| """Generates: outputs/Ramim_Contribution_Guide.pdf"""
|
| from fpdf import FPDF, XPos, YPos
|
| from pathlib import Path
|
|
|
| W = 190
|
|
|
| class PDF(FPDF):
|
| def header(self):
|
| self.set_font("Helvetica", "B", 9)
|
| self.set_fill_color(26, 107, 138)
|
| self.set_text_color(255, 255, 255)
|
| self.cell(0, 8, "SpiceFusionNet | Ramim's Contribution Guide | CSE 414",
|
| fill=True, new_x=XPos.LMARGIN, new_y=YPos.NEXT, align="C")
|
| self.set_text_color(0)
|
| self.ln(2)
|
|
|
| def footer(self):
|
| self.set_y(-12)
|
| self.set_font("Helvetica", "I", 8)
|
| self.set_text_color(150)
|
| self.cell(0, 8, f"Page {self.page_no()} | Md. Noushad Jahan Ramim | 22201257", align="C")
|
|
|
| def chapter_title(self, num, title):
|
| self.set_fill_color(26, 107, 138)
|
| self.set_text_color(255, 255, 255)
|
| self.set_font("Helvetica", "B", 13)
|
| self.cell(0, 10, f" {num}. {title}", fill=True,
|
| new_x=XPos.LMARGIN, new_y=YPos.NEXT)
|
| self.set_text_color(0)
|
| self.ln(3)
|
|
|
| def section(self, title):
|
| self.set_fill_color(210, 230, 240)
|
| self.set_text_color(20, 60, 90)
|
| self.set_font("Helvetica", "B", 11)
|
| self.cell(0, 8, f" {title}", fill=True,
|
| new_x=XPos.LMARGIN, new_y=YPos.NEXT)
|
| self.set_text_color(0)
|
| self.ln(1)
|
|
|
| def _w(self):
|
| return self.w - self.l_margin - self.r_margin
|
|
|
| def body(self, text):
|
| self.set_x(self.l_margin)
|
| self.set_font("Helvetica", "", 10)
|
| self.set_text_color(30, 30, 30)
|
| self.multi_cell(self._w(), 6, text)
|
| self.ln(2)
|
|
|
| def highlight(self, text):
|
| self.set_x(self.l_margin)
|
| self.set_fill_color(255, 243, 205)
|
| self.set_font("Helvetica", "B", 10)
|
| self.set_text_color(120, 60, 0)
|
| self.multi_cell(self._w(), 7, f" KEY POINT: {text}", fill=True)
|
| self.set_text_color(0)
|
| self.ln(2)
|
|
|
| def code_block(self, lines):
|
| self.set_fill_color(40, 40, 40)
|
| self.set_text_color(180, 230, 180)
|
| self.set_font("Courier", "", 8.5)
|
| self.ln(1)
|
| for line in lines:
|
| self.set_x(self.l_margin)
|
| self.cell(self._w(), 5.2, f" {line}", fill=True,
|
| new_x=XPos.LMARGIN, new_y=YPos.NEXT)
|
| self.set_text_color(0)
|
| self.ln(3)
|
|
|
| def kv(self, key, val):
|
| self.set_x(self.l_margin)
|
| self.set_font("Helvetica", "B", 10)
|
| self.set_text_color(26, 107, 138)
|
| self.multi_cell(self._w(), 6, f" {key}:")
|
| self.set_x(self.l_margin)
|
| self.set_font("Helvetica", "", 10)
|
| self.set_text_color(30, 30, 30)
|
| self.multi_cell(self._w(), 6, f" {val}")
|
| self.ln(1)
|
|
|
| def analogy(self, title, text):
|
| self.set_x(self.l_margin)
|
| self.set_fill_color(232, 245, 233)
|
| self.set_font("Helvetica", "B", 10)
|
| self.set_text_color(30, 100, 30)
|
| self.cell(0, 7, f" Analogy -- {title}", fill=True,
|
| new_x=XPos.LMARGIN, new_y=YPos.NEXT)
|
| self.set_x(self.l_margin)
|
| self.set_font("Helvetica", "I", 9.5)
|
| self.set_text_color(40, 80, 40)
|
| self.multi_cell(self._w(), 6, f" {text}", fill=True)
