Initial FactoryVision AI model package (private review)

README.md

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+---
+language:
+- en
+pipeline_tag: image-classification
+library_name: pytorch
+tags:
+- industrial-vision
+- factoryvision-ai
+- casting-defect-detection
+- transfer-learning
+- mobilenetv3
+- education
+---
+# FactoryVision AI — MobileNetV3-Small Casting Inspector
+
+**FactoryVision AI — Credits and Attribution**
+
+**Original Developer & Model Author:** Thevanth Rajamohan  
+**Technical Review, Code Contributions & Model Publication Support:** Swapin Vidya  
+**Publishing Organisation:** TechSphere Consultancy Services Private Limited  
+**Hugging Face:** https://huggingface.co/TechsphereAI  
+**Official Website:** https://techsphereconsultancy.com/
+
+**Project Provenance:** FactoryVision AI was originally developed by Thevanth Rajamohan. Swapin Vidya contributed technical review, detection-metrics improvements, testing, model publication preparation and documentation. The models are being prepared for publication under TechSphere Consultancy Services Private Limited's Hugging Face namespace, with the original developer's permission.
+
+**Original GitHub Repository:** https://github.com/Theivanth/Factory-Vision-AI  
+**Merged contribution by Swapin Vidya:** https://github.com/Theivanth/Factory-Vision-AI/pull/1
+
+## Purpose and limitations
+Educational transfer-learning binary casting inspection (`def_front`, `ok_front`). **Human review is required.** Not a validated or certified automated quality acceptance system.
+
+## Architecture and files
+- torchvision MobileNetV3-Small, pretrained backbone and replaced classification head, fine-tuned on casting images
+- Weights: `model.pth` (archive `checkpoints/casting_mobilenet_v3_small.pth`)
+- Validation-selected best casting architecture in the supplied manifest: `mobilenet_v3_small`
+- Class order in `labels.json`; for architecture and preprocessing see `src/quality.py` in the source repository
+
+## Training dataset
+Real-Life Industrial Dataset of Casting Product by Ravirajsinh Dabhi (https://www.kaggle.com/datasets/ravirajsinh45/real-life-industrial-dataset-of-casting-product). The source README identifies CC BY-NC-ND 4.0 dataset restrictions, including non-commercial and no-derivatives clauses. Rights to redistribute trained model weights remain to be confirmed; do not redistribute source images.
+
+## Held-out test performance (from supplied Kaggle output)
+- Accuracy: **0.9944**
+- Macro precision: **0.9932**
+- Macro recall: **0.9948**
+- Macro F1: **0.9940**
+- Test confusion matrix (rows true, columns predicted; class order `def_front`, `ok_front`): `[[450, 3], [1, 261]]`
+- Mean inference latency reported by Kaggle: **0.395 ms** (environment-specific, not a deployed CPU benchmark)
+
+Metrics are from the saved training output, not independently reproduced. The test split did not determine architecture selection; the selection metric was validation macro F1.
+
+## Inference and dependencies
+Use the model-loader and preprocessors from `src/quality.py` of https://github.com/Theivanth/Factory-Vision-AI . The checkpoint is a PyTorch artifact, **not** a drop-in Hugging Face Transformers model. Avoid loading untrusted serialized checkpoints.
+
+## Licensing and distribution
+The source code's MIT licence is distinct from trained-weight rights. **Keep private until the casting dataset conditions and weight publication rights are clarified.** No public checkpoint licence is asserted.

SOURCE.md

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+Source: https://github.com/Theivanth/Factory-Vision-AI
+Original Project Developer and Model Author: Thevanth Rajamohan
+Technical Reviewer, Code Contributor and Hugging Face Publication Support: Swapin Vidya
+Publishing Organisation: TechSphere Consultancy Services Private Limited
+Hugging Face: https://huggingface.co/TechsphereAI
+Official Website: https://techsphereconsultancy.com/
+Swapin Vidya's merged contribution: https://github.com/Theivanth/Factory-Vision-AI/pull/1
+Model checkpoint SHA-256: 2ae61f4261539927721868f85ef99243d3f435dec85cb1476ef398403deebe5f

labels.json

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+[
+  "def_front",
+  "ok_front"
+]

metrics.json

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+{
+  "checkpoint": "casting_mobilenet_v3_small.pth",
+  "validation": {
+    "accuracy": 0.9961948249619482,
+    "precision_macro": 0.9955908289241622,
+    "recall_macro": 0.9966755319148937,
+    "macro_f1": 0.9961178724995878,
+    "per_class_precision": {
+      "def_front": 1.0,
+      "ok_front": 0.9911816578483245
+    },
+    "per_class_recall": {
+      "def_front": 0.9933510638297872,
+      "ok_front": 1.0
+    },
+    "per_class_f1": {
+      "def_front": 0.9966644429619747,
+      "ok_front": 0.995571302037201
+    },
+    "confusion_matrix": [
+      [
+        747,
+        5
+      ],
+      [
+        0,
+        562
+      ]
+    ],
+    "classes": [
+      "def_front",
+      "ok_front"
+    ],
+    "mean_inference_latency_ms": 0.3442122115672786
+  },
+  "test": {
+    "accuracy": 0.9944055944055944,
+    "precision_macro": 0.9932095343680709,
+    "recall_macro": 0.9947803447752894,
+    "macro_f1": 0.9939853292506478,
+    "per_class_precision": {
+      "def_front": 0.9977827050997783,
+      "ok_front": 0.9886363636363636
+    },
+    "per_class_recall": {
+      "def_front": 0.9933774834437086,
+      "ok_front": 0.9961832061068703
+    },
+    "per_class_f1": {
+      "def_front": 0.995575221238938,
+      "ok_front": 0.9923954372623575
+    },
+    "confusion_matrix": [
+      [
+        450,
+        3
+      ],
+      [
+        1,
+        261
+      ]
+    ],
+    "classes": [
+      "def_front",
+      "ok_front"
+    ],
+    "mean_inference_latency_ms": 0.395475506294432
+  }
+}

model.pth

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+oid sha256:2ae61f4261539927721868f85ef99243d3f435dec85cb1476ef398403deebe5f
+size 6205925