2. Technical Specifications
| Specification | Details |
|---|
Assignment 1 / Image / Model Backbone
Model Architecture
Interactive exploration of the end-to-end training pipeline and backbone architectures.
Assignment 1 / Image / Model Backbone
1. End-to-End Pipeline
Raw Data
(Images)
(Images)
→
Preprocessing
(Resize 256 → Crop 224)
(Resize 256 → Crop 224)
→
Model Backbone
ResNet50
ResNet50
→
Classifier Head
(Linear Layer)
(Linear Layer)
→
Output
(257 Class Logits)
(257 Class Logits)
ResNet50 Internal Flow:
- Conv1 & MaxPool: Initial spatial downsampling (7x7 convolution).
- Layers 1 to 4: Stacked Bottleneck blocks with Residual Connections (Skip Connections) to solve the vanishing gradient problem.
- Global Average Pooling (GAP): Flattens the 7x7 spatial feature map into a 2048-dimensional feature vector.
3. Learning Mechanism: Local Features vs. Global Context
A fundamental difference exists in how these two architectures process spatial information and build representations from raw pixels.
Convolutional Neural Network (CNN)
- Local Receptive Fields: Employs sliding convolutional filters (e.g., 3x3) to capture local spatial correlations such as edges, corners, and textures.
- Hierarchical Abstraction: Progressively builds representations. Early layers "see" local patterns, while deeper layers combine them into full semantic objects through pooling.
- High Inductive Bias: Inherently assumes that pixels close to each other are related (spatial locality) and that objects can appear anywhere in the image (translation invariance). This makes CNNs highly sample-efficient on smaller datasets.
Vision Transformer (ViT)
- Global Self-Attention: Treats the image as a sequence of flattened 16x16 patches. Every patch calculates its relationship (attention score) with every other patch from the very first layer.
- Contextual Understanding: Capable of capturing long-range dependencies and global context immediately, rather than waiting for deep layers to pool features together.
- Low Inductive Bias: Has no built-in assumption about translation invariance or spatial locality. It must learn these spatial properties entirely from data, making it inherently "data-hungry" and heavily reliant on large-scale pre-training.