task/task_03/README.md

🔬 Task 3: Beam Search & Length Penalty Ablation for Caption Quality Trade-offs

📌 The Big Question: Does Beam Search Actually Make Captions Better?

When an AI model generates a caption for an image, it faces a decision at every single word: which word should come next? The simplest approach is greedy decoding — at each step, just pick the single highest-probability word and move on. It's fast, but it's short-sighted. It often gets "trapped" in a mediocre caption because it couldn't look ahead.

Beam search changes this by keeping multiple candidate captions alive simultaneously and only committing when the full sequence is generated. But this comes at a cost — it's slower, and the quality gains aren't guaranteed.

Then there's length penalty: a scalar that either punishes the model for producing short captions (< 1.0) or rewards it for staying concise (> 1.0). The interaction between beam size and length penalty is non-trivial and poorly understood without experiments.

This task cracks the problem open with a full ablation study across 9 decoding configurations to answer:

• Which combination of beam size and length penalty produces the best captions?
• Is the quality improvement worth the latency cost?
• What's the Pareto-optimal strategy for real-time vs. offline captioning?

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🧠 Background: Training Setup

Before decoding, we need a good model. This task proceeds in two phases:

Phase 1: Fine-tune BLIP on 10k COCO Captions

BLIP (Bootstrapping Language-Image Pre-training) is fine-tuned on 10,000 training image–caption pairs from the MS-COCO 2017 dataset using the existing training pipeline:

python train.py --model blip

• Training data: 10,000 COCO training images (30,000 used in the main project)
• Epochs: 3 with cosine LR schedule and linear warmup
• Optimizer: AdamW, lr=1e-5, effective batch size=64 (gradient accumulation)
• Checkpointing: Best checkpoint saved to outputs/blip/best/ based on validation CIDEr
• Best validation CIDEr achieved during training: 0.6199 (at epoch 3)

The fine-tuned checkpoint in outputs/blip/best/ is the model used for all 9 ablation configurations below.

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🛑 Baseline: Greedy Decoding (beam=1)

Before running beam search, we establish a greedy baseline — the simplest possible decoding strategy.

Metric	Score
CIDEr	0.4783
BLEU-4	0.2341
METEOR	0.2701
ROUGE-L	0.4502
Mean caption length	9.8 tokens
Latency per 100 images	4.2s

Why it fails: Greedy decode selects each word independently. By ignoring future context, it often commits to a locally plausible but globally mediocre path — resulting in generic captions like "a man is standing in a field" even when the image contains much richer detail.

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🌟 Enhanced: Beam Search Ablation (3×3 Grid)

Design: The 9-Configuration Grid

We sweep two decoding hyperparameters simultaneously:

beam_size      ∈ {1, 3, 5}
length_penalty ∈ {0.8, 1.0, 1.2}
──────────────────────────────────────
Total configurations : 9
Evaluation images    : 500 COCO val

What each parameter controls:

Parameter	< 1.0	= 1.0	> 1.0
length_penalty	Punishes short captions (forces longer output)	Neutral	Rewards compact captions
beam_size	1 = greedy	3 = balanced	5 = high quality, slower

Metrics Computed Per Configuration

For each of the 9 configurations, four quality metrics are computed on 500 COCO validation images:

Metric	What it Measures
CIDEr	Consensus-based: how well captions match 5 human references
BLEU-4	4-gram precision overlap with reference captions
METEOR	Precision/recall with stemming, synonym matching
ROUGE-L	Longest common subsequence F1 with references
Mean Length	Average number of tokens per generated caption
Latency/100	Seconds to generate captions for 100 images

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📊 Full Results: All 9 Configurations

Results sorted by CIDEr score (primary metric):

Rank	Beam	LenPen	CIDEr	BLEU-4	METEOR	ROUGE-L	Avg Len	Lat/100	Pareto?
1 🏆	5	1.0	0.5598	0.2891	0.3089	0.4953	10.8	15.1s	✅
2	3	1.2	0.5456	0.2791	0.2981	0.4872	11.2	9.4s	✅
3	3	1.0	0.5451	0.2821	0.3012	0.4891	10.5	9.1s	✅
4	5	1.2	0.5106	0.2674	0.2914	0.4734	11.9	15.8s	—
5	3	0.8	0.5031	0.2641	0.2891	0.4705	9.6	8.7s	—
6	5	0.8	0.4914	0.2558	0.2834	0.4621	9.4	14.2s	—
7	1	1.0	0.4783	0.2341	0.2701	0.4502	9.8	4.2s	✅
8	1	1.2	0.4651	0.2271	0.2658	0.4461	10.4	4.3s	—
9	1	0.8	0.4512	0.2201	0.2614	0.4389	9.2	4.1s	—

✅ Pareto-optimal = no other config has both higher CIDEr AND lower latency.

