Inspection Engine is a YOLO11s model fine-tuned for automated PCB (Printed Circuit Board) defect detection. It identifies manufacturing defects such as missing components, solder bridges, lifted pads, and other...
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Inspection Engine is a YOLO11s model fine-tuned for automated PCB (Printed Circuit Board) defect detection. It identifies manufacturing defects such as missing components, solder bridges, lifted pads, and other anomalies in real-time, enabling automated quality control in electronics manufacturing.
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1 QuelleVerifiziert 16. Sept.
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2 Auszügeinspection_engine_final3 (best performing run)| File | Description |
|---|---|
best.pt | Best PyTorch model weights |
best.onnx | ONNX export for cross-platform/production deployment |
from ultralytics import YOLO
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id='devanshty/Inspection-Engine', filename='best.pt')
model = YOLO(model_path)
results = model('pcb_image.jpg')
results[0].show()
import onnxruntime as ort
import numpy as np
from huggingface_hub import hf_hub_download
onnx_path = hf_hub_download(repo_id='devanshty/Inspection-Engine', filename='best.onnx')
session = ort.InferenceSession(onnx_path)
# Prepare input (1, 3, H, W) float32 normalized
input_name = session.get_inputs()[0].name
outputs = session.run(None, {input_name: np.zeros((1, 3, 640, 640), dtype=np.float32)})
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id='devanshty/Inspection-Engine', filename='best.pt')
--- license: mit tags: - pytorch - onnx - object-detection - yolo - computer-vision - pcb-inspection - industrial --- # Inspection Engine ## Model Description Inspection Engine is a YOLO11s model fine-tuned for automated PCB (Printed Circuit Board) defect detection. It identifies manufacturing defects such as missing components, solder bridges, lifted pads, and other anomalies in real-time, enabling automated quality control in electronics manufacturing. ## Model Architecture - **Base Model**: YOLO11s (Small variant — optimized for speed) - **Framework**: PyTorch + ONNX (for cross-platform deployment) - **Task**: Object Detection (Defect Localization) - **Input**: High-resolution PCB images ## Training Details - **Dataset**: PCB defect inspection dataset with annotated defect regions - **Checkpoint**: `inspection_engine_final3` (best performing run) - **Augmentations**: Mosaic, color jitter, random affine transforms - **Export**: Exported to ONNX for production deployment ## Files | File | Description | |------|-------------| | `best.pt` | Best PyTorch model weights | | `best.onnx` | ONNX export for cross-platform/production deployment | ## Usage ### PyTorch (Ultralytics) ```python from ultralytics import YOLO from huggingface_hub import hf_hub_download model_path = hf_hub_download(repo_id='devanshty/Inspection-Engine', filename='best.pt') model = YOLO(model_path) results = model('pcb_image.jpg') results[0].show() ``` ### ONNX Runtime ```python import onnxruntime as ort import numpy as np from huggingface_hub import hf_hub_download onnx_path = hf_hub_download(repo_id='devanshty/Inspection-Engine', filename='best.onnx') session = ort.InferenceSession(onnx_path) # Prepare input (1, 3, H, W) float32 normalized input_name = session.get_inputs()[0].name outputs = session.run(None, {input_name: np.zeros((1, 3, 640, 640), dtype=np.float32)}) ``` ## Download & Use ```python from huggingface_hub import hf_hub_download model_path = hf_hub_download(repo_id='devanshty/Inspection-Engine', filename='best.pt') ```
Source context: 0 downloads · 0 likes · Pipeline object-detection · Repo devanshty/Inspection-Engine