|
| self.set_text_color(0)
|
| self.ln(3)
|
|
|
| def qa(self, q, a):
|
| self.set_x(self.l_margin)
|
| self.set_font("Helvetica", "B", 10)
|
| self.set_text_color(100, 0, 100)
|
| self.multi_cell(self._w(), 6, f" Q: {q}")
|
| self.set_x(self.l_margin)
|
| self.set_font("Helvetica", "", 10)
|
| self.set_text_color(30, 30, 30)
|
| self.multi_cell(self._w(), 6, f" A: {a}")
|
| self.ln(2)
|
|
|
| pdf = PDF()
|
| pdf.set_margins(10, 15, 10)
|
| pdf.set_auto_page_break(auto=True, margin=15)
|
|
|
|
|
| pdf.add_page()
|
| pdf.set_fill_color(26, 107, 138)
|
| pdf.rect(0, 25, 210, 58, "F")
|
| pdf.set_y(33)
|
| pdf.set_font("Helvetica", "B", 22)
|
| pdf.set_text_color(255, 255, 255)
|
| pdf.cell(0, 12, "SpiceFusionNet", new_x=XPos.LMARGIN, new_y=YPos.NEXT, align="C")
|
| pdf.set_font("Helvetica", "B", 14)
|
| pdf.cell(0, 8, "Ramim's Contribution -- Full Study Guide",
|
| new_x=XPos.LMARGIN, new_y=YPos.NEXT, align="C")
|
| pdf.set_font("Helvetica", "", 11)
|
| pdf.cell(0, 7, "Model Architecture | EfficientNet-B4 | Phase 1 & Phase 2 Training",
|
| new_x=XPos.LMARGIN, new_y=YPos.NEXT, align="C")
|
|
|
| pdf.set_y(93)
|
| pdf.set_text_color(0)
|
| pdf.set_font("Helvetica", "B", 11)
|
| pdf.cell(0, 7, "Prepared for: Md. Noushad Jahan Ramim (ID: 22201257)",
|
| new_x=XPos.LMARGIN, new_y=YPos.NEXT, align="C")
|
| pdf.set_font("Helvetica", "", 10)
|
| pdf.cell(0, 6, "CSE 414 - Machine Learning and Deep Learning Lab",
|
| new_x=XPos.LMARGIN, new_y=YPos.NEXT, align="C")
|
| pdf.cell(0, 6, "University of Asia Pacific | May 2026",
|
| new_x=XPos.LMARGIN, new_y=YPos.NEXT, align="C")
|
|
|
| pdf.ln(8)
|
| pdf.set_fill_color(240, 248, 255)
|
| pdf.set_font("Helvetica", "B", 10)
|
| pdf.set_text_color(26, 107, 138)
|
| pdf.cell(0, 8, " What this guide covers:", fill=True,
|
| new_x=XPos.LMARGIN, new_y=YPos.NEXT)
|
| pdf.set_font("Helvetica", "", 10)
|
| pdf.set_text_color(30)
|
| for item in [
|
| "1. Model Architecture -- why 3 branches, AttentionFusion, fusion_head",
|
| "2. EfficientNet-B4 -- what it is, why B4, transfer learning, 1792-d",
|
| "3. Phase 1 Training -- CE loss, label smoothing, AdamW, LR schedule, early stopping",
|
| "4. Phase 2 Training -- SupCon loss, what freezes, projection head, tau",
|
| "5. Q&A Prep -- every question sir might ask YOU, with ready answers",
|
| ]:
|
| pdf.cell(W, 6.5, f" {item}", new_x=XPos.LMARGIN, new_y=YPos.NEXT)
|
|
|
|
|
| pdf.add_page()
|
| pdf.chapter_title("01", "Model Architecture -- The Big Picture")
|
|
|
| pdf.section("What is SpiceFusionNet?")
|
| pdf.body(
|
| "SpiceFusionNet is the deep learning model YOU designed. It is called a "
|
| "multi-modal fusion network because it combines THREE different types of information "
|
| "from the same spice image -- not just one. Most classifiers look at pixels only. "
|
| "SpiceFusionNet looks at: visual appearance (CNN), surface texture (LBP+GLCM), "
|
| "and color signature (HSV histogram). All three together."
