Upload yolov8n fine-tuned on GC10-DET (DetectionBench)

.gitattributes

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+BoxF1_curve.png filter=lfs diff=lfs merge=lfs -text
+BoxPR_curve.png filter=lfs diff=lfs merge=lfs -text
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+confusion_matrix.png filter=lfs diff=lfs merge=lfs -text
+confusion_matrix_normalized.png filter=lfs diff=lfs merge=lfs -text
+gc10det_yolov8n_showcase.jpg filter=lfs diff=lfs merge=lfs -text
+val_batch0_pred.jpg filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: agpl-3.0
+
+pipeline_tag: object-detection
+
+library_name: ultralytics
+
+datasets:
+  - dronefreak/GC10-DET
+
+tags:
+  - object-detection
+  - detectionbench
+  - ultralytics
+  - pytorch
+  - computer-vision
+  - industrial-inspection
+  - surface-defect-detection
+  - manufacturing-qa
+  - metallic-surface
+  - quality-control
+metrics:
+  - map50
+  - map50-95
+  - precision
+  - recall
+  - f1
+
+base_model: "Ultralytics/YOLOv8"
+
+model-index:
+  - name: YOLOv8n Finetuned on GC10-DET
+    results:
+      - task:
+          type: object-detection
+          name: Object Detection
+        dataset:
+          name: GC10-DET
+          type: gc10det
+        metrics:
+          - type: mAP50
+            value: 73.25
+            name: mAP@50 (test split)
+          - type: mAP50-95
+            value: 38.74
+            name: mAP@50-95 (test split)
+          - type: precision
+            value: 67.99
+            name: Precision (test split)
+          - type: recall
+            value: 70.87
+            name: Recall (test split)
+        source:
+          url: https://github.com/dronefreak/DetectionBench
+          name: DetectionBench
+---
+
+
+# YOLOv8n Finetuned on GC10-DET
+
+Fine-tuned YOLOv8n object detector on the **GC10-DET** benchmark dataset, trained and evaluated as part of [DetectionBench](https://github.com/dronefreak/DetectionBench) -- a framework for reproducibly benchmarking modern object detectors with identical training recipes and evaluation metrics across multiple real-world datasets.
+
+<br>
+
+<!-- ROW 1: Identity & Tech Stack -->
+<div style="display: flex; justify-content: center; align-items: center; gap: 8px; margin-bottom: 8px; flex-wrap: wrap;">
+  <img src="https://img.shields.io/badge/Task-Object_Detection-blue?style=flat-square" alt="Task">
+  <img src="https://img.shields.io/badge/Framework-Ultralytics_YOLO-0aa1a7?style=flat-square" alt="Framework">
+  <img src="https://img.shields.io/badge/Base_Model-YOLOv8n-purple?style=flat-square" alt="Base Model">
+</div>
+
+<!-- ROW 2: Performance Metrics -->
+<div style="display: flex; justify-content: center; align-items: center; gap: 8px; margin-bottom: 8px; flex-wrap: wrap;">
+  <img src="https://img.shields.io/badge/mAP@50-73.25%25-success?style=flat-square" alt="mAP@50">
+  <img src="https://img.shields.io/badge/mAP@50:95-38.74%25-orange?style=flat-square" alt="mAP@50:95">
+  <img src="https://img.shields.io/badge/Params-3.2M-lightgrey?style=flat-square" alt="Params">
+</div>
+
+<!-- ROW 3: Metadata -->
+<div style="display: flex; justify-content: center; align-items: center; gap: 8px; margin-bottom: 24px; flex-wrap: wrap;">
+  <img src="https://img.shields.io/badge/License-AGPL--3.0-lightgrey?style=flat-square" alt="License">
+  <a href="https://github.com/dronefreak/DetectionBench"><img src="https://img.shields.io/badge/Source-DetectionBench-black?style=flat-square" alt="Source"></a>
+</div>
+
+---
+
+## Detection Showcase
+
+<p align="center">
+  <img src="gc10det_yolov8n_showcase.jpg" alt="GC10-DET Detection Demo" width="900">
+</p>
+
+---
+
+## Performance
+
+| Metric     | Score (%)       |
+| ---------- | --------------- |
+| mAP@50     | 73.25     |
+| mAP@50-95  | 38.74   |
+| Precision  | 67.99 |
+| Recall     | 70.87    |
+| F1 Score   | 69.4        |
+| Parameters | 3.2M    |
+| FLOPs      | 8.7B (at 640 px)     |
+
+---
+
+## Evaluation Protocol
+
+Metrics reported in this model card are computed on the GC10-DET **test** split, using DetectionBench's standard evaluation pipeline (`detectionbench-evaluate`).
