refactor: rename model

README.md

@@ -6,7 +6,7 @@
 [![Tests](https://img.shields.io/badge/Tests-PyTest-green.svg)](tests/)
 
 
-# Facial Expression Recognition
+# EmoteVision
 
 A compact end-to-end training and evaluation pipeline for facial expression classification using a Hugging Face dataset and a ResNet50 backbone.
 

src/models/__init__.py

@@ -1,3 +1,3 @@
-from src.models.facial_recognition_model import FacialRecognitionModel
+from src.models.emote_vision_model import EmoteVisionModel
 
-__all__ = ["FacialRecognitionModel"]
+__all__ = ["EmoteVisionModel"]

src/models/facial_recognition_model.py → src/models/emote_vision_model.py

@@ -3,12 +3,13 @@
 import torch.nn.functional as F
 from torchvision.models import resnet50, ResNet50_Weights
 
-class FacialRecognitionModel(nn.Module):
+
+class EmoteVisionModel(nn.Module):
     def __init__(self, embedding_size: int = 512, num_classes: int = 7):
         super().__init__()
 
         # Pretrained ResNet50 backbone
-        self.base_model = resnet50(weights = ResNet50_Weights.DEFAULT)
+        self.base_model = resnet50(weights=ResNet50_Weights.DEFAULT)
 
         in_features = self.base_model.fc.in_features
         self.base_model.fc = nn.Identity()
@@ -27,12 +28,11 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         Returns logits suitable for `nn.CrossEntropyLoss`.
         """
         features = self.base_model(x)
-        vector = torch.flatten(features, start_dim = 1)
+        vector = torch.flatten(features, start_dim=1)
 
         embeddings = self.embedding_layer(vector)
         embeddings = F.relu(embeddings)
 
         logits = self.classifier(embeddings)
 
         return logits
-

src/pipeline.py

@@ -6,7 +6,7 @@
 from rich.panel import Panel
 
 from src import DataLoader, Trainer, Evaluator
-from src.models import FacialRecognitionModel
+from src.models import EmoteVisionModel
 
 console = Console()
 
@@ -22,7 +22,7 @@ def run_pipeline(config: dict = None) -> None:
     output_dir = config.get("output_dir", "outputs")
     os.makedirs(output_dir, exist_ok=True)
 
-    console.print(Panel.fit("[bold blue]Facial Expression Recognition Pipeline[/bold blue]"))
+    console.print(Panel.fit("[bold blue]EmoteVision Pipeline[/bold blue]"))
 
     # Step 1: Data Preparation
     with console.status("[bold green]Loading DataModule...[/bold green]", spinner="dots"):
@@ -32,7 +32,7 @@ def run_pipeline(config: dict = None) -> None:
 
     # Step 2: Model & Optimizer Setup
     with console.status("[bold green]Initializing Model & Optimizer...[/bold green]", spinner="dots"):
-        model = FacialRecognitionModel()
+        model = EmoteVisionModel()
         optimizer = optim.Adam(model.parameters(), lr=config["learning_rate"])
         criterion = nn.CrossEntropyLoss()
     console.print("  Initialized Model and Optimizer")

tests/test_facial_recognition.py

@@ -1,12 +1,12 @@
 import pytest
 import torch
-from src.models import FacialRecognitionModel
+from src.models import EmoteVisionModel
 
 def test_model_output_shape():
     batch_size = 4
     num_classes = 5
 
-    model = FacialRecognitionModel(num_classes = num_classes)
+    model = EmoteVisionModel(num_classes = num_classes)
 
     fake_images = torch.randn(batch_size, 3, 150, 150)
 
@@ -16,7 +16,7 @@ def test_model_output_shape():
     assert output.shape == (batch_size, num_classes), f"Expected shape {(batch_size, num_classes)}, got {output.shape}"
 
 def test_backbone_weights_are_frozen():
-    model = FacialRecognitionModel()
+    model = EmoteVisionModel()
 
     first_layer_param = next(model.base_model.parameters())
 

tests/test_trainer.py

@@ -1,5 +1,5 @@
 import pytest
-from src.models import FacialRecognitionModel
+from src.models import EmoteVisionModel
 from src import Trainer
 import torch
 import torch.nn as nn