|
| )
|
| pdf.analogy("The Expert Chef",
|
| "A chef identifying a spice doesn't just look at it. They feel the texture "
|
| "(rough or smooth?) and notice the color (bright yellow or dark orange?). "
|
| "SpiceFusionNet does the same -- three information sources, one smart decision.")
|
|
|
| pdf.section("The Three Branches -- What Each One Does")
|
| pdf.kv("Branch 1 -- CNN (Image)",
|
| "Input: 224x224 pixel image. Architecture: EfficientNet-B4 (pretrained on ImageNet). "
|
| "Output: 1792 numbers describing the visual appearance -- shapes, edges, patterns.")
|
| pdf.kv("Branch 2 -- Texture",
|
| "Input: LBP (10-d) + GLCM (48-d) = 58 numbers from surface analysis. "
|
| "Architecture: MLP (58->128->256) with BatchNorm and ReLU. "
|
| "Output: 256 numbers describing surface texture -- rough, smooth, granular.")
|
| pdf.kv("Branch 3 -- Color",
|
| "Input: HSV Histogram -- H:36 bins, S:32 bins, V:32 bins = 100 numbers. "
|
| "Architecture: MLP (100->64->128) with BatchNorm and ReLU. "
|
| "Output: 128 numbers describing exact color signature.")
|
|
|
| pdf.section("Why THREE branches? Why not just the CNN?")
|
| pdf.body(
|
| "Because cumin and coriander look almost IDENTICAL in photos. The CNN alone gets "
|
| "confused. But their TEXTURE is different -- cumin has fine ridges, coriander smoother. "
|
| "Their COLOR is slightly different too. By combining all three, the model has much more "
|
| "information. This is why full fusion gets 99.68% while image-only gets 99.59% -- "
|
| "the extra branches fill the gaps the CNN cannot cover."
|
| )
|
|
|
| pdf.section("AttentionFusion -- The Smart Combination")
|
| pdf.body(
|
| "After three branches output (1792 + 256 + 128 = 2176 numbers total), we combine them. "
|
| "Simple concatenation treats all three equally. AttentionFusion is smarter -- it LEARNS "
|
| "which branch matters most for each spice type."
|
| )
|
| pdf.body(
|
| "How: A tiny neural network takes the 2176-d vector and outputs 3 numbers "
|
| "(alpha_img, alpha_tex, alpha_col). Softmax makes them sum to 1.0 (like percentages). "
|
| "Each branch output is multiplied by its weight. Result: 2176-d intelligently weighted vector."
|
| )
|
| pdf.highlight(
|
| "For turmeric (bright yellow powder) -- color branch gets higher weight. "
|
| "For coriander (textured seed) -- texture branch gets higher weight. "
|
| "The model learns these weights automatically during Phase 3 training. No manual tuning."
|
| )
|
| pdf.code_block([
|
| "# AttentionFusion logic (src/model.py)",
|
| "cat = concat(f_cnn=1792, f_tex=256, f_col=128) # -> 2176-d",
|
| "gates = softmax( Linear(2176, 3) ) # [a_img, a_tex, a_col] sum=1.0",
|
| "fused = a_img*f_cnn + a_tex*f_tex + a_col*f_col # weighted 2176-d",
|
| "out = fusion_head(fused) # MLP(2176->512->11) -> class scores",
|
| ])
|
|
|
| pdf.section("Model Size")
|
| pdf.code_block([
|
| "EfficientNet-B4 backbone : ~19.3 Million parameters",
|
| "Texture + Color MLPs : ~56 Thousand parameters",
|
| "Fusion head + Attention : ~2.2 Million parameters",
|
| "TOTAL : ~21.6 Million parameters",
|
| ])
|
|
|
|
|
| pdf.add_page()
|
| pdf.chapter_title("02", "EfficientNet-B4 -- The Backbone YOU Integrated")
|
|
|
| pdf.section("What is EfficientNet?")
|
| pdf.body(
|
| "EfficientNet is a CNN family created by Google Brain (Tan & Le, ICML 2019). "
|
| "The key innovation: COMPOUND SCALING. Old models scaled in only one direction -- "
|
| "deeper (more layers) OR wider (more filters) OR higher resolution. "
|
| "EfficientNet scales all three dimensions TOGETHER in a balanced ratio. "
|
| "Result: best accuracy per parameter count in its era."