+
+---
+
+## GC10-DET Model Zoo
+
+Every model DetectionBench has trained and evaluated on GC10-DET so far, for full transparency -- see [DetectionBench](https://github.com/dronefreak/DetectionBench) for the smaller, curated comparison set used on the project README.
+
+| Model                | mAP@50        | mAP@50-95       | Precision         | Recall         |
+| --------------------- | ------------- | --------------- | ----------------- | -------------- |
+| RF-DETR Small | 76.07 | 42.51 | 87.86 | 65.03 |
+| RF-DETR Medium | 75.93 | 41.93 | 78.25 | 67.76 |
+| YOLO26s | 75.77 | 38.15 | 77.16 | 74.07 |
+| YOLO26n | 74.25 | 38.31 | 80.5 | 67.99 |
+| YOLO26m | 73.97 | 36.94 | 75.7 | 68.19 |
+| YOLOv8n | 73.25 | 38.74 | 67.99 | 70.87 |
+| YOLO11s | 72.54 | 35.07 | 72.39 | 66.8 |
+| YOLOv8s | 72.54 | 37.79 | 78.54 | 65.23 |
+| YOLOv8m | 71.88 | 38.8 | 69.77 | 70.84 |
+| YOLO11n | 70.44 | 40.09 | 78.93 | 62.64 |
+| RF-DETR Nano | 70.17 | 38.06 | 77.08 | 71.04 |
+---
+
+## Per-Class Performance
+
+| Class                      | mAP@50          | mAP@50-95         |
+| -------------------------- | --------------- | ----------------- |
+| crease       | 32.63 | 13.23 |
+| crescent_gap       | 96.78 | 61.17 |
+| inclusion       | 40.45 | 9.81 |
+| oil_spot       | 47.72 | 21.42 |
+| punching_hole       | 90.23 | 49.13 |
+| rolled_pit       | 99.5 | 69.65 |
+| silk_spot       | 54.39 | 19.51 |
+| waist_folding       | 94.14 | 56.46 |
+| water_spot       | 87.65 | 57.78 |
+| welding_line       | 89.01 | 29.19 |
+---
+
+## Evaluation Visualizations
+
+### Precision-Recall Curve
+
+![PR Curve](BoxPR_curve.png)
+
+### F1 Curve
+
+![F1 Curve](BoxF1_curve.png)
+
+### Confusion Matrix
+
+![Confusion Matrix](confusion_matrix.png)
+
+### Normalized Confusion Matrix
+
+![Normalized Confusion Matrix](confusion_matrix_normalized.png)
+
+---
+
+## Dataset
+
+This model was trained on **GC10-DET**. For the full dataset description, provenance, license, and citation, see the dataset card:
+
+https://huggingface.co/datasets/dronefreak/GC10-DET
+
+### Classes
+
+* crease
+* crescent_gap
+* inclusion
+* oil_spot
+* punching_hole
+* rolled_pit
+* silk_spot
+* waist_folding
+* water_spot
+* welding_line
+---
+
+## Usage
+
+### Install Dependencies
+
+```bash
+pip install ultralytics huggingface_hub
+```
+
+### Load Model from Hugging Face
+
+```python
+from huggingface_hub import hf_hub_download
+from ultralytics import YOLO
+
+weights = hf_hub_download(
+    repo_id="dronefreak/gc10det-yolov8n",
+    filename="best.pt"
+)
+
+model = YOLO(weights)
+```
+
+### Run Inference
+
+```python
+results = model.predict(
+    source="image.jpg",
+    conf=0.25
+)
+
+results[0].show()
+```
+---
+
+## Training Configuration
+
+| Setting          | Value                           |
+| ---------------- | -------------------------------- |
+| Dataset          | GC10-DET       |
+| Framework        | Ultralytics YOLO                  |
+| Training Toolkit | DetectionBench                   |