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🌡️ CIDEr Heatmap: Beam Size × Length Penalty

The heatmap visualizes how CIDEr score varies across the full 3×3 grid. Warmer (brighter) cells = better caption quality.

Length Penalty →     0.8      1.0      1.2
                  ┌────────┬────────┬────────┐
Beam = 1          │ 0.4512 │ 0.4783 │ 0.4651 │  ← greedy, fastest
                  ├────────┼────────┼────────┤
Beam = 3          │ 0.5031 │ 0.5451 │ 0.5456 │  ← balanced sweet spot
                  ├────────┼────────┼────────┤
Beam = 5          │ 0.4914 │★0.5598 │ 0.5106 │  ← peak quality
                  └────────┴────────┴────────┘

Key pattern: The length_penalty=1.0 column is consistently strong. lp=0.8 penalizes longer candidates too aggressively, causing early truncation. lp=1.2 over-rewards length, leading to captions that run on beyond the reference length and accumulate noise tokens.

See results/cider_heatmap.png for the colour-coded version.

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⚡ Latency Analysis: The Speed–Quality Tradeoff

Generation time (seconds per 100 images) vs. CIDEr score:

CIDEr
0.56 |                              ★ (beam=5, lp=1.0)
0.55 |            ● ●  (beam=3, lp=1.0/1.2)
0.50 |    ●
0.48 |                                     Pareto
0.47 | ● (beam=1, lp=1.0)                  Frontier ─╮
     └──────────────────────────────────────────────────
          4s       9s      14s      →  Latency/100

Use Case	Recommended Config	CIDEr	Latency/100
Real-time (live captioning, APIs)	beam=1, lp=1.0	0.4783	4.2s
Balanced (standard apps)	beam=3, lp=1.0	0.5451	9.1s
Offline (batch processing, archives)	beam=5, lp=1.0	0.5598	15.1s

Key finding: Going from greedy (beam=1) to beam=3 yields a +14% CIDEr improvement at only a 2.2× latency cost. Going further from beam=3 to beam=5 adds only +2.7% more CIDEr at a further 1.7× latency cost — rapidly diminishing returns.

See results/latency_barchart.png and results/quality_speed_scatter.png.

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🔍 Analysis: Key Findings

Finding 1: Beam Size Matters More Than Length Penalty

Across all three length penalty settings, the CIDEr variance driven by beam size (range: ~0.08) is larger than the variance driven by length penalty (range: ~0.03). Beam size is the primary lever; length penalty is a fine-tuning knob.

Finding 2: Length Penalty = 1.0 is the Safest Default

For every beam size, lp=1.0 performs at par or best. This is because the COCO captions used as references are themselves moderate length (~10 tokens). Any penalty that pushes the model toward shorter (lp=0.8) or longer (lp=1.2) sequences diverges from the reference distribution.

Finding 3: Optimal for API Design

• Real-time captioning API (< 5s/100 images required): use beam=1, lp=1.0
• Standard captioning (< 10s/100): use beam=3, lp=1.0 ← recommended default
• High-fidelity offline: use beam=5, lp=1.0

Finding 4: Why lp=0.8 Hurts

lp=0.8 encourages the beam to prefer shorter sequences. Combined with beam=5, it actually reduces CIDEr below the greedy baseline for some images because BLIP's captions are already quite compact and penalizing length causes early stopping before key objects are mentioned.

Finding 5: BLEU-4 Agrees With CIDEr

The ranking by BLEU-4 is nearly identical to CIDEr ranking (Spearman ρ ≈ 0.93), validating that our CIDEr-based conclusions are not an artifact of the metric choice.

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🏗️ Pipeline: 5 Independent Components

All code is organized into 5 self-contained modules. Each can be imported individually in a Jupyter notebook or run as a standalone script:

File	What It Does	Returns
step1_load_model.py	Load BLIP + fine-tuned checkpoint	(model, processor, device)
step2_prepare_data.py	Load 500 COCO val images	DataLoader
step3_run_ablation.py	Run 9-config grid, compute 4 metrics + latency	list[dict] (9 result rows)
step4_visualize.py	Generate 3 publication figures	dict[str, path]
step5_analyze.py	Pareto analysis, findings report	dict (findings)
pipeline.py	Master orchestrator — chains all steps	All of the above

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🚀 How to Run

Make sure you are in the project root directory and your virtualenv is active.

source venv/bin/activate
export PYTHONPATH=.

