📊 Performance & Inference Pipeline Improvements (v1.2.0)
This release marks a fundamental shift in the inference architecture. By moving to a memory-native pipeline and the YOLO26 backbone, we have slashed the error rate by 50% and virtually eliminated I/O bottlenecks.
📈 Performance Benchmarks
Accuracy (Exact-Match)
Tested against the v1.1.0 baseline using
| Version | Correct | Total | Accuracy |
|---|---|---|---|
| v1.1.0 | 1258 | 1300 | 96.8% |
| v1.2.0 | 1279 | 1300 | 98.4% |
The YOLO26 Advantage: The transition to the new backbone with Label Anchoring solved "trade-split" errors where the model previously struggled to distinguish between incoming and outgoing sides in tight or non-standard crops.
Inference Latency (Batch = 1, E2E)
Measured on an RTX 5070. E2E includes the full pipeline from input to structured dictionary.
| Input Method | Median (ms) | p95 (ms) |
|---|---|---|
| NumPy Array / Bytes | 20.5 | 35.0 |
| File Path (v1.2.0) | 28.0 | 47.4 |
| v1.1.0 (File Path) | 36.8 | 73.4 |
Note
By supporting direct byte streams, v1.2.0 treats images as uncompressed data internally. This bypasses traditional disk I/O overhead, enabling sub-25ms response times for high-frequency applications.
🛠️ Internal Improvements
- YOLO26 Architecture: Replaced the previous detection backbone. Improved handling of box overlaps prevents duplicate item counting in cluttered trade windows.
- Zero-Latency I/O: Added native support for
bytesandnp.ndarray. You can now feed the model directly from a video stream or network request without saving to disk first. - Fully Offline Operation: The base CLIP weights are now pulled and cached locally during the first initialization. Subsequent runs require zero internet connectivity.
🔍 Evaluation Criteria
- Exact-Match: A "Correct" result requires every Item ID, Name, and Robux value to match the ground truth perfectly.
- Environment: All benchmarks were performed after a 5-iteration warm-up to ensure CUDA kernels were fully resident in memory.