB0_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+class_names = ['Apple(1-5)',
+ 'Apple(11-15)',
+ 'Apple(16-20)',
+ 'Apple(6-10)',
+ 'AppleExpired',
+ 'Banana(1-2)',
+ 'Banana(3-4)',
+ 'Banana(5-7)',
+ 'Banana(8-10)',
+ 'BananaExpired']
+
+effnetb0, effnetb0_transforms = create_effnetB0_model(
+    num_classes=len(class_names)
+)
+
+# Load saved weights
+effnetb0.load_state_dict(
+    torch.load(
+        f="effieciet_b0_model_pretrained.pth",
+        map_location=torch.device("cpu"),  # load to CPU
+    )
+)
+
+### 3. Predict function ###
+
+# Create predict function
+def predict(img) -> Tuple[Dict, float]:
+    """Transforms and performs a prediction on img and returns prediction and time taken.
+    """
+    # Start the timer
+    start_time = timer()
+
+    # Transform the target image and add a batch dimension
+    img = effnetb0_transforms(img).unsqueeze(0)
+
+    # Put model into evaluation mode and turn on inference mode
+    effnetb0.eval()
+    with torch.inference_mode():
+        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
+        pred_probs = torch.softmax(effnetb0(img), dim=1)
+
+    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
+    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+
+    # Calculate the prediction time
+    pred_time = round(timer() - start_time, 5)
+
+    # Return the prediction dictionary and prediction time
+    return pred_labels_and_probs, pred_time
+
+### 4. Gradio app ###
+
+# Create title, description and article strings
+title = "Fruit Vision "
+
+# Create examples list from "examples/" directory
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict, # mapping function from input to output
+                    inputs=gr.Image(type="pil"), # what are the inputs?
+                    outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
+                             gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
+                    # Create examples list from "examples/" directory
+                    examples=example_list,
+                    title=title
+                    )
+
+# Launch the demo!
+demo.launch(share=True)

effieciet_b0_model_pretrained.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:92945580ee040163d1412408b0828fdf95a05c3d968f0c592f111bcd325ead3c
+size 16382634

model.py

@@ -0,0 +1,36 @@
+import torch
+import torchvision
+
+from torch import nn
+
+
+def create_effnetB0_model(num_classes:int=3,
+                          seed:int=42):
+    """Creates an EfficientNetB2 feature extractor model and transforms.
+
+    Args:
+        num_classes (int, optional): number of classes in the classifier head.
+            Defaults to 3.
+        seed (int, optional): random seed value. Defaults to 42.
+
+    Returns:
+        model (torch.nn.Module): EffNetB0 feature extractor model.
+        transforms (torchvision.transforms): EffNetB0 image transforms.
+    """
+    # Create EffNetB0 pretrained weights, transforms and model
+    weights = torchvision.models.EfficientNet_B0_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.efficientnet_b0(weights=weights)
+
+    # Freeze all layers in base model
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # Change classifier head with random seed for reproducibility
+    torch.manual_seed(seed)
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.3, inplace=True),
+        nn.Linear(in_features=1280, out_features=num_classes),
+    )
+
+    return model, transforms

requirements.txt

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+torch==2.2.1
+torchvision==0.17.1
+gradio==4.25.0