Option A: Run Full Pipeline (Demo Mode — No GPU Required)

Uses pre-computed results bundled in results/ablation_results.json. All 3 figures are generated, the analysis is printed, and findings.md is saved.

venv/bin/python task/task_03/pipeline.py --demo

Outputs:

• task/task_03/results/cider_heatmap.png — 3×3 CIDEr heatmap
• task/task_03/results/latency_barchart.png — latency per config
• task/task_03/results/quality_speed_scatter.png — Pareto scatter
• task/task_03/results/findings.md — written analysis

Option B: Run Full Pipeline (Live GPU Inference)

Downloads COCO val, runs all 9 configs end-to-end. Requires the fine-tuned BLIP checkpoint at outputs/blip/best/ and a GPU (MPS or CUDA).

venv/bin/python task/task_03/pipeline.py

Option C: Run Individual Components (for Notebook / HuggingFace inspection)

# Step 1 — Load model
from task.task_03.step1_load_model import load_model
model, processor, device = load_model()

# Step 2 — Prepare data
from task.task_03.step2_prepare_data import load_val_data
dataloader = load_val_data(processor, n=500, batch_size=8)

# Step 3 — Run ablation (or load cached)
from task.task_03.step3_run_ablation import run_ablation
results = run_ablation(model, processor, dataloader, device)

# Step 4 — Visualize
from task.task_03.step4_visualize import visualize_all
paths = visualize_all(results)

# Step 5 — Analyze
from task.task_03.step5_analyze import analyze_results
findings = analyze_results(results)

Option D: Run Step 3 in Live Mode (standalone)

venv/bin/python task/task_03/step3_run_ablation.py --live  # GPU inference
venv/bin/python task/task_03/step3_run_ablation.py         # pre-computed

Option E: Regenerate Figures Only (no inference needed)

venv/bin/python task/task_03/step4_visualize.py   # generates all 3 PNGs
venv/bin/python task/task_03/step5_analyze.py     # prints analysis

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🏆 How to Read and Judge the Results

results/cider_heatmap.png

• Brighter / warmer cells = higher CIDEr (better captions)
• Row = beam size (1 → 3 → 5, top to bottom)
• Column = length penalty (0.8 → 1.0 → 1.2, left to right)
• Look for the ★ — it marks the best config at beam=5, lp=1.0 (CIDEr: 0.5598)

results/quality_speed_scatter.png

• X-axis = latency (lower = faster)
• Y-axis = CIDEr (higher = better)
• Red dashed line = Pareto frontier — configs on this line dominate all others
• Points above the frontier do not exist; points below are dominated

results/findings.md

A machine-readable summary of the best config and insights — suitable for direct inclusion in a project report.

❓ Why Does lp=0.8 Sometimes Beat lp=1.2 for beam=3?

lp=0.8 produces shorter captions that can sometimes align better with short reference captions in COCO. The COCO validation set has high variance in reference length (7–20 tokens). For images with very short human captions, penalizing length (lp=0.8) accidentally aligns better. lp=1.0 wins on average because it is distribution-neutral.

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📁 Folder Structure

task/task_03/
├── step1_load_model.py       # Component 1: Load BLIP + checkpoint
├── step2_prepare_data.py     # Component 2: COCO val DataLoader (500 images)
├── step3_run_ablation.py     # Component 3: 9-config sweep + 4 metrics + latency
├── step4_visualize.py        # Component 4: Heatmap, latency chart, scatter
├── step5_analyze.py          # Component 5: Rankings, Pareto, findings
├── pipeline.py               # Master orchestrator (--demo or live)
└── results/
    ├── ablation_results.json      # Pre-computed 9-config × 6-metric table
    ├── findings.md                # Written analysis (auto-generated)
    ├── cider_heatmap.png          # 3×3 CIDEr quality heatmap
    ├── latency_barchart.png       # Grouped latency bar chart
    └── quality_speed_scatter.png  # Pareto frontier scatter

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⚙️ Dependencies

All dependencies are already in the project requirements.txt:

Package	Used For
transformers	BLIP model loading and inference
torch	GPU acceleration (MPS / CUDA)
datasets	COCO 2017 validation split
pycocoevalcap	CIDEr metric computation
nltk	BLEU-4 and METEOR metrics
rouge-score	ROUGE-L metric
matplotlib	Heatmap, bar chart, scatter figures
numpy	Matrix operations for the heatmap grid

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🔗 Connection to the Broader Project

This task feeds directly back into the main project:

• The best config (beam=5, lp=1.0) is the default decoding setting in eval.py for the main evaluation sweep.
• The latency measurements inform the API design recommendation in app.py (real-time tab uses beam=1, compare tab uses beam=3).
• Results are referenced in the main README and experiments/results_beam_search_and_decoding_settings_comparison.md.

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Author: Manoj Kumar — March 2026