+| Epochs (configured max) | 180 |
+| Epochs (actually trained) | 120 |
+| Early Stopping Patience | 25 |
+| Batch Size | auto (Ultralytics AutoBatch) |
+| Image Size | 640 |
+| Optimizer | AdamW |
+| Initial Learning Rate | 0.001 |
+| Seed | 0 |
+---
+
+## Repository Contents
+
+```text
+best.pt
+results.csv
+args.yaml
+BoxPR_curve.png
+BoxF1_curve.png
+BoxP_curve.png
+BoxR_curve.png
+confusion_matrix.png
+confusion_matrix_normalized.png
+val_batch0_pred.jpg
+gc10det_yolov8n_showcase.jpg
+README.md
+```
+
+---
+
+## Related Resources
+
+* [GC10-DET dataset card](https://huggingface.co/datasets/dronefreak/GC10-DET) on Hugging Face
+* [DetectionBench](https://github.com/dronefreak/DetectionBench) -- reproducible benchmarks for modern object detectors on real-world datasets
+* [GC10-DET paper (Sensors 2020, doi:10.3390/s20061562)](https://doi.org/10.3390/s20061562)
+* [GC10-DET GitHub (official data source)](https://github.com/lvxiaoming2019/GC10-DET-Metallic-Surface-Defect-Datasets)
+
+---
+
+## Training Framework
+
+This model was trained using [DetectionBench](https://github.com/dronefreak/DetectionBench), an open-source framework for benchmarking object detectors across multiple real-world datasets with a common pipeline.
+
+Features include:
+
+* A dataset-adapter registry for converting real-world datasets into a canonical format
+* Identical training/evaluation recipes across model families (Ultralytics YOLO/RT-DETR, RF-DETR)
+* Hardware profiling (latency, FPS, VRAM, parameters, FLOPs)
+* One-command reproducibility via versioned Hydra configs
+
+If you find this model useful, please consider starring the repository.
+
+---
+
+## Known Limitations
+
+* No official split: GC10-DET's paper defines no train/valid/test division, so this adapter creates a deterministic seeded 80/10/10 split over the sorted-then-shuffled image list -- results are not directly comparable to a paper that uses a different split.
+* Small dataset: only 1,840 training images (2,300 total) across 10 classes, so absolute scores are more sensitive to the specific split than on the project's larger datasets, and per-class scores on the rarest classes are noisy.
+* Severe class imbalance: `silk_spot` is 24.8% of all boxes, while `crease` has only 74 instances (2.1%) across the whole dataset -- its per-class score is measured on very few examples and should be read with caution.
+* Sparse, mostly single-defect images: 1.55 instances/image on average (median 1, max 11), unlike the crowded-scene datasets (PKLot/VisDrone/UAVDT) -- this is a localization task on large, easy-to-see boxes (median 3.48% of image area) rather than a small-object or dense-detection problem.
+* Different visual domain: GC10-DET is grayscale industrial line-scan imagery of rolled steel surfaces, not a natural-scene photo -- the first industrial-inspection dataset in DetectionBench, so these results say nothing about how these checkpoints would perform on outdoor/natural-scene detection or vice versa.