|
| )
|
| pdf.analogy("Balanced Diet vs One Food",
|
| "If you only eat protein (make network deeper only), you miss vitamins and carbs. "
|
| "EfficientNet eats a balanced diet -- deeper + wider + higher resolution in the "
|
| "right proportions. Most efficient use of every parameter.")
|
|
|
| pdf.section("Why B4 Specifically? (YOUR Ablation A4 Proved This)")
|
| pdf.code_block([
|
| "Backbone Accuracy Inference Verdict",
|
| "MobileNetV3-Large 93.5% 0.8ms Too weak for our task",
|
| "EfficientNet-B0 95.0% 1.0ms Still not enough",
|
| "EfficientNet-B2 96.8% 1.4ms Good but not best",
|
| "ResNet-50 94.2% 1.85ms Older architecture, lower acc",
|
| "EfficientNet-B4 99.68% 2.10ms BEST accuracy-speed tradeoff",
|
| ])
|
| pdf.body(
|
| "B5, B6, B7 would be slightly more accurate but significantly slower and need "
|
| "more GPU memory. For real-world deployment, B4 is the sweet spot. "
|
| "This is a design decision YOU justified through experimentation."
|
| )
|
|
|
| pdf.section("How YOU Loaded It (The Exact Code You Wrote)")
|
| pdf.code_block([
|
| "import timm # PyTorch Image Models library",
|
| "",
|
| "self.backbone = timm.create_model(",
|
| " 'efficientnet_b4', # model name in timm",
|
| " pretrained=True, # load ImageNet-1k pretrained weights",
|
| " num_classes=0, # REMOVE original 1000-class head",
|
| " global_pool='avg', # Global Average Pooling -> 1792-d vector",
|
| " drop_rate=0.4, # dropout for regularization",
|
| ")",
|
| "",
|
| "# Output: (batch_size, 1792) feature vector per image",
|
| ])
|
|
|
| pdf.section("What Does Each Parameter Mean?")
|
| pdf.kv("pretrained=True",
|
| "Model starts with weights trained on 1.2 million ImageNet images. It already knows "
|
| "how to detect edges, shapes, textures from general images. We START from this "
|
| "knowledge instead of random weights. This is Transfer Learning.")
|
| pdf.kv("num_classes=0",
|
| "Removes EfficientNet's original 1000-class ImageNet classification head. "
|
| "We only want the raw feature extractor (1792-d output). "
|
| "We add our own img_head (Phase 1) and fusion_head (Phase 3) on top.")
|
| pdf.kv("global_pool='avg'",
|
| "After convolution layers, feature maps are (7x7x1792). Global Average Pooling "
|
| "takes the spatial average of each feature map. 7x7 -> 1 number per map. "
|
| "1792 maps -> 1792-dimensional vector. Translation invariant.")
|
| pdf.kv("drop_rate=0.4",
|
| "40% of neurons randomly set to zero during training. "
|
| "Prevents overfitting. Model cannot memorize -- must learn generalizable patterns.")
|
|
|
| pdf.section("What is Transfer Learning? (You Will Definitely Be Asked)")
|
| pdf.body(
|
| "Transfer learning: use a model trained on Task A to help with Task B. "
|
| "EfficientNet-B4 was trained on ImageNet (1.2M diverse images -- animals, cars, food). "
|
| "It learned GENERAL visual features: how to detect edges, gradients, textures, shapes. "
|
| "We TRANSFER this general knowledge to our spice-specific task."
|
| )
|
| pdf.highlight(
|
| "Without transfer learning: we have 11,000 spice images -- too few to train "
|
| "a 19M parameter model from scratch. With transfer learning: start from strong "
|
| "general knowledge, fine-tune for spices. Result: 99.68% accuracy."
|
| )
|
|
|
| pdf.section("What is the 1792-d Vector?")
