initial commit

.gitattributes

@@ -33,3 +33,5 @@ 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
+effnetb2_traffic_sign_recognition.pth filter=lfs diff=lfs merge=lfs -text
+.pth filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,97 @@
+
+import gradio as gr
+import os
+import torch
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+class_classes = [
+    "Speed limit (20km/h)",
+    "Speed limit (30km/h)",
+    "Speed limit (50km/h)",
+    "Speed limit (60km/h)",
+    "Speed limit (70km/h)",
+    "Speed limit (80km/h)",
+    "End of speed limit (80km/h)",
+    "Speed limit (100km/h)",
+    "Speed limit (120km/h)",
+    "No passing",
+    "No passing for vehicles over 3.5 metric tons",
+    "Right-of-way at the next intersection",
+    "Priority road",
+    "Yield",
+    "Stop",
+    "No vehicles",
+    "Vehicles over 3.5 metric tons prohibited",
+    "No entry",
+    "General caution",
+    "Dangerous curve to the left",
+    "Dangerous curve to the right",
+    "Double curve",
+    "Bumpy road",
+    "Slippery road",
+    "Road narrows on the right",
+    "Road work",
+    "Traffic signals",
+    "Pedestrians",
+    "Children crossing",
+    "Bicycles crossing",
+    "Beware of ice/snow",
+    "Wild animals crossing",
+    "End of all speed and passing limits",
+    "Turn right ahead",
+    "Turn left ahead",
+    "Ahead only",
+    "Go straight or right",
+    "Go straight or left",
+    "Keep right",
+    "Keep left",
+    "Roundabout mandatory",
+    "End of no passing",
+    "End of no passing by vehicles over 3.5 metric tons"
+]
+
+# 2. model and transfomrs prep
+
+effnetb2, effnetb2_transforms = create_effnetb2_model(3)
+
+effnetb2.load_state_dict(torch.load(f="traffic_sign_classification/effnetb2_traffic_sign_recognition.pth", map_location=torch.device("cpu")))
+
+# predict function
+
+def predict(
+    img,
+    model=effnetb2,
+    transform=effnetb2_transforms,
+    class_classes: List[str] = class_classes,   # 43 human-readable names
+    k: int = 3
+) -> Tuple[Dict[str, float], float]:
+    """
+    Returns:
+      • dict of top-k {label: prob} sorted by prob desc
+      • inference time (sec)
+    """
+    start = timer()
+
+    # 1. Pre-process
+    img_t = transform(img).unsqueeze(0)
+
+    # 2. Forward pass
+    model.eval()
+    with torch.inference_mode():
+        logits = model(img_t)
+        probs  = torch.softmax(logits, dim=1).squeeze(0)   # shape [43]
+
+    # 3. Top-k
+    top_probs, top_idxs = probs.topk(k)                    # tensors of length k
+    pred_topk = {
+        class_classes[int(idx)]: float(prob)
+        for idx, prob in zip(top_idxs, top_probs)
+    }
+
+    pred_time = round(timer() - start, 4)
+    return pred_topk, pred_time
+
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+# 4. gradio app

effnetb2_traffic_sign_recognition.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:020a082f6adb11370ecb526c2b7a169162a9d9417cbe95972cc6211027f32839
+size 31513082

model.py

@@ -0,0 +1,47 @@
+import torch
+import torchvision
+from torch import nn
+device = "cuda" if torch.cuda.is_available() else "cpu"
+device
+
+def create_effnetb2_model(num_classes=43,
+                           seed: int=42):
+
+  import torch
+  from torch import nn
+  import torchvision
+  from torchvision import datasets
+  from torchvision import transforms
+  from torchvision.transforms import ToTensor
+
+
+
+  # set up pretrained effnetb2 weights
+
+  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+
+   #2 get effnetb2 transforms
+
+  transforms = weights.transforms()
+
+  #instance
+
+  model = torchvision.models.efficientnet_b2(weights=weights)
+
+  # freeze base layer ≈ zero gradd
+
+  for param in model.parameters():
+    param.requires_grad = False
+
+
+
+  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

@@ -0,0 +1,4 @@
+torch==2.6.0
+torchvision==0.21.0
+gradio==5.35
+