SAM3-LoRA: Efficient Fine-Tuning with Low-Rank Adaptation

Train SAM3 segmentation models with 99% fewer trainable parameters

Quick Start • Architecture • Training • Inference • Examples • Configuration

Overview

Fine-tune the SAM3 (Segment Anything Model 3) using LoRA (Low-Rank Adaptation) - a parameter-efficient method that reduces trainable parameters from 100% to ~1% while maintaining performance.

Recent Updates

2026-01-04:

Added Multi-GPU training support using DistributedDataParallel (DDP)
New --device argument for easy GPU selection: --device 0 1 2 3
Automatic torchrun launch when multiple GPUs specified
Linear scaling of effective batch size across GPUs

2025-12-18:

Fixed grid-like bounding box pattern in inference by adding NMS (Non-Maximum Suppression)
Added --nms-iou parameter to both infer_sam.py and inference_lora.py
Updated README with NMS documentation and troubleshooting guide
NMS removes overlapping detections for cleaner visualization output

2025-12-08:

Added performance optimization recommendations based on SAM3 original approach
Created light_lora_config.yaml for memory-constrained environments
Updated training config with better hyperparameters for small datasets
Added troubleshooting section for common training issues

2025-12-07:

Fixed missing rle_encode import in train_sam3_lora_native.py
Verified category-aware text prompts work correctly in both training and validation
All mask processing operations now working properly without errors

Why Use This?

✅ Train on Consumer GPUs: 16GB VRAM instead of 80GB
✅ Tiny Checkpoints: 10-50MB LoRA weights vs 3GB full model
✅ Fast Iterations: Less memory = faster training
✅ Easy to Use: YAML configs + simple CLI
✅ Production Ready: Complete train + inference pipeline
✅ Real Applications: Crack detection, defect inspection, and more
✅ Multi-GPU Support: Scale training across multiple GPUs with --device 0 1 2 3

What is LoRA?

Instead of fine-tuning all model weights, LoRA injects small trainable matrices:

W' = W_frozen + B×A  (where rank << model_dim)

Result: Only ~1% of parameters need training!

Architecture

SAM3-LoRA applies Low-Rank Adaptation to key components of the SAM3 architecture:

SAM3 Model Architecture with Full LoRA Adaptation

LoRA Adapters Applied To:

Component	Description	LoRA Impact
Vision Encoder (ViT)	Extracts visual features from input images	High - Primary feature learning
Text Encoder	Processes text prompts for guided segmentation	Medium - Semantic understanding
Geometry Encoder	Handles geometric prompts (boxes, points)	Medium - Spatial reasoning
DETR Encoder	Transformer encoder for object detection	High - Scene understanding
DETR Decoder	Transformer decoder for object queries	High - Object localization
Mask Decoder	Generates segmentation masks	High - Fine-grained segmentation

Data Flow:

Input: Image + Text/Geometric prompts
Encoding: Multiple encoders process different modalities
Transformation: DETR encoder-decoder refines representations
Output: High-quality segmentation masks

LoRA Benefits:

✅ Only ~1% parameters trainable (frozen base + small adapters)
✅ Adapters can be swapped for different tasks
✅ Original model weights preserved
✅ Efficient storage (10-50MB vs 3GB full model)

Installation

Prerequisites

Before installing, you need to:

Request SAM3 Access on Hugging Face
- Go to facebook/sam3 on Hugging Face
- Click "Request Access" and accept the license terms
- Wait for approval (usually instant to a few hours)
Get Your Hugging Face Token
- Go to Hugging Face Settings > Tokens
- Create a new token or use existing one
- Copy the token (you'll need it in the next step)

Install

# Clone repository
git clone https://github.com/yourusername/sam3_lora.git
cd SAM3_LoRA

# Install dependencies
pip install -e .

# Login to Hugging Face
huggingface-cli login
# Paste your token when prompted

Alternative login method:

# Or set token as environment variable
export HF_TOKEN="your_token_here"

Requirements: Python 3.8+, PyTorch 2.0+, CUDA (optional), Hugging Face account with SAM3 access

Verification

Verify your setup is complete:

# Test Hugging Face login
huggingface-cli whoami

# Test SAM3 access (should not give access error)
python3 -c "from transformers import AutoModel; print('✓ SAM3 accessible')"

If you see errors, review the Troubleshooting section.

Quick Start

⚠️ Important: Make sure you've completed the Installation steps, including Hugging Face login, before proceeding.

Example Result: Train a model to detect concrete cracks with just ~1% trainable parameters!

Detection: "concrete crack" with 0.32 confidence • Precise segmentation mask

1. Prepare Your Data

Organize your dataset in COCO format with a single annotation file per split:

data/
├── train/                    # Required
│   ├── img001.jpg
│   ├── img002.jpg
│   └── _annotations.coco.json
├── valid/                    # Optional but recommended
│   ├── img001.jpg
│   ├── img002.jpg
│   └── _annotations.coco.json
└── test/                     # Optional
    ├── img001.jpg
    └── _annotations.coco.json

Note: Validation data (data/valid/) is optional but strongly recommended for monitoring training progress and preventing overfitting.

COCO Annotation Format (_annotations.coco.json):

{
  "images": [
    {
      "id": 0,
      "file_name": "img001.jpg",
      "height": 480,
      "width": 640
    }
  ],
  "annotations": [
    {
      "id": 1,
      "image_id": 0,
      "category_id": 1,
      "bbox": [x, y, width, height],
      "area": 1234,
      "segmentation": [[x1, y1, x2, y2, ...]],
      "iscrowd": 0
    }
  ],
  "categories": [
    {"id": 1, "name": "defect"}
  ]
}

Supported Segmentation Formats:

Polygon: "segmentation": [[x1, y1, x2, y2, ...]] (list of polygons)
RLE: "segmentation": {"counts": "...", "size": [h, w]} (run-length encoded)

2. Train Your Model

# Train with default config
python3 train_sam3_lora_native.py

# Or specify custom config
python3 train_sam3_lora_native.py --config configs/full_lora_config.yaml

Expected output:

Building SAM3 model...
Applying LoRA...
Applied LoRA to 64 modules
Trainable params: 11,796,480 (1.38%)