|
| pdf.body(
|
| "EfficientNet-B4's last convolutional stage has 1792 feature maps. "
|
| "Each feature map detects a specific visual pattern (one detects horizontal edges, "
|
| "another detects orange colors, another detects granular texture, etc.). "
|
| "After Global Average Pooling, each map collapses to a single number. "
|
| "1792 feature maps -> 1792 numbers. This 1792-d vector is a COMPRESSED ENCODING "
|
| "of the entire image -- all visual information in 1792 floating point numbers."
|
| )
|
|
|
|
|
| pdf.add_page()
|
| pdf.chapter_title("03", "Phase 1 Training -- Backbone Pre-training (30 Epochs)")
|
|
|
| pdf.section("What Happens in Phase 1?")
|
| pdf.body(
|
| "Phase 1 is Step 1 of our 3-phase curriculum training. Goal: teach the model "
|
| "to classify spices from IMAGES ALONE before adding texture and color. "
|
| "Only the CNN branch (image branch) is active. Texture and color branches exist "
|
| "but produce zero outputs (data loaded with multimodal=False). "
|
| "Forward pass: image -> EfficientNet-B4 -> img_head -> 11 class scores."
|
| )
|
|
|
| pdf.section("Loss Function: Cross-Entropy with Label Smoothing (0.1)")
|
| pdf.body(
|
| "Cross-Entropy Loss is the standard loss for classification. For each image, "
|
| "it compares predicted probabilities with the true label. The model learns to "
|
| "maximize probability for the correct class."
|
| )
|
| pdf.body(
|
| "Label Smoothing (0.1): Instead of hard targets (correct=1.0, others=0.0), "
|
| "use soft targets: correct class = 0.9, others = 0.01 each (shared evenly). "
|
| "This prevents overconfidence -- the model learns to say '90% sure' not '100% sure'."
|
| )
|
| pdf.highlight(
|
| "Why label smoothing? An overconfident model memorizes training data and fails on "
|
| "new images. Label smoothing 0.1 reduces overconfidence and improves test accuracy. "
|
| "It is like a student who is confident but not arrogant."
|
| )
|
|
|
| pdf.section("Optimizer: AdamW (Adam with Weight Decay)")
|
| pdf.kv("Learning Rate (lr=1e-4)",
|
| "Controls update step size. 0.0001 is standard for fine-tuning. "
|
| "Too large: model diverges. Too small: training takes forever.")
|
| pdf.kv("Weight Decay (1e-4)",
|
| "L2 regularization -- penalizes large weights. Keeps all weights small and distributed. "
|
| "Prevents any single weight from dominating. Reduces overfitting.")
|
|
|
| pdf.section("Learning Rate Scheduler: Linear Warmup + Cosine Annealing")
|
| pdf.code_block([
|
| "Stage 1 -- Linear Warmup (Epochs 1-3):",
|
| " LR starts at 0.0001 * 0.001 = 0.0000001 (very tiny)",
|
| " Increases linearly to full LR 0.0001 over 3 epochs",
|
| " WHY: At start, weights are randomly initialized or from ImageNet.",
|
| " Large updates immediately can destroy pretrained knowledge.",
|
| " Warmup lets the model stabilize before full-speed training.",
|
| "",
|
| "Stage 2 -- Cosine Annealing (Epochs 4-30):",
|
| " LR decreases following cosine curve: 0.0001 -> 0.000001",
|
| " WHY: As model improves, smaller steps = more precise fine-tuning.",
|
| " Smooth decay avoids overshooting the optimal solution.",
|
| ])
|
| pdf.analogy("Landing a Plane",
|
| "Warmup = slow taxi on runway. Full LR = cruise altitude. "
|
| "Cosine annealing = smooth descent to land. You never land at full speed.")
|
|
|
| pdf.section("Early Stopping (patience=8)")
|
| pdf.body(
|
| "After each epoch, validation accuracy is measured on the held-out val set. "
|
| "If val accuracy does NOT improve for 8 consecutive epochs, training stops "
|
| "automatically -- even if 30 epochs haven't been reached yet."
|
| )
|
| pdf.kv("p1_best.pth", "Saved whenever a NEW highest val accuracy is achieved during training")
|
| pdf.kv("p1_last.pth", "Saved at the very final epoch (whether stopped early or not)")
|
| pdf.body(
|
| "Why patience=8? Generous enough to allow the model to escape flat regions "
|
| "in the loss landscape. Not so large that we waste compute on a stuck model."
