- Test range of thresholds to find optimal F1
- Support both baseline and fine-tuned models
- Option for max vs mean similarity aggregation
- Output results table with TP/FP/FN/precision/recall/F1
- Add threshold selection section with similarity distribution analysis
- Document that fine-tuned model needs threshold 0.82 (vs baseline 0.75)
- Add table comparing baseline vs fine-tuned distributions
- Update test commands to include correct thresholds
- Reference analyze_similarity_distribution.sh for threshold optimization
- Add --similarity-details flag to test_logo_detection.py
- Track true positive, false positive, and missed detection similarities
- Compute distribution statistics (min, max, mean, stddev, percentiles)
- Analyze overlap between TP and FP distributions
- Suggest optimal threshold based on data
- Show per-detection breakdown with top-5 matches
- Create analyze_similarity_distribution.sh wrapper script
- Supports baseline, finetuned, or both models
- Saves output to similarity_analysis/ directory
The from_pretrained method was applying LoRA twice:
1. In the constructor via lora_r parameter
2. When loading with PeftModel.from_pretrained()
Now creates model with lora_r=0 and loads LoRA weights separately.
Note: Warning about "missing adapter keys" for layers 0-11 is expected
since those layers are frozen and don't have LoRA adapters.
Previously the trainer saved a new "best" model if either separation
OR loss improved, with loss checked as a fallback. This caused
confusing behavior where models with lower separation could overwrite
better models.
Now only separation (gap between positive and negative similarity) is
used to determine the best model, which is the key metric for
contrastive learning quality.
Implement contrastive learning with LoRA to fine-tune CLIP's vision
encoder on LogoDet-3K dataset for improved logo embedding similarity.
New training module (training/):
- config.py: TrainingConfig dataclass with all hyperparameters
- dataset.py: LogoContrastiveDataset with logo-level splits
- model.py: LogoFineTunedCLIP wrapper with LoRA support
- losses.py: InfoNCE, TripletLoss, SupConLoss implementations
- trainer.py: Training loop with mixed precision and checkpointing
- evaluation.py: EmbeddingEvaluator for validation metrics
New scripts:
- train_clip_logo.py: Main training entry point
- export_model.py: Export to HuggingFace-compatible format
Configurations:
- configs/jetson_orin.yaml: Optimized for Jetson Orin AGX
- configs/cloud_rtx4090.yaml: Optimized for 24GB cloud GPUs
- configs/cloud_a100.yaml: Optimized for 80GB cloud GPUs
Documentation:
- CLIP_FINETUNING.md: Training guide and usage instructions
- CLOUD_TRAINING.md: Cloud GPU recommendations and cost estimates
Modified:
- logo_detection_detr.py: Add fine-tuned model loading support
- pyproject.toml: Add peft, pyyaml, torchvision dependencies
- Add section explaining how margin works differently in multi-ref vs
margin-only matching, with examples showing why margin-only fails
when using multiple references per logo
- Update run_model_comparison.sh to use optimal threshold (0.70) and
margin (0.05) based on test results
- Add DINOv2 Large model test to comparison script
- Add threshold optimization test analysis to results document
- Add -e/--embedding-model parameter to Key Parameters table
- Add --clear-cache parameter
- Document all 3 test scripts with output file table
- Update project structure with new scripts and analysis doc
- Expand Models section with embedding model options table
- Add note about clearing cache when switching models
- Add test_results_analysis.md for documenting test findings
- Add detailed explanation of mean vs max aggregation methods
- Include concrete example with Nike logo and 5 reference images
- Add decision table for when to use each approach
- Show how min_matching_refs works independently of aggregation
- Update DetectLogosDETR to support both CLIP and DINOv2 models
- Rename clip_model parameter to embedding_model
- Add model type detection for different embedding extraction
- DINOv2 uses CLS token, CLIP uses get_image_features()
- Add -e/--embedding-model argument to test_logo_detection.py
- Include model name in file output header
- Add run_threshold_tests.sh for testing various threshold/margin values
- Add run_model_comparison.sh for comparing CLIP vs DINOv2 models
- Add detailed instructions for LogoDet-3K dataset placement
- Document all test script parameters including new options:
- simple matching method
- --output-file for clean results output
- --use-max-similarity, --positive-samples, --negative-samples
- Add section on running comparison tests with shell script
- Update project structure to include run_comparison_tests.sh
- Add --output-file argument to test_logo_detection.py that appends
only the results summary (no progress indicators) to specified file
- Add write_results_to_file() with detailed header showing test type
and method parameters
- Update run_comparison_tests.sh to use --output-file instead of
tee/redirection, keeping console output separate from file output
- Add find_all_matches() method to DetectLogosDETR that returns all
logos above similarity threshold without any rejection logic
- Add --matching-method simple option to test script
- Update run_comparison_tests.sh to include simple matching as Test 1
- Update documentation to describe simple matching method
The multi-ref matching method was missing a margin check against other
logos, causing excessive false positives. This fix adds:
- margin parameter to find_best_match_multi_ref() that requires the
best logo's score to exceed the second-best by a minimum margin
- Test script now passes --margin to both matching methods
- Updated documentation to reflect margin applies to both methods
Also adds run_comparison_tests.sh to run all three matching methods
and compare results.
Add DETR+CLIP based logo detection library and test framework:
- DetectLogosDETR class for logo detection and matching
- Test script with margin-based and multi-ref matching methods
- Data preparation script for test database
- Documentation for API usage and test methodology