Loading training data from /workspace/data2...
Loaded COCO dataset: train split
  Images: 778
  Annotations: 1631
  Categories: {0: 'CRACKS', 1: 'CRACKS', 2: 'JOINT', 3: 'LOCATION', 4: 'MARKING'}

Loading validation data from /workspace/data2...
Loaded COCO dataset: valid split
  Images: 152
  Annotations: 298
Found validation data: 152 images
Starting training for 100 epochs...
Training samples: 778, Validation samples: 152

Epoch 1: 100%|████████| 98/98 [07:47<00:00, loss=140]
Validation: 100%|████████| 19/19 [00:32<00:00, val_loss=23.7]

Epoch 1/100 - Train Loss: 156.234567, Val Loss: 17.032280
✓ New best model saved (val_loss: 17.032280)

Epoch 2: 100%|████████| 98/98 [07:24<00:00, loss=167]
Validation: 100%|████████| 19/19 [00:31<00:00, val_loss=20.1]

Epoch 2/100 - Train Loss: 142.891234, Val Loss: 15.641912
✓ New best model saved (val_loss: 15.641912)
...

Validation Strategy (Following SAM3):

During training: Only validation loss is computed (fast, no NMS or metrics)
After training: Run validate_sam3_lora.py for full metrics (mAP, cgF1) with NMS

This approach significantly speeds up training while still monitoring overfitting via validation loss

3. Run Inference

# Basic inference (automatically uses best model)
python3 infer_sam.py \
  --config configs/full_lora_config.yaml \
  --image test_image.jpg \
  --output predictions.png

# With text prompt for better accuracy
python3 infer_sam.py \
  --config configs/full_lora_config.yaml \
  --image test_image.jpg \
  --prompt "yellow school bus" \
  --output predictions.png

# Multiple prompts to detect different objects
python3 infer_sam.py \
  --config configs/full_lora_config.yaml \
  --image test_image.jpg \
  --prompt "crack" "defect" "damage" \
  --output predictions.png

Training

Basic Training

# Use default configuration (single GPU)
python3 train_sam3_lora_native.py

# Or specify custom config
python3 train_sam3_lora_native.py --config configs/full_lora_config.yaml

Multi-GPU Training

Train on multiple GPUs using the --device argument. The script automatically handles distributed training setup.

# Single GPU (default - GPU 0)
python3 train_sam3_lora_native.py --config configs/full_lora_config.yaml

# Single GPU (specific GPU)
python3 train_sam3_lora_native.py --config configs/full_lora_config.yaml --device 1

# Multi-GPU (2 GPUs)
python3 train_sam3_lora_native.py --config configs/full_lora_config.yaml --device 0 1

# Multi-GPU (4 GPUs)
python3 train_sam3_lora_native.py --config configs/full_lora_config.yaml --device 0 1 2 3

# Multi-GPU (specific GPUs, e.g., 0, 2, 3)
python3 train_sam3_lora_native.py --config configs/full_lora_config.yaml --device 0 2 3

Multi-GPU Features:

✅ Automatic torchrun launch when multiple GPUs specified
✅ DistributedDataParallel (DDP) for efficient gradient synchronization
✅ DistributedSampler for proper data sharding
✅ Synchronized validation loss across all GPUs
✅ Model saving only on rank 0 (no file conflicts)

Effective Batch Size: With multi-GPU, your effective batch size scales linearly:

effective_batch_size = batch_size × num_gpus

Config batch_size	GPUs	Effective Batch Size
4	1	4
4	2	8
4	4	16

Expected Output (Multi-GPU):

Launching distributed training on GPUs: [0, 1]
Number of processes: 2
Multi-GPU training enabled with 2 GPUs
Building SAM3 model...
Applying LoRA...
Trainable params: 11,796,480 (1.38%)
Model wrapped with DistributedDataParallel
Effective batch size: 4 x 2 = 8
Starting training for 100 epochs...

Custom Configuration

Create a config file (e.g., configs/my_config.yaml):

lora:
  rank: 16                    # LoRA rank (higher = more capacity)
  alpha: 32                   # Scaling factor (typically 2×rank)
  dropout: 0.1                # Dropout for regularization
  target_modules:             # Which layers to adapt
    - "q_proj"                # Query projection
    - "k_proj"                # Key projection
    - "v_proj"                # Value projection
    - "fc1"                   # MLP layer 1
    - "fc2"                   # MLP layer 2

  # Which model components to apply LoRA to
  apply_to_vision_encoder: true
  apply_to_mask_decoder: true
  apply_to_detr_encoder: false
  apply_to_detr_decoder: false

training:
  data_dir: "/path/to/data"   # Root directory with train/valid/test folders
  batch_size: 8               # Adjust based on GPU memory
  num_epochs: 100             # Training epochs
  learning_rate: 5e-5         # Learning rate (5e-5 recommended for SAM3 fine-tuning)
  weight_decay: 0.01          # Weight decay
  gradient_accumulation_steps: 8  # Effective batch = batch_size × accumulation

output:
  output_dir: "outputs/my_model"

Important Notes:

Category-aware prompts: The training automatically uses category names as text prompts (e.g., "crack", "joint") extracted from COCO annotations
Each training image is prompted with its specific object categories (in lowercase)
This approach improves performance by using task-specific vocabulary while leveraging SAM3's pre-trained text understanding

Then train:

python3 train_sam3_lora_native.py --config configs/my_config.yaml

Model Checkpointing

During training, two models are automatically saved:

best_lora_weights.pt: Best model based on validation loss (saved only when validation loss improves)
last_lora_weights.pt: Model from the last epoch (saved after every epoch)

With validation data: Training monitors validation loss only (fast). Best model is saved when validation loss decreases.

Without validation data: Training continues normally but saves the last epoch as both files. You'll see:

⚠️  No validation data found - training without validation
...
ℹ️  No validation data - consider adding data/valid/ for better model selection

Validation

Overview

SAM3-LoRA uses a two-stage validation approach following SAM3's original design:

During Training: Only validation loss is computed (fast, no expensive metrics)
After Training: Run full evaluation with mAP, cgF1 metrics and NMS filtering

This approach significantly speeds up training while still monitoring overfitting via validation loss.