|
| )
|
|
|
| pdf.section("What is the Output of Phase 1?")
|
| pdf.body(
|
| "A model (p1_best.pth) that classifies spices from images alone with high accuracy. "
|
| "EfficientNet-B4 has now been fine-tuned from general ImageNet knowledge to "
|
| "spice-specific knowledge. This strong foundation is handed to Phase 2."
|
| )
|
| pdf.highlight(
|
| "Phrase to use: 'Phase 1 establishes a strong image-based feature extractor. "
|
| "The backbone adapts from general ImageNet knowledge to spice-specific visual "
|
| "patterns through fine-tuning. This checkpoint is the foundation for Phase 2.'"
|
| )
|
|
|
|
|
| pdf.add_page()
|
| pdf.chapter_title("04", "Phase 2 Training -- Supervised Contrastive Learning (10 Epochs)")
|
|
|
| pdf.section("Why Do We Need Phase 2?")
|
| pdf.body(
|
| "After Phase 1, the model classifies spices well overall -- but still struggles "
|
| "with HARD cases. Coriander vs cumin look nearly identical in photos. "
|
| "In the model's internal representation (embedding space), coriander and cumin "
|
| "images are clustered very CLOSE to each other -- like neighbors on a map. "
|
| "When they are close, small errors flip the prediction."
|
| )
|
| pdf.body(
|
| "Phase 2 uses Supervised Contrastive Loss (SupCon) to fix this. "
|
| "Goal: same-class images cluster TIGHTLY together. Different-class images "
|
| "(especially the hard pairs) are pushed FAR APART in embedding space."
|
| )
|
| pdf.analogy("Reorganizing a Library",
|
| "Phase 1: books are findable but coriander and cumin books are mixed on the same shelf. "
|
| "Phase 2: reorganize so all coriander books are in one corner, all cumin books "
|
| "in a completely different corner, far away. Finding the right book becomes easy.")
|
|
|
| pdf.section("SupCon Loss -- Simple Explanation")
|
| pdf.body(
|
| "In each training batch of 32 images, there are multiple images per class. "
|
| "For example: 3 cumin images, 2 coriander images, 4 turmeric images, etc."
|
| )
|
| pdf.code_block([
|
| "For each anchor image (say: cumin_photo_01):",
|
| "",
|
| " POSITIVE pairs = other images of the SAME class in the batch",
|
| " -> cumin_photo_02, cumin_photo_03",
|
| " -> PULL these closer: make their embeddings similar",
|
| "",
|
| " NEGATIVE pairs = images of DIFFERENT classes",
|
| " -> all coriander, turmeric, paprika, etc. images",
|
| " -> PUSH these apart: make embeddings different",
|
| "",
|
| "Loss = -log( similarity_with_positives / similarity_with_all_others )",
|
| "Minimize loss = maximize positive similarity relative to negatives",
|
| ])
|
| pdf.kv("Temperature tau=0.07",
|
| "Controls sharpness of contrast. Lower = more aggressive pushing apart. "
|
| "0.07 is from the original SupCon paper (Khosla et al., NeurIPS 2020). "
|
| "Standard value used in research worldwide.")
|
|
|
| pdf.section("What Gets FROZEN and What Gets TRAINED?")
|
| pdf.code_block([
|
| "TRAINABLE in Phase 2:",
|
| " - backbone (EfficientNet-B4) <- reshaped to separate hard pairs",
|
| " - proj_head (1792->512->128) <- creates SupCon embedding space",
|
| "",
|
| "FROZEN in Phase 2 (DO NOT UPDATE):",
|
| " - tex_branch <- not involved in SupCon",
|
| " - col_branch <- not involved in SupCon",
|
| " - img_head <- Phase 1 classification weights, keep intact",
|
| " - fusion_head <- Phase 3 will train this",
|
| ])
|
| pdf.body(
|
| "WHY freeze so much? Phase 2 is ONLY about reshaping the CNN embedding space. "
|
| "We don't want to destroy the Phase 1 classification ability. Only the backbone "
|
| "and projection head need to change. Everything else stays safe."
|
| )
|
|
|
| pdf.section("The Projection Head -- What and Why?")
