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.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
+confusion_matrix.png filter=lfs diff=lfs merge=lfs -text
+exdark_yolov8s_showcase.jpg filter=lfs diff=lfs merge=lfs -text
+val_batch0_pred.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,307 @@
+---
+license: agpl-3.0
+
+pipeline_tag: object-detection
+
+library_name: ultralytics
+
+datasets:
+  - dronefreak/ExDark
+
+tags:
+  - object-detection
+  - detectionbench
+  - ultralytics
+  - pytorch
+  - computer-vision
+  - low-light
+  - night-images
+  - dark-images
+  - robustness
+metrics:
+  - map50
+  - map50-95
+  - precision
+  - recall
+  - f1
+
+base_model: "Ultralytics/YOLOv8"
+---
+
+
+# YOLOv8s Finetuned on ExDark
+
+Fine-tuned YOLOv8s object detector on the **ExDark** 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-YOLOv8s-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@50-73.01%25-success?style=flat-square" alt="mAP@50">
+  <img src="https://img.shields.io/badge/mAP@50:95-45.85%25-orange?style=flat-square" alt="mAP@50:95">
+  <img src="https://img.shields.io/badge/Params-11.2M-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="exdark_yolov8s_showcase.jpg" alt="ExDark Detection Demo" width="900">
+</p>
+
+---
+
+## Performance
+
+| Metric     | Score (%)       |
+| ---------- | --------------- |
+| mAP@50     | 73.01     |
+| mAP@50-95  | 45.85   |
+| Precision  | 78.26 |
+| Recall     | 65.13    |
+| F1 Score   | 71.1        |
+| Parameters | 11.2M    |
+| FLOPs      | 28.6B     |
+
+---
+
+## Evaluation Protocol
+
+Metrics reported in this model card are computed on the ExDark **test** split, using DetectionBench's standard evaluation pipeline (`detectionbench-evaluate`).
+
+---
+
+## ExDark Model Zoo
+
+Every model DetectionBench has trained and evaluated on ExDark so far, for full transparency -- see [DetectionBench](https://github.com/dronefreak/DetectionBench) for the smaller, curated comparison set used on the project README.
+
+| Rank                       | Model                | mAP@50        | mAP@50-95       | Precision         | Recall         |
+| -------------------------- | --------------------- | ------------- | --------------- | ----------------- | -------------- |
+| 1           | RF-DETR Small | 88.98 | 61.67 | 83.07 | 81.89 |
+| 2           | RF-DETR Medium | 88.64 | 62.55 | 86.6 | 79.46 |
+| 3           | RF-DETR Nano | 85.27 | 58.01 | 85.18 | 74.67 |
+| 4           | YOLOv26l | 77.51 | 50.88 | 80.71 | 70.72 |
+| 5           | YOLOv26m | 76.54 | 50.02 | 82.29 | 68.83 |
+| 6           | YOLOv8x | 75.4 | 48.39 | 81.53 | 65.86 |
+| 7           | YOLOv8l | 75.26 | 48.48 | 81.44 | 67.58 |
+| 8           | YOLOv8m | 74.69 | 48.05 | 78.4 | 69.17 |
+| 9           | YOLOv11x | 74.41 | 48.98 | 81.87 | 67.05 |
+| 10           | YOLOv9m | 74.17 | 47.38 | 76.27 | 67.94 |
+| 11           | YOLOv26s | 74.0 | 48.32 | 79.11 | 65.59 |
+| 12           | YOLOv11l | 73.44 | 47.56 | 78.57 | 67.09 |
+| 13           | YOLOv11s | 73.35 | 46.8 | 77.93 | 66.38 |
+| 14           | YOLOv11m | 73.17 | 47.16 | 74.83 | 67.23 |
+| 15           | YOLOv8s | 73.01 | 45.85 | 78.26 | 65.13 |
+| 16           | YOLOv26n | 72.7 | 46.27 | 81.0 | 62.67 |
+| 17           | YOLOv8n | 71.29 | 44.78 | 78.25 | 62.76 |
+| 18           | YOLOv11n | 70.36 | 44.72 | 76.18 | 61.15 |
+---
+
+## Per-Class Performance
+
+| Class                      | mAP@50          | mAP@50-95         |
+| -------------------------- | --------------- | ----------------- |
+| Bicycle       | 78.66 | 53.59 |
+| Boat       | 78.23 | 41.02 |
+| Bottle       | 62.26 | 39.43 |
+| Bus       | 82.95 | 63.11 |
+| Car       | 80.2 | 54.86 |
+| Cat       | 75.24 | 45.63 |
+| Chair       | 64.4 | 35.9 |
+| Cup       | 70.4 | 44.24 |
+| Dog       | 69.53 | 46.58 |
+| Motorbike       | 79.4 | 48.47 |
+| People       | 76.12 | 42.0 |
+| Table       | 58.74 | 35.42 |
+---
+
+## Evaluation Visualizations
+
+### Precision-Recall Curve
+
+![PR Curve](BoxPR_curve.png)
+
+### F1 Curve
+
+![F1 Curve](BoxF1_curve.png)
+
+### Confusion Matrix
+
+![Confusion Matrix](confusion_matrix.png)
+
+---
+
+## Dataset
+
+This model was trained on **ExDark**. For the full dataset description, provenance, license, and citation, see the dataset card:
+
+https://huggingface.co/datasets/dronefreak/ExDark
+
