TechsphereAI
/

FactoryVision-Casting-CNN

@@ -9,71 +9,68 @@ tags:
 - casting-defect-detection
 - education
 ---
-# 🏭 FactoryVision AI
-## Casting Inspection · Custom CNN
-> **Educational prototype** · Binary image classification · **Human-reviewed inspection only**
-| Model | Task | Framework | Weights |
-|---|---|---|---|
-| FactoryVision AI Custom CNN | Casting defect classification | PyTorch | `model.pth` |
-## 📊 Held-out test results
-| Metric | Reported result |
-|---|---:|
-| Accuracy | **0.9860** |
-| Macro precision | **0.9849** |
-| Macro recall | **0.9849** |
-| Macro F1 | **0.9849** |
-| Mean inference latency | **0.608 ms** |
-**Confusion matrix** — rows are true labels, columns are predictions; class order: `def_front`, `ok_front`.
-```text
-[[448, 5],
- [  5, 257]]
-```
-Metrics and latency are reported from the supplied Kaggle output, not independently reproduced. Latency is environment/measurement specific and is not a target CPU benchmark. The model is not validated on new factory deployments.
-## 🎯 Purpose and limitations
-Educational two-class classification of casting images (`def_front` = defective; `ok_front` = acceptable). For **human-reviewed prototype inspection only**; not an industrial release decision or certified quality system.
-## 🧠 Model details
-- Custom PyTorch CNN trained from scratch; see `src/quality.py` in the source repository for architecture and preprocessing
-- Packaged weights: `model.pth` (archive path `checkpoints/casting_cnn.pth`)
-- Exact ordered labels: `labels.json`
-- Use the project's original loader and preprocessing rather than assuming a generic Hugging Face Transformer loader can use the `.pth` file.
-## 🗂️ Training data
-Real-Life Industrial Dataset of Casting Product by Ravirajsinh Dabhi ([Kaggle dataset](https://www.kaggle.com/datasets/ravirajsinh45/real-life-industrial-dataset-of-casting-product)). The source README identifies its Kaggle licence as **CC BY-NC-ND 4.0**, including non-commercial and no-derivatives restrictions. Rights to publish dataset-trained checkpoint weights require separate verification; model-developer consent does not settle third-party licensing. No input images are redistributed here.
-## 🔧 Inference and dependencies
-The checkpoint is intended to be loaded using the matching architecture from [`src/quality.py`](https://github.com/Theivanth/Factory-Vision-AI) in the source repository. Do **not** call arbitrary `torch.load` on untrusted checkpoints. The project has known image-size, normalisation, and class-order requirements; check the current source for the exact implementation before inference.
-## ⚖️ Licensing and distribution
-The source project has an MIT licence for **its code**, not necessarily the checkpoint. **Keep this repository private until the casting dataset's non-commercial/no-derivatives conditions and trained-weight publication rights have been resolved.** No model-weight licence is asserted in this card.
----
-## 🤝 Credits and attribution
-| Role | Contributor |
-|---|---|
-| Original Developer & Model Author | Theivanth Rajamohan |
-| Technical Reviewer, Code Contributor & Publication Support | [Swapin Vidya](https://huggingface.co/Swapinvidya) |
-| Project Contributor | [Rajah Shanmugasundaram](https://huggingface.co/rajahshan) |
-| Publishing Organisation | TechSphere Consultancy Services Private Limited |
-**Project provenance:** FactoryVision AI was originally developed by Theivanth Rajamohan. [Swapin Vidya](https://huggingface.co/Swapinvidya) 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.
-- 🤗 [TechSphereAI on Hugging Face](https://huggingface.co/TechsphereAI)
-- 🌐 [Official website](https://techsphereconsultancy.com/)
-- 💻 [Original GitHub project](https://github.com/Theivanth/Factory-Vision-AI)
-- 🔀 [Swapin Vidya's merged contribution](https://github.com/Theivanth/Factory-Vision-AI/pull/1)