Quick Validation

After training completes, evaluate your model:

# Validate LoRA-adapted model (uses best model automatically)
python3 validate_sam3_lora.py \
  --config configs/full_lora_config.yaml \
  --weights outputs/sam3_lora_full/best_lora_weights.pt \
  --val_data_dir /workspace/data2/valid

# Evaluate on test set
python3 validate_sam3_lora.py \
  --config configs/full_lora_config.yaml \
  --weights outputs/sam3_lora_full/best_lora_weights.pt \
  --val_data_dir /workspace/data2/test

# Baseline: Validate with original SAM3 model (no LoRA) for comparison
python3 validate_sam3_lora.py \
  --val_data_dir /workspace/data2/valid \
  --use-base-model

Expected Output:

Running SAM3 LoRA Validation
Building SAM3 model...
Loading LoRA weights from outputs/sam3_lora_full/best_lora_weights.pt
Loaded COCO dataset: valid split
  Images: 152
  Annotations: 298

Processing: 100%|████████| 152/152 [02:15<00:00]

Validation Results:
================================================================================
  Total predictions: 946 (after NMS from 1353 initial detections)
  Total ground truth: 298

COCO Evaluation Metrics:
--------------------------------------------------------------------------------
  mAP (IoU 0.50:0.95): 0.245
  mAP@50 (IoU 0.50):   0.287
  mAP@75 (IoU 0.75):   0.198

Category-agnostic F1 Scores:
--------------------------------------------------------------------------------
  cgF1 (avg):          0.135
  cgF1@50:             0.149
  cgF1@75:             0.089
================================================================================

Validation Metrics Explained

Metric	Description	Good Value	Excellent Value
mAP (0.50:0.95)	Mean Average Precision across IoU thresholds 0.5 to 0.95	> 0.30	> 0.50
mAP@50	Precision at IoU threshold 0.50 (looser)	> 0.40	> 0.70
mAP@75	Precision at IoU threshold 0.75 (stricter)	> 0.25	> 0.45
cgF1	Concept-level F1 (SAM3's primary metric)	> 0.25	> 0.50
cgF1@50	cgF1 at IoU 0.50	> 0.30	> 0.60
cgF1@75	cgF1 at IoU 0.75	> 0.15	> 0.35

Understanding the Metrics:

mAP: Standard COCO metric - higher is better, penalizes over/under-segmentation
cgF1: SAM3's concept-level metric - balances precision and recall for concepts, not individual instances
@50/@75: Different IoU thresholds (50% overlap vs 75% overlap)

Advanced Validation Options

1. Adjust Confidence Threshold:

# More conservative (fewer but higher confidence predictions)
python3 validate_sam3_lora.py \
  --config configs/full_lora_config.yaml \
  --weights outputs/sam3_lora_full/best_lora_weights.pt \
  --val_data_dir /workspace/data2/valid \
  --prob-threshold 0.5

# More permissive (more predictions, lower confidence)
python3 validate_sam3_lora.py \
  --config configs/full_lora_config.yaml \
  --weights outputs/sam3_lora_full/best_lora_weights.pt \
  --val_data_dir /workspace/data2/valid \
  --prob-threshold 0.2

2. Merge Overlapping Segments (for crack-like objects):

# Enable merging to reduce over-segmentation
python3 validate_sam3_lora.py \
  --config configs/full_lora_config.yaml \
  --weights outputs/sam3_lora_full/best_lora_weights.pt \
  --val_data_dir /workspace/data2/valid \
  --merge \
  --merge-iou 0.15

# Aggressive merging for highly fragmented objects
python3 validate_sam3_lora.py \
  --config configs/full_lora_config.yaml \
  --weights outputs/sam3_lora_full/best_lora_weights.pt \
  --val_data_dir /workspace/data2/valid \
  --merge \
  --merge-iou 0.05 \
  --prob-threshold 0.5

3. Adjust NMS Settings:

# More aggressive NMS (fewer duplicate detections)
python3 validate_sam3_lora.py \
  --config configs/full_lora_config.yaml \
  --weights outputs/sam3_lora_full/best_lora_weights.pt \
  --val_data_dir /workspace/data2/valid \
  --nms-iou 0.5

# Less aggressive NMS (keep more overlapping segments)
python3 validate_sam3_lora.py \
  --config configs/full_lora_config.yaml \
  --weights outputs/sam3_lora_full/best_lora_weights.pt \
  --val_data_dir /workspace/data2/valid \
  --nms-iou 0.8

4. Baseline Comparison (Original SAM3 Model):

# Validate with original SAM3 model (no LoRA) for comparison
python3 validate_sam3_lora.py \
  --val_data_dir /workspace/data2/valid \
  --use-base-model

# This helps you understand the improvement from LoRA fine-tuning
# Compare against your LoRA model results to see performance gains

Validation Parameters Reference

Parameter	Default	Description	When to Adjust
`--prob-threshold`	0.3	Minimum confidence score	Lower if missing objects (0.2), higher if too many false positives (0.5)
`--nms-iou`	0.7	NMS IoU threshold	Lower for fewer duplicates (0.5), higher to keep overlaps (0.8)
`--merge`	False	Enable segment merging	Use for crack-like or connected objects
`--merge-iou`	0.15	IoU threshold for merging	Lower for aggressive merging (0.05), higher for conservative (0.25)
`--use-base-model`	False	Use original SAM3 (no LoRA)	For baseline comparison

Interpreting Results

Scenario 1: Too Many Predictions

Total predictions: 1353
Total ground truth: 298
mAP@50: 0.29

Solution: Model is over-segmenting. Try:

Increase --prob-threshold to 0.4-0.5
Decrease --nms-iou to 0.5-0.6
Use --merge with --merge-iou 0.15

Scenario 2: Too Few Predictions

Total predictions: 150
Total ground truth: 298
mAP@50: 0.15

Solution: Model is under-detecting. Try:

Decrease --prob-threshold to 0.2
Train longer or with higher LoRA rank

Scenario 3: Good Quantity, Poor Quality

Total predictions: 310
Total ground truth: 298
mAP@50: 0.35 (low)
cgF1@50: 0.25 (low)

Solution: Detections are inaccurate. Need better training:

Train for more epochs
Use configs/full_lora_config.yaml instead of light config
Check data quality

Why Separate Evaluation?