|
| pdf.body(
|
| "proj_head architecture: Linear(1792->512) -> ReLU -> Linear(512->128) -> L2-Normalize."
|
| )
|
| pdf.body(
|
| "It projects the 1792-d backbone output to a 128-d embedding. "
|
| "L2 normalization means every embedding vector has length exactly 1.0 -- "
|
| "all embeddings live on a 128-dimensional unit sphere (like points on a globe). "
|
| "Cosine similarity measures the angle between two points on this sphere. "
|
| "SupCon loss minimizes angles between same-class points."
|
| )
|
| pdf.highlight(
|
| "After Phase 2: all coriander images cluster in one area of the 128-d sphere. "
|
| "All cumin images cluster in a completely different, distant area. "
|
| "This clean geometric separation makes Phase 3 classification far more confident."
|
| )
|
|
|
| pdf.section("Why No Validation Accuracy in Phase 2?")
|
| pdf.body(
|
| "SupCon loss does not produce class predictions -- it only shapes the embedding space. "
|
| "There is no accuracy to monitor. Phase 2 always runs all 10 epochs, no early stopping. "
|
| "The effectiveness of Phase 2 is proven indirectly by Phase 3's final accuracy."
|
| )
|
|
|
| pdf.section("Ablation A2 -- YOUR Proof That Phase 2 Works")
|
| pdf.code_block([
|
| "Experiment: What if we SKIP Phase 2 entirely?",
|
| "",
|
| "Phase 1 -> Phase 3 directly (no SupCon): ~97.8% accuracy",
|
| "Phase 1 -> Phase 2 -> Phase 3 (full): 99.68% accuracy",
|
| "",
|
| "Improvement from Phase 2: +1.88 percentage points",
|
| "Most impact on: coriander/cumin, paprika/turmeric (the hardest pairs)",
|
| ])
|
| pdf.body(
|
| "This ablation study proves Phase 2 is not optional -- it is essential. "
|
| "The model performs well overall without it, but the hard cases suffer most. "
|
| "And in food safety applications, getting the hard cases RIGHT is critical."
|
| )
|
|
|
|
|
| pdf.add_page()
|
| pdf.chapter_title("05", "Q&A Prep -- Questions Sir Will Ask YOU")
|
|
|
| pdf.section("Architecture Questions")
|
| pdf.qa("Why multi-modal? Why not just CNN?",
|
| "Cumin and coriander look almost identical in photos -- CNN alone gets confused. "
|
| "Texture (LBP+GLCM) and color (HSV) give extra information the image cannot provide. "
|
| "Ablation A1 confirmed: removing any branch drops accuracy. Full fusion is always best.")
|
| pdf.qa("What is AttentionFusion and why not simple concatenation?",
|
| "Simple concatenation weights all branches equally. AttentionFusion uses a learned "
|
| "softmax gate (3 weights summing to 1.0). For yellow powder spices, color branch "
|
| "gets higher weight. For textured seeds, texture gets more weight. The model learns "
|
| "these weights automatically during Phase 3 training -- more intelligent than fixed weights.")
|
| pdf.qa("What does num_classes=0 do?",
|
| "Removes EfficientNet-B4's original 1000-class ImageNet head. We only want the feature "
|
| "extractor part -- 1792-d output. We then add our own custom classification heads.")
|
|
|
| pdf.section("EfficientNet Questions")
|
| pdf.qa("Why EfficientNet-B4 and not ResNet or ViT?",
|
| "We tested 5 backbones (Ablation A4). EfficientNet-B4 gave the best accuracy (99.68%) "
|
| "with fast inference (2.70ms). ResNet-50 only got 94.2%. ViT got 99.73% but is 4.30ms -- "
|
| "37% slower. For real-world deployment, B4 gives the best accuracy-speed tradeoff.")
|
| pdf.qa("What is Transfer Learning?",
|
| "Using a model pre-trained on a large dataset (ImageNet, 1.2M images) as the starting "
|
| "point for our task. EfficientNet-B4 already knows how to detect edges, shapes, textures. "
|
| "We only have 11,000 spice images -- training from scratch would give poor results. "
|
| "Transfer learning gives us a 99.68% result with limited data.")