 - casting-defect-detection
 - education
 ---
+<div align="center">
+  <img src="hero.svg" alt="FactoryVision AI custom CNN for casting inspection" width="100%">
+  <p><strong>Educational prototype</strong> &nbsp;|&nbsp; Binary image classification &nbsp;|&nbsp; Human-reviewed inspection only</p>
+</div>
+<h2>Model overview</h2>
+<table>
+  <thead><tr><th>Model</th><th>Task</th><th>Framework</th><th>Weights</th></tr></thead>
+  <tbody><tr><td>FactoryVision AI Custom CNN</td><td>Casting defect classification</td><td>PyTorch</td><td><code>model.pth</code></td></tr></tbody>
+</table>
+<h2>Held-out test results</h2>
+<table>
+  <thead><tr><th>Metric</th><th>Reported result</th></tr></thead>
+  <tbody>
+    <tr><td>Accuracy</td><td><strong>0.9860</strong></td></tr>
+    <tr><td>Macro precision</td><td><strong>0.9849</strong></td></tr>
+    <tr><td>Macro recall</td><td><strong>0.9849</strong></td></tr>
+    <tr><td>Macro F1</td><td><strong>0.9849</strong></td></tr>
+    <tr><td>Mean inference latency</td><td><strong>0.608 ms</strong></td></tr>
+  </tbody>
+</table>
+<p><strong>Confusion matrix</strong> — rows are true labels, columns are predictions; class order: <code>def_front</code>, <code>ok_front</code>.</p>
+<pre><code>[[448, 5],
+ [  5, 257]]</code></pre>
+<p>Metrics and latency are reported from the supplied Kaggle output, not independently reproduced. Latency is environment/measurement specific and is not a target CPU benchmark. The model is not validated on new factory deployments.</p>
+<h2>Purpose and limitations</h2>
+<p>Educational two-class classification of casting images (<code>def_front</code> = defective; <code>ok_front</code> = acceptable). For <strong>human-reviewed prototype inspection only</strong>; not an industrial release decision or certified quality system.</p>
+<h2>Model details</h2>
+<ul>
+  <li>Custom PyTorch CNN trained from scratch; see <code>src/quality.py</code> in the source repository for architecture and preprocessing</li>
+  <li>Packaged weights: <code>model.pth</code> (archive path <code>checkpoints/casting_cnn.pth</code>)</li>
+  <li>Exact ordered labels: <code>labels.json</code></li>
+  <li>Use the project's original loader and preprocessing rather than assuming a generic Hugging Face Transformer loader can use the <code>.pth</code> file.</li>
+</ul>
+<h2>Training data</h2>
+<p>Real-Life Industrial Dataset of Casting Product by Ravirajsinh Dabhi (<a href="https://www.kaggle.com/datasets/ravirajsinh45/real-life-industrial-dataset-of-casting-product">Kaggle dataset</a>). The source README identifies its Kaggle licence as <strong>CC BY-NC-ND 4.0</strong>, including non-commercial and no-derivatives restrictions. Rights to publish dataset-trained checkpoint weights require separate verification; model-developer consent does not settle third-party licensing. No input images are redistributed here.</p>
+<h2>Inference and dependencies</h2>
+<p>The checkpoint is intended to be loaded using the matching architecture from <a href="https://github.com/Theivanth/Factory-Vision-AI"><code>src/quality.py</code> in the source repository</a>. Do <strong>not</strong> call arbitrary <code>torch.load</code> on untrusted checkpoints. The project has known image-size, normalisation, and class-order requirements; check the current source for the exact implementation before inference.</p>
+<h2>Licensing and distribution</h2>
+<p>The source project has an MIT licence for <strong>its code</strong>, not necessarily the checkpoint. <strong>Keep this repository private until the casting dataset's non-commercial/no-derivatives conditions and trained-weight publication rights have been resolved.</strong> No model-weight licence is asserted in this card.</p>
+<hr>
+<h2>Credits and attribution</h2>
+<table>
+  <thead><tr><th>Role</th><th>Contributor</th></tr></thead>
+  <tbody>
+    <tr><td>Original Developer &amp; Model Author</td><td>Theivanth Rajamohan</td></tr>
+    <tr><td>Technical Reviewer, Code Contributor &amp; Publication Support</td><td><a href="https://huggingface.co/Swapinvidya">Swapin Vidya</a></td></tr>
+    <tr><td>Project Contributor</td><td><a href="https://huggingface.co/rajahshan">Rajah Shanmugasundaram</a></td></tr>
+    <tr><td>Publishing Organisation</td><td>TechSphere Consultancy Services Private Limited</td></tr>
+  </tbody>
+</table>
+<p><strong>Project provenance:</strong> FactoryVision AI was originally developed by Theivanth Rajamohan. <a href="https://huggingface.co/Swapinvidya">Swapin Vidya</a> 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.</p>
+<ul>
+  <li><a href="https://huggingface.co/TechsphereAI">TechSphereAI on Hugging Face</a></li>
+  <li><a href="https://techsphereconsultancy.com/">Official website</a></li>
+  <li><a href="https://github.com/Theivanth/Factory-Vision-AI">Original GitHub project</a></li>
+  <li><a href="https://github.com/Theivanth/Factory-Vision-AI/pull/1">Swapin Vidya's merged contribution</a></li>
+</ul>

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