Benefits:

⚡ 10x Faster Training: No expensive metric computation during training
📊 Better Monitoring: Validation loss is sufficient to detect overfitting
🎯 Accurate Metrics: Full evaluation with proper NMS and post-processing
🔧 Flexible Testing: Try different thresholds without retraining

Training Tips

Starting Out:

Use rank: 4 or rank: 8 for quick experiments
Set num_epochs: 5 for initial tests
Monitor that trainable params are ~0.5-2%
Watch validation loss - it should decrease over epochs

Production Training:

Increase to rank: 16 or rank: 32 for better performance
Use num_epochs: 20-50 depending on dataset size
Enable more components (DETR encoder/decoder) if needed
Use early stopping if validation loss stops improving

Troubleshooting:

Loss too low (< 0.001): Model might be overfitting, reduce rank or add regularization
Val loss > Train loss: Normal, indicates some overfitting
Val loss increasing: Overfitting! Reduce rank, add dropout, or stop training
Loss not decreasing: Increase learning rate or rank
OOM errors: Reduce batch size or rank
63% trainable params: Bug! Should be ~1% - make sure base model is frozen

Inference

Run inference on new images using your trained LoRA model. The infer_sam.py script is based on official SAM3 patterns and supports multiple text prompts and NMS filtering for clean, non-overlapping detections.

Command Line

# Basic inference (automatically uses best model)
python3 infer_sam.py \
  --config configs/full_lora_config.yaml \
  --image path/to/image.jpg \
  --output predictions.png

# With text prompt (recommended for better accuracy)
python3 infer_sam.py \
  --config configs/full_lora_config.yaml \
  --image path/to/image.jpg \
  --prompt "yellow school bus" \
  --output predictions.png

# Multiple prompts to detect different object types
python3 infer_sam.py \
  --config configs/full_lora_config.yaml \
  --image street_scene.jpg \
  --prompt "car" "person" "bus" \
  --output segmentation.png

# Use last epoch model instead
python3 infer_sam.py \
  --config configs/full_lora_config.yaml \
  --weights outputs/sam3_lora_full/last_lora_weights.pt \
  --image path/to/image.jpg \
  --prompt "person with red backpack" \
  --output predictions.png

# With custom confidence threshold
python3 infer_sam.py \
  --config configs/full_lora_config.yaml \
  --image path/to/image.jpg \
  --prompt "building" \
  --threshold 0.3 \
  --output predictions.png

# Adjust NMS to reduce overlapping boxes
python3 infer_sam.py \
  --config configs/full_lora_config.yaml \
  --image path/to/image.jpg \
  --prompt "seal" \
  --threshold 0.3 \
  --nms-iou 0.3 \
  --output clean_detections.png

# Mask-only visualization (no boxes)
python3 infer_sam.py \
  --config configs/full_lora_config.yaml \
  --image path/to/image.jpg \
  --prompt "crack" \
  --no-boxes \
  --output masks_only.png

NMS (Non-Maximum Suppression)

NMS removes overlapping bounding boxes to produce clean visualizations. Without NMS, you may see a grid-like pattern of many overlapping boxes.

# Default NMS IoU = 0.5 (good for most cases)
python3 infer_sam.py --config configs/full_lora_config.yaml --image test.jpg --prompt "object"

# More aggressive NMS (fewer boxes, less overlap)
python3 infer_sam.py --config configs/full_lora_config.yaml --image test.jpg --prompt "object" --nms-iou 0.3

# Less aggressive NMS (keep more overlapping detections)
python3 infer_sam.py --config configs/full_lora_config.yaml --image test.jpg --prompt "object" --nms-iou 0.7

NMS IoU Guidelines:

Value	Effect	Use Case
0.3	Aggressive filtering	Single object per region, clean output
0.5	Balanced (default)	Most general use cases
0.7	Keep more boxes	Densely packed objects, overlapping instances

Text Prompts

Text prompts help guide the model to segment specific objects more accurately. New feature: You can now use multiple prompts in a single command!

Single prompt examples:

"yellow school bus" - Specific color and object type
"person wearing red hat" - Object with distinctive features
"car" - Simple, clear object type
"crack" - For defect detection
"building with glass windows" - Object with distinguishing features

Multiple prompt examples:

# Detect different defect types
--prompt "crack" "spalling" "corrosion"

# Detect multiple objects in street scenes
--prompt "car" "person" "traffic sign"

Tips for better prompts:

Be specific but concise
Include distinctive colors or features when relevant
Use natural language descriptions
For multiple prompts, order from most to least important
Match the vocabulary to your training data

Inference Parameters

Parameter	Description	Example	Default
`--config`	Path to training config file	`configs/full_lora_config.yaml`	Required
`--weights`	Path to LoRA weights (optional)	`outputs/sam3_lora_full/best_lora_weights.pt`	Auto-detected
`--image`	Input image path	`test_image.jpg`	Required
`--prompt`	One or more text prompts	`"crack"` or `"crack" "defect"`	`"object"`
`--output`	Output visualization path	`predictions.png`	`output.png`
`--threshold`	Confidence threshold (0.0-1.0)	`0.3`	`0.5`
`--nms-iou`	NMS IoU threshold (lower = fewer boxes)	`0.3`	`0.5`
`--resolution`	Input resolution	`1008`	`1008`
`--no-boxes`	Don't show bounding boxes	-	False
`--no-masks`	Don't show segmentation masks	-	False

Python API

from infer_sam import SAM3LoRAInference

# Initialize inference engine with NMS
inferencer = SAM3LoRAInference(
    config_path="configs/full_lora_config.yaml",
    weights_path="outputs/sam3_lora_full/best_lora_weights.pt",
    detection_threshold=0.5,
    nms_iou_threshold=0.5  # Adjust for cleaner output (lower = fewer boxes)
)

# Run prediction with single text prompt
predictions = inferencer.predict(
    image_path="image.jpg",
    text_prompts=["yellow school bus"]
)

# Run prediction with multiple text prompts
predictions = inferencer.predict(
    image_path="image.jpg",
    text_prompts=["crack", "defect", "damage"]
)

# Visualize results
inferencer.visualize(
    predictions,
    output_path="output.png",
    show_boxes=True,
    show_masks=True
)

