Update README.md

README.md

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-license: cc-by-nd-4.0
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+---
+library_name: pytorch
+tags:
+  - computer-vision
+  - object-detection
+  - image-classification
+  - yolov7
+  - marine-biology
+pipeline_tag: object-detection
+model-index:
+  - name: YOLOv7 Baselines on N-MARINE
+    results:
+      - task:
+          type: object-detection
+          name: Object Detection
+        dataset:
+          type: other
+          name: N-MARINE
+          url: https://open.canada.ca/data/en/dataset/2ae46860-f82a-4127-bb1f-b02e36ef6a70
+          split: test
+          citation: |
+            Morris, C. J., Ayyagari, K. D., Porter, D., Nguyen, Q. K., Hanlon, J., & Whidden, C. (2025).
+            *Newfoundland Marine Refuge Fish Classification Dataset (N-Marine)*.
+            Government of Canada Open Data Portal.
+            https://open.canada.ca/data/en/dataset/2ae46860-f82a-4127-bb1f-b02e36ef6a70
+        metrics:
+          - name: "mAP@0.5"
+            type: mAP
+            value: 0.808
+          - name: "mAP@0.5:0.95"
+            type: mAP
+            value: 0.494
+          - name: precision
+            type: precision
+            value: 0.807
+          - name: recall
+            type: recall
+            value: 0.764
+---
+
+# YOLOv7 Baselines for N-MARINE
+This repo hosts baseline **YOLOv7** models trained on the **N-MARINE** dataset (North