+---
+
+## Citation
+
+If you use this model in your research, please consider citing:
+
+1. The GC10-DET dataset (see below)
+2. The original YOLOv8n architecture (see below)
+3. The other model architectures shown in the Model Zoo/External Comparison tables above, if you reference their results
+4. DetectionBench, the training/evaluation framework used to produce this checkpoint
+```
+@article{lv2020deep,
+  title = {Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network},
+  author = {Lv, Xiaoming and Duan, Fajie and Jiang, Jia-jia and Fu, Xiao and Gan, Lin},
+  journal = {Sensors},
+  volume = {20},
+  number = {6},
+  pages = {1562},
+  year = {2020},
+  publisher = {MDPI},
+  doi = {10.3390/s20061562}
+}
+```
+
+```bibtex
+No official YOLOv8 research paper has been published by Ultralytics; this is their own recommended software citation instead:
+
+@software{jocher2023yolov8,
+  author = {Glenn Jocher and Ayush Chaurasia and Jing Qiu},
+  title = {Ultralytics YOLOv8},
+  version = {8.0.0},
+  year = {2023},
+  url = {https://github.com/ultralytics/ultralytics},
+  license = {AGPL-3.0}
+}
+```
+Other architectures compared against on GC10-DET in this model card:
+### RF-DETR
+
+```bibtex
+@inproceedings{robinson2026rfdetr,
+  title     = {RF-DETR: Real-Time Detection Transformer},
+  author    = {Robinson, Isaac and Robicheaux, Peter and Popov, Matvei and Ramanan, Deva and Peri, Neehar},
+  booktitle = {International Conference on Learning Representations (ICLR)},
+  year      = {2026},
+  url       = {https://arxiv.org/abs/2511.09554}
+}
+
+@article{oquab2023dinov2,
+  title={DINOv2: Learning Robust Visual Features without Supervision},
+  author={Oquab, Maxime and Darcet, Timoth{\'e}e and Moutakanni, Theo and Vo, Huy and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and others},
+  journal={arXiv preprint arXiv:2304.07193},
+  year={2023}
+}
+```
+### YOLO11
+
+```bibtex
+No official YOLO11 research paper has been published by Ultralytics; the most commonly cited independent architectural analysis is used instead:
+
+@article{khanam2024yolov11,
+  title={YOLOv11: An Overview of the Key Architectural Enhancements},
+  author={Khanam, Rahima and Hussain, Muhammad},
+  journal={arXiv preprint arXiv:2410.17725},
+  year={2024}
+}
+```
+### YOLO26
+
+```bibtex
+@article{jocher2026yolo26,
+  title={Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models},
+  author={Jocher, Glenn and Qiu, Jing and Liu, Mengyu and Lyu, Shuai and Akyon, Fatih Cagatay and Kalfaoglu, Muhammet Esat},
+  journal={arXiv preprint arXiv:2606.03748},
+  year={2026}
+}
+```
+```bibtex
+@software{Saksena_DetectionBench_2026,
+  author = {Saksena, Saumya Kumaar},
+  title = {DetectionBench: Reproducible Benchmarks for Modern Object Detectors on Real-World Datasets},
+  url = {https://github.com/dronefreak/DetectionBench},
+  year = {2026}
+}
+```

args.yaml

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+mode: train
+model: yolov8n.pt
+data: /home/neural_debugger/Downloads/datasets/ObjectDetectionDatasets/gc10-det-DatasetNinja/yolo_dataset/data.yaml
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+time: null
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+save: true
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+device: '0'
+workers: 8
+project: /home/neural_debugger/projects/DetectionBench/experiments/gc10det/yolov8n
+name: yolov8n
+exist_ok: true
+pretrained: true
+cls_remap: true
+optimizer: AdamW
+verbose: true
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+erasing: 0.4
+cfg: null
+tracker: tracktrack.yaml
+save_dir: /home/neural_debugger/projects/DetectionBench/experiments/gc10det/yolov8n/yolov8n

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