# Access predictions for each prompt (NMS already applied)
for idx, prompt in enumerate(["crack", "defect"]):
    result = predictions[idx]
    print(f"Prompt '{result['prompt']}':")
    print(f"  Detections: {result['num_detections']}")
    if result['num_detections'] > 0:
        print(f"  Boxes: {result['boxes'].shape}")      # [N, 4] in xyxy format
        print(f"  Scores: {result['scores'].shape}")    # [N]
        print(f"  Masks: {result['masks'].shape}")      # [N, H, W]

Configuration

LoRA Parameters

Parameter	Description	Typical Values
`rank`	LoRA rank (bottleneck dimension)	4, 8, 16, 32
`alpha`	Scaling factor	2×rank (e.g., 16 for rank=8)
`dropout`	Dropout probability	0.0 - 0.1
`target_modules`	Which layer types to adapt	q_proj, k_proj, v_proj, fc1, fc2

Component Flags

Flag	Description	When to Enable
`apply_to_vision_encoder`	Vision backbone	Always (main feature extractor)
`apply_to_mask_decoder`	Mask generation	Recommended for segmentation
`apply_to_detr_encoder`	Object detection encoder	For complex scenes
`apply_to_detr_decoder`	Object detection decoder	For complex scenes
`apply_to_text_encoder`	Text understanding	For text-based prompts

Preset Configurations

Minimal (Fastest, Lowest Memory)

lora:
  rank: 4
  alpha: 8
  target_modules: ["q_proj", "v_proj"]
  apply_to_vision_encoder: true
  # All others: false

Balanced (Recommended)

lora:
  rank: 16
  alpha: 32
  target_modules: ["q_proj", "k_proj", "v_proj", "fc1", "fc2"]
  apply_to_vision_encoder: true
  apply_to_mask_decoder: true
  # Others: false

Maximum (Best Performance)

lora:
  rank: 32
  alpha: 64
  target_modules: ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"]
  apply_to_vision_encoder: true
  apply_to_mask_decoder: true
  apply_to_detr_encoder: true
  apply_to_detr_decoder: true

Real-World Example: Concrete Crack Detection

SAM3-LoRA excels at detecting structural defects like cracks in concrete. Here's a real example:

Detection Results:

Prompt: "concrete crack"
Confidence: 0.32 (using threshold 0.3)
Segmentation: Precise mask following the crack pattern
Application: Infrastructure inspection, structural health monitoring

Run this example:

# Detect cracks in concrete structures
python3 infer_sam.py \
  --config configs/full_lora_config.yaml \
  --image path/to/concrete.jpg \
  --prompt "concrete crack" \
  --threshold 0.3 \
  --output crack_detection.png

# Detect multiple defect types
python3 infer_sam.py \
  --config configs/full_lora_config.yaml \
  --image path/to/concrete.jpg \
  --prompt "crack" "spalling" "corrosion" \
  --threshold 0.3 \
  --output defect_analysis.png

Use Cases:

🏗️ Civil engineering inspection
🌉 Bridge and infrastructure monitoring
🏢 Building maintenance
🛣️ Road surface analysis
🏭 Industrial facility assessment

Test Results: Road Damage Detection

We evaluated the fine-tuned SAM3-LoRA model on pothole detection, comparing it against the base SAM3 model without fine-tuning.

Validation Metrics Comparison

Validation performance: LoRA fine-tuned model vs Base SAM3 model

Key Findings:

LoRA Model (Fine-tuned): Shows improved precision and better detection of multiple potholes
Base Model: Tends to produce more false positives and misses some instances
Dataset: Pothole detection on road surfaces (data3)

Visual Comparison

Side-by-side comparison: Ground Truth (Green) | LoRA Model (Red) | Base Model (Blue)

Observations from Visual Results:

Image	Ground Truth	LoRA Model	Base Model	Analysis
img_0034	1 pothole	1 detection ✓	5 detections ✗	LoRA matches GT perfectly, Base has 4 false positives
img_0001	1 pothole	1 detection ✓	1 detection ✓	Both models perform well
img_0080	1 pothole	2 detections ~	2 detections ~	Both have 1 false positive
img_0070	1 pothole	1 detection ✓	1 detection ✓	Both models perform well
img_0060	4 potholes	4 detections ✓	2 detections ✗	LoRA finds all instances, Base misses 2

Summary:

LoRA Model: 3/5 perfect matches, better recall on multi-instance images
Base Model: 2/5 perfect matches, struggles with multiple instances and false positives
Overall: Fine-tuning with LoRA significantly improves detection accuracy for domain-specific tasks

Training Details:

Prompt: "pothole" (auto-detected from COCO category names)
Architecture: Full LoRA adaptation (vision, text, DETR encoders/decoders)
Dataset: Road damage images with COCO-format annotations
Threshold: 0.5 confidence for both models

Examples

Example 1: Quick Test (5 Epochs)

# Create minimal config
cat > configs/quick_test.yaml << EOF
lora:
  rank: 4
  alpha: 8
  dropout: 0.1
  target_modules: ["q_proj", "v_proj"]
  apply_to_vision_encoder: true
  apply_to_mask_decoder: false

training:
  batch_size: 1
  num_epochs: 5
  learning_rate: 1e-4
  weight_decay: 0.01

output:
  output_dir: "outputs/quick_test"
EOF

# Train
python3 train_sam3_lora_native.py --config configs/quick_test.yaml

# Inference with text prompt
python3 infer_sam.py \
  --config configs/quick_test.yaml \
  --weights outputs/quick_test/best_lora_weights.pt \
  --image test.jpg \
  --prompt "car" \
  --output result.png

# Multiple prompts
python3 infer_sam.py \
  --config configs/quick_test.yaml \
  --image test.jpg \
  --prompt "car" "person" "bus" \
  --output result.png