+### Classes
+
+* Bicycle
+* Boat
+* Bottle
+* Bus
+* Car
+* Cat
+* Chair
+* Cup
+* Dog
+* Motorbike
+* People
+* Table
+---
+
+## 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/exdark-yolov8s",
+    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          | ExDark       |
+| Framework        | Ultralytics YOLO                  |
+| Training Toolkit | DetectionBench                   |
+| Epochs (configured max) | 500 |
+| Epochs (actually trained) | 220 |
+| Early Stopping Patience | 100 |
+| Batch Size | 32 |
+| Image Size | 640 |
+| Optimizer | auto |
+| Initial Learning Rate | 0.001 |
+| Seed | 0 |
+---
+
+## Repository Contents
+
+```text
+best.pt
+results.csv
+args.yaml
+BoxPR_curve.png
+BoxF1_curve.png
+confusion_matrix.png
+val_batch0_pred.jpg
+exdark_yolov8s_showcase.jpg
+README.md
+```
+
+---
+
+## Related Resources
+
+* [ExDark dataset card](https://huggingface.co/datasets/dronefreak/ExDark) on Hugging Face
+* [DetectionBench](https://github.com/dronefreak/DetectionBench) -- reproducible benchmarks for modern object detectors on real-world 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
+
+* Severe class imbalance: `People` accounts for roughly 46% of all annotated boxes while `Bus` is the rarest class, so per-class accuracy on rare classes is measured on very few test examples and should be read with wide uncertainty.
+* Small dataset overall (7,344 images, 734 in the test split, across 12 classes) -- limited training signal for several classes independent of the imbalance above.
+* Two-hop provenance: this dataset was converted to YOLO format by a third-party Roboflow export before reaching DetectionBench, not sourced directly from the original per-class-folder release; images are pre-resized to 640x640 by that export.
+* The original authors separately request non-commercial use of this dataset (beyond the BSD-3-Clause license text itself) -- this applies to any model trained on it, not only the raw images.
+---
+
+## Citation
+
+If you use this model in your research, please consider citing:
+
+1. The ExDark dataset (see below)
+2. The original YOLOv8s architecture (see below)
+3. DetectionBench, the training/evaluation framework used to produce this checkpoint
+
+```
+@article{Exdark,
+  title = {Getting to Know Low-light Images with The Exclusively Dark Dataset},
+  author = {Loh, Yuen Peng and Chan, Chee Seng},
+  journal = {Computer Vision and Image Understanding},
+  volume = {178},
+  pages = {30-42},
+  year = {2019},
+  doi = {https://doi.org/10.1016/j.cviu.2018.10.010}
+}
+```
+
+```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}
+}
+```
+
+```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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+task: detect
+mode: train
+model: yolov8s.pt
+data: /home/saumya.saksena/projects/ExDark/data/detectionbench_data.yaml
+epochs: 500
+time: null
+patience: 100
+batch: 32
+imgsz: 640
+save: true
+save_period: -1
+cache: false
+device: '0'
+workers: 4
+project: /home/saumya.saksena/projects/DetectionBench/experiments/exdark/yolov8s
+name: yolov8s
+exist_ok: true
+pretrained: true
+cls_remap: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
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+compile: false
+channels_last: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+conf: null
+iou: 0.7
+max_det: 300
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+dnn: false
+plots: true
+end2end: null
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: true
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+show: false
+save_frames: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
+optimize: false
+dynamic: false
+simplify: true
+opset: null
+workspace: null
+nms: false
+lr0: 0.001
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+distill_model: null
+dis: 6.0
+box: 7.5
+cls: 0.5
+cls_pw: 0.0
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+rle: 1.0
+angle: 1.0
+dlog: 1.0
+dgrad: 0.5
+dlam: 1.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+bgr: 0.0
+mosaic: 1.0
+mixup: 0.0
+cutmix: 0.0
+copy_paste: 0.0
+copy_paste_mode: flip
+auto_augment: randaugment
+erasing: 0.4
+cfg: null
+tracker: tracktrack.yaml
+save_dir: /home/saumya.saksena/projects/DetectionBench/experiments/exdark/yolov8s/yolov8s

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