|
| pdf.qa("What is Global Average Pooling? Why does it give 1792-d?",
|
| "After convolutional layers, we have 1792 feature maps each of spatial size 7x7. "
|
| "Global Average Pooling takes the mean of all 49 values in each feature map. "
|
| "1792 maps x 1 number each = 1792-d vector. This is translation invariant -- "
|
| "the spice can be anywhere in the image and we still get a consistent representation.")
|
|
|
| pdf.section("Phase 1 Questions")
|
| pdf.qa("What is label smoothing and why use it?",
|
| "Instead of hard targets (1.0 correct, 0.0 others), use 0.9 for correct and 0.01 for "
|
| "others. Prevents overconfidence. An overconfident model memorizes training data but "
|
| "fails on new images. Label smoothing 0.1 improves generalization on the test set.")
|
| pdf.qa("Why warmup in the learning rate schedule?",
|
| "At training start, a large LR causes chaotic gradient updates that can destroy "
|
| "pretrained ImageNet weights. Warmup starts with a tiny LR and gradually increases "
|
| "over 3 epochs, letting the model stabilize before full-speed training begins.")
|
| pdf.qa("What is early stopping? Why patience=8?",
|
| "If validation accuracy does not improve for 8 consecutive epochs, training stops. "
|
| "Prevents overfitting and saves compute. Patience=8 is generous enough to escape "
|
| "flat regions in the loss landscape before deciding the model is stuck.")
|
|
|
| pdf.section("Phase 2 Questions")
|
| pdf.qa("Explain Supervised Contrastive Learning simply.",
|
| "In each batch, same-class images are positive pairs, different-class are negatives. "
|
| "SupCon Loss pulls positive pairs close together and pushes negative pairs far apart "
|
| "in the 128-d embedding space. After Phase 2, all cumin images are in one cluster, "
|
| "all coriander in another distant cluster -- they are easy to distinguish.")
|
| pdf.qa("What is temperature tau=0.07 in SupCon?",
|
| "Controls how sharp the contrast is. Lower = more aggressive separation. "
|
| "0.07 is the standard value from Khosla et al. (NeurIPS 2020). "
|
| "Too high: weak learning signal. Too low: training becomes unstable.")
|
| pdf.qa("Why freeze most layers in Phase 2?",
|
| "Phase 2 ONLY reshapes the CNN embedding space for hard-negative pairs. "
|
| "We freeze tex_branch, col_branch, img_head, fusion_head to preserve Phase 1 "
|
| "classification weights. Only backbone and proj_head are updated.")
|
|
|
| pdf.section("The Numbers You Must Know By Heart")
|
| pdf.code_block([
|
| "1792-d = EfficientNet-B4 Global Average Pool output",
|
| "58-d = LBP(10) + GLCM(48) texture input to texture branch",
|
| "100-d = HSV Histogram (H:36 + S:32 + V:32) color input",
|
| "256-d = Texture branch MLP output",
|
| "128-d = Color branch MLP output OR proj_head SupCon embedding",
|
| "2176-d = 1792 + 256 + 128 (fused representation after AttentionFusion)",
|
| "21.6M = Total model parameters",
|
| "Phase 1 = 30 epochs | CE + LabelSmooth(0.1) | ALL params train",
|
| "Phase 2 = 10 epochs | SupCon (tau=0.07) | backbone + proj_head only",
|
| "Phase 3 = 10 epochs | 0.5*CE + 0.5*SupCon | ALL params train",
|
| "99.68% = SpiceFusionNet Top-1 Accuracy on test set",
|
| "2.70ms = Inference time per image",
|
| ])
|
|
|
| pdf.ln(4)
|
| pdf.set_fill_color(26, 107, 138)
|
| pdf.set_text_color(255, 255, 255)
|
| pdf.set_font("Helvetica", "B", 11)
|
| pdf.cell(0, 11, " You built this. You understand it. Walk in with confidence.",
|
| fill=True, new_x=XPos.LMARGIN, new_y=YPos.NEXT, align="C")
|
|
|
| out_path = Path("D:/SpiceNet/outputs/Ramim_Contribution_Guide.pdf")
|
| out_path.parent.mkdir(parents=True, exist_ok=True)
|
| pdf.output(str(out_path))
|
| print(f"PDF saved -> {out_path}")
|
|
|