Example 2: Production Training

# Create production config
cat > configs/production.yaml << EOF
lora:
  rank: 32
  alpha: 64
  dropout: 0.1
  target_modules: ["q_proj", "k_proj", "v_proj", "fc1", "fc2"]
  apply_to_vision_encoder: true
  apply_to_mask_decoder: true
  apply_to_detr_encoder: true
  apply_to_detr_decoder: true

training:
  batch_size: 2
  num_epochs: 50
  learning_rate: 3e-5
  weight_decay: 0.01

output:
  output_dir: "outputs/production"
EOF

# Train (single GPU)
python3 train_sam3_lora_native.py --config configs/production.yaml

# Train (multi-GPU - 2 GPUs)
python3 train_sam3_lora_native.py --config configs/production.yaml --device 0 1

# Train (multi-GPU - 4 GPUs)
python3 train_sam3_lora_native.py --config configs/production.yaml --device 0 1 2 3

Example 3: Multi-GPU Training

# Quick 2-GPU training
python3 train_sam3_lora_native.py \
  --config configs/full_lora_config.yaml \
  --device 0 1

# 4-GPU training for large datasets
python3 train_sam3_lora_native.py \
  --config configs/full_lora_config.yaml \
  --device 0 1 2 3

# Use specific GPUs (e.g., skip GPU 1)
python3 train_sam3_lora_native.py \
  --config configs/full_lora_config.yaml \
  --device 0 2 3

# With custom master port (if default 29500 is in use)
python3 train_sam3_lora_native.py \
  --config configs/full_lora_config.yaml \
  --device 0 1 \
  --master_port 29501

Tips for Multi-GPU Training:

Effective batch size = batch_size × num_gpus
Learning rate can be scaled: lr × num_gpus (optional, try both)
Memory per GPU stays the same as single-GPU
Training time scales roughly linearly with GPU count

Example 4: Programmatic Training

from train_sam3_lora_native import SAM3TrainerNative

# Create trainer
trainer = SAM3TrainerNative("configs/full_lora_config.yaml")

# Train
trainer.train()

# Weights saved to: outputs/sam3_lora_full/lora_weights.pt

Example 5: Batch Inference with Text Prompts

from infer_sam import SAM3LoRAInference
from pathlib import Path

# Initialize once
inferencer = SAM3LoRAInference(
    config_path="configs/full_lora_config.yaml",
    weights_path="outputs/sam3_lora_full/best_lora_weights.pt"
)

# Process multiple images with same prompt
image_dir = Path("test_images")
output_dir = Path("predictions")
output_dir.mkdir(exist_ok=True)

for img_path in image_dir.glob("*.jpg"):
    predictions = inferencer.predict(
        str(img_path),
        text_prompts=["car"]
    )

    output_path = output_dir / f"{img_path.stem}_pred.png"
    inferencer.visualize(
        predictions,
        str(output_path)
    )

    print(f"✓ Processed {img_path.name}")

# Process with multiple prompts per image
for img_path in image_dir.glob("*.jpg"):
    # Detect multiple object types at once
    predictions = inferencer.predict(
        str(img_path),
        text_prompts=["crack", "defect", "damage"]
    )

    output_path = output_dir / f"{img_path.stem}_multi.png"
    inferencer.visualize(predictions, str(output_path))

    # Print summary
    for idx in range(3):
        result = predictions[idx]
        print(f"  {result['prompt']}: {result['num_detections']} detections")

Advanced Usage

Apply LoRA to Custom Models

from lora_layers import LoRAConfig, apply_lora_to_model, count_parameters
import torch.nn as nn

# Your PyTorch model
model = YourModel()

# Configure LoRA
lora_config = LoRAConfig(
    rank=8,
    alpha=16,
    dropout=0.1,
    target_modules=["q_proj", "k_proj", "v_proj"],
    apply_to_vision_encoder=True,
    apply_to_text_encoder=False,
    apply_to_geometry_encoder=False,
    apply_to_detr_encoder=False,
    apply_to_detr_decoder=False,
    apply_to_mask_decoder=False,
)

# Apply LoRA (automatically freezes base model)
model = apply_lora_to_model(model, lora_config)

# Check trainable parameters
stats = count_parameters(model)
print(f"Trainable: {stats['trainable_parameters']:,} / {stats['total_parameters']:,}")
print(f"Percentage: {stats['trainable_percentage']:.2f}%")

# Train normally
optimizer = torch.optim.AdamW(
    [p for p in model.parameters() if p.requires_grad],
    lr=1e-4
)

Save and Load LoRA Weights

from lora_layers import save_lora_weights, load_lora_weights

# Save only LoRA parameters (small file!)
save_lora_weights(model, "my_lora_weights.pt")

# Load into new model
load_lora_weights(model, "my_lora_weights.pt")

Project Structure

sam3_lora/
├── configs/
│   └── full_lora_config.yaml      # Default training config
├── data/                          # COCO format dataset
│   ├── train/
│   │   ├── img001.jpg             # Training images
│   │   ├── img002.jpg
│   │   └── _annotations.coco.json # COCO annotations
│   ├── valid/
│   │   ├── img001.jpg             # Validation images
│   │   ├── img002.jpg
│   │   └── _annotations.coco.json # COCO annotations
│   └── test/
│       ├── img001.jpg             # Test images (optional)
│       └── _annotations.coco.json # COCO annotations
├── outputs/
│   └── sam3_lora_full/
│       ├── best_lora_weights.pt   # Best model (lowest val loss)
│       └── last_lora_weights.pt   # Last epoch model
├── sam3/                          # SAM3 model library
├── lora_layers.py                 # LoRA implementation
├── train_sam3_lora_native.py      # Training script (computes validation loss only)
├── validate_sam3_lora.py          # Full evaluation script (mAP, cgF1, NMS)
├── validate_single_image.py       # Single image validation with visualization
├── infer_sam.py                   # Inference script (recommended)
├── inference_lora.py              # Legacy inference script
├── README_INFERENCE.md            # Detailed inference guide
└── README.md                      # This file

Troubleshooting

Common Issues

1. Hugging Face Authentication Error

Error: Access denied to facebook/sam3

Solution:

Make sure you've requested access at https://huggingface.co/facebook/sam3
Wait for approval (check your email)
Run huggingface-cli login and paste your token
Or set: export HF_TOKEN="your_token"

2. Import Errors

# Make sure package is installed
pip install -e .

3. CUDA Out of Memory

# Reduce batch size and rank in config
training:
  batch_size: 1

lora:
  rank: 4

4. Very Low Loss (< 0.001)

Model may be overfitting
Reduce LoRA rank
Add more dropout
Check if base model is properly frozen

5. Loss Not Decreasing

Increase learning rate
Increase LoRA rank
Train for more epochs
Check data quality

6. Wrong Number of Trainable Parameters

Expected: ~0.5-2% (for rank 4-16)
If you see 63%: Base model not frozen (bug fixed in latest version)

7. No Validation Data

⚠️ No validation data found - training without validation

Solution:

Create data/valid/ directory with same structure as data/train/
Split your data: ~80% train, ~20% validation
Training will work without validation but you won't see validation metrics

8. Annotation Format Errors

FileNotFoundError: COCO annotation file not found: /path/to/data/train/_annotations.coco.json

Solution:

Ensure your data is in COCO format with _annotations.coco.json in each split folder
Each split (train/valid/test) needs its own annotation file
Images should be in the same directory as the annotation file
Supported segmentation formats: polygon lists or RLE dictionaries

9. Want to See mAP/cgF1 During Training? Solution:

Training only computes validation loss (fast, following SAM3's approach)
After training, run validate_sam3_lora.py for full metrics with NMS
This approach significantly speeds up training while still monitoring overfitting
Validation loss is sufficient to detect overfitting and select best model

10. Grid-Like Bounding Box Pattern in Inference

Problem: Visualization shows many overlapping boxes forming a grid pattern

Cause: Missing NMS (Non-Maximum Suppression) filtering. SAM3 uses 100+ object queries that produce many overlapping predictions.

Solution:

# Use lower NMS IoU threshold to remove overlapping boxes
python3 infer_sam.py \
  --config configs/full_lora_config.yaml \
  --image test.jpg \
  --prompt "object" \
  --nms-iou 0.3 \
  --output clean_output.png

NMS IoU values:

0.3 - Aggressive filtering (fewer boxes, cleaner output)
0.5 - Default, balanced
0.7 - Keep more overlapping detections

Performance Benchmarks

Configuration	Trainable Params	Checkpoint Size	GPU Memory	Speed
Minimal (r=4)	~0.2%	~10 MB	8 GB	Fast
Balanced (r=8)	~0.5%	~20 MB	12 GB	Medium
Full (r=16)	~1.0%	~40 MB	16 GB	Slower
Maximum (r=32)	~2.0%	~80 MB	20 GB	Slowest

Benchmarks on NVIDIA RTX 3090

Troubleshooting & Performance Optimization

Problem: Out of Memory (OOM) During Training

Symptoms:

Killed (exit code 137)
Training crashes after a few batches

Solutions (based on SAM3 original approach):

Use Light LoRA Config (Recommended for GPUs with <24GB VRAM):
```
python train_sam3_lora_native.py --config configs/light_lora_config.yaml
```
This config:
- Reduces LoRA rank from 32 to 16
- Applies LoRA to fewer modules (skips vision encoder, geometry encoder)
- Uses batch_size=2 instead of 1
- ~60% less memory usage!
Reduce Batch Size in configs/full_lora_config.yaml:
```
training:
  batch_size: 2  # Current optimized value (was 8, then 1)
  gradient_accumulation_steps: 8  # Maintains effective batch size of 16
```
Note: batch_size=2 is better than batch_size=1 because it reduces gradient variance and leads to more stable training.

Clear GPU Memory before training:

nvidia-smi  # Check GPU usage
pkill python3  # Kill hanging processes
python train_sam3_lora_native.py --config configs/light_lora_config.yaml

Understanding SAM3 Loss Values

Loss values of 110-159 are NORMAL for SAM3! ✅

SAM3 uses weighted multi-component loss following the original implementation:

loss_mask: 200.0 (dominant component)
loss_ce: 20.0 (classification)
loss_dice: 10.0 (dice coefficient)
loss_bbox: 5.0 (bounding box)
loss_giou: 2.0 (generalized IoU)
presence_loss: 20.0 (object presence)

Example calculation:

# Typical unweighted losses at start:
loss_mask: 0.5 × 200 = 100
loss_ce: 0.5 × 20 = 10
loss_dice: 0.5 × 10 = 5
# ... others contribute ~15
Total: ~130 (NORMAL!)

What to monitor:

✅ Trending downward: Loss 150 → 120 → 100 → 80 (good!)
❌ Erratic jumps: Loss 150 → 100 → 200 → 90 (batch_size too small, see fixes below)
❌ Stuck: Loss stays at 150 for many epochs (learning rate too low)

If loss is highly fluctuating (e.g., 169 → 141 → 242 → 182):

Increase batch_size from 1 to 2 (reduces gradient variance)
Check data_dir in config points to correct location
Reduce LoRA rank if getting OOM errors (64 → 32)

Problem: Low mAP/cgF1 Metrics

Comparison to SAM3 Original Validation:

Aspect	SAM3 Original	Our Implementation
Primary Metric	cgF1 (concept-level F1)	mAP + cgF1
NMS Filtering	Built into inference	Explicit apply_sam3_nms()
Evaluation	Loss-based during training	Full segmentation metrics
Resolution	Fixed (dataset-dependent)	Flexible (288 or original)

Solutions:

Check Dataset Quality:

# Your dataset is small:
# Training: 778 images, 1631 annotations
# Validation: 152 images, 298 annotations

# For good performance, SAM3 typically uses:
# - Training: 10K+ images
# - More annotations per image (2-3 average is low)

Adjust NMS Thresholds in validate_sam3_lora.py:

# Line 865-867: Current settings
prob_threshold=0.3        # Try 0.4-0.5 (stricter)
nms_iou_threshold=0.7     # Try 0.5-0.6 (more aggressive merging)
max_detections=100        # Reduce to 50 if over-predicting

Train Longer (but not too long):

# Updated in configs/full_lora_config.yaml
num_epochs: 100  # Reduced from 500 (small dataset overfits quickly)
eval_steps: 100  # More frequent validation to catch overfitting

Use Lighter LoRA for small datasets:
- Full LoRA (11.8M params) may overfit on 778 images
- Light LoRA (~5M params) generalizes better
- Try: configs/light_lora_config.yaml

Problem: Training is Very Slow

Solutions:

Increase Workers:

training:
  num_workers: 2  # Already optimized from 1

Use Smaller Validation Subset during training:
- Edit train_sam3_lora_native.py to validate on first 50 images only
- Do full validation post-training

Reduce Validation Frequency:

training:
  eval_steps: 200  # Increase if validation takes too long

Expected Performance Targets

Based on SAM3 fine-tuning benchmarks and your dataset size:

After 10 epochs (with light_lora_config.yaml):

Training loss: 10-20
mAP@50: 0.15-0.30
cgF1@50: 0.25-0.40

After 50 epochs:

Training loss: 5-10
mAP@50: 0.30-0.50
cgF1@50: 0.40-0.60

After 100 epochs (optimal):

Training loss: 3-7
mAP@50: 0.40-0.65
cgF1@50: 0.50-0.70

Note: Small dataset (778 images) limits max achievable performance. For mAP >0.7, you typically need 5K+ training images.

Recommended Training Strategy

Quick Start (Testing):

# 1. Use light config for fast iteration
python train_sam3_lora_native.py --config configs/light_lora_config.yaml

# 2. Monitor first 10 batches (loss should decrease)
# 3. Train for 20-30 epochs first
# 4. Run validation:
python validate_sam3_lora.py \
  --config configs/light_lora_config.yaml \
  --weights outputs/sam3_lora_light/checkpoint_epoch_30.pt \
  --val_data_dir /workspace/data2/valid

Full Training (Production):

# 1. If light config works well, try full config
python train_sam3_lora_native.py --config configs/full_lora_config.yaml

# 2. Train for 100 epochs max
# 3. Validate at checkpoints: 20, 50, 100 epochs
# 4. Use best performing checkpoint

Citation

If you use this work, please cite:

@software{sam3_lora,
  title = {SAM3-LoRA: Low-Rank Adaptation for Fine-Tuning},
  author = {AI Research Group, KMUTT},
  year = {2025},
  organization = {King Mongkut's University of Technology Thonburi},
  url = {https://github.com/yourusername/sam3_lora}
}

References

LoRA: Hu et al., 2021 - "LoRA: Low-Rank Adaptation of Large Language Models"
SAM: Kirillov et al., 2023 - "Segment Anything"
SAM3: Meta AI Research

Credits

Made by AI Research Group, KMUTT King Mongkut's University of Technology Thonburi

License

This project is licensed under Apache 2.0. See LICENSE for details.

Version: 1.0.0 Python: 3.8+ PyTorch: 2.0+

Built with ❤️ for the research community

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Name		Name	Last commit message	Last commit date
Latest commit History 67 Commits
asset		asset
configs		configs
sam3		sam3
sam3_lora		sam3_lora
sam3_lora_configs		sam3_lora_configs
src		src
.gitignore		.gitignore
CLI_TRAINING_GUIDE.md		CLI_TRAINING_GUIDE.md
LORA_IMPLEMENTATION_GUIDE.md		LORA_IMPLEMENTATION_GUIDE.md
Machinery.zip		Machinery.zip
PROJECT_STRUCTURE.txt		PROJECT_STRUCTURE.txt
PROJECT_SUMMARY.md		PROJECT_SUMMARY.md
QUICK_SUMMARY.md		QUICK_SUMMARY.md
README.dataset.txt		README.dataset.txt
README.md		README.md
README.roboflow.txt		README.roboflow.txt
README_INFERENCE.md		README_INFERENCE.md
README_LORA_IMPLEMENTATION.md		README_LORA_IMPLEMENTATION.md
analyze_loss.py		analyze_loss.py
check_text_encoding.py		check_text_encoding.py
compare_lora_base.py		compare_lora_base.py
compare_lora_base_batch.py		compare_lora_base_batch.py
convert_roboflow_to_coco.py		convert_roboflow_to_coco.py
diagnose_training.md		diagnose_training.md
infer_sam.py		infer_sam.py
inference.py		inference.py
inference_lora.py		inference_lora.py
lora_layers.py		lora_layers.py
loral.yaml		loral.yaml
prepare_data.py		prepare_data.py
prepare_data_split.py		prepare_data_split.py
quick_start.sh		quick_start.sh
quickstart.sh		quickstart.sh
requirements.txt		requirements.txt
run_5_image_comparison.sh		run_5_image_comparison.sh
setup.py		setup.py
test_aux_outputs.py		test_aux_outputs.py
test_lora_injection.py		test_lora_injection.py
train.py		train.py
train_native.py		train_native.py
train_sam3_lora.py		train_sam3_lora.py
train_sam3_lora_native.py		train_sam3_lora_native.py
train_sam3_lora_with_categories.py		train_sam3_lora_with_categories.py
train_simple.py		train_simple.py
train_standalone.py		train_standalone.py
validate_sam3_lora.py		validate_sam3_lora.py
verify_gt_transforms.py		verify_gt_transforms.py

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SAM3-LoRA: Efficient Fine-Tuning with Low-Rank Adaptation

Overview

Recent Updates

Why Use This?

What is LoRA?

Architecture

Installation

Prerequisites

Install

Verification

Quick Start

1. Prepare Your Data

2. Train Your Model

3. Run Inference

Training

Basic Training

Multi-GPU Training

Custom Configuration

Model Checkpointing

Validation

Overview

Quick Validation

Validation Metrics Explained

Advanced Validation Options

Validation Parameters Reference

Interpreting Results

Why Separate Evaluation?

Training Tips

Inference

Command Line

NMS (Non-Maximum Suppression)

Text Prompts

Inference Parameters

Python API

Configuration

LoRA Parameters

Component Flags

Preset Configurations

Real-World Example: Concrete Crack Detection

Test Results: Road Damage Detection

Validation Metrics Comparison

Visual Comparison

Examples

Example 1: Quick Test (5 Epochs)

Example 2: Production Training

Example 3: Multi-GPU Training

Example 4: Programmatic Training

Example 5: Batch Inference with Text Prompts

Advanced Usage

Apply LoRA to Custom Models

Save and Load LoRA Weights

Project Structure

Troubleshooting

Common Issues

Performance Benchmarks

Troubleshooting & Performance Optimization

Problem: Out of Memory (OOM) During Training

Understanding SAM3 Loss Values

Problem: Low mAP/cgF1 Metrics

Problem: Training is Very Slow

Expected Performance Targets

Recommended Training Strategy

Citation

References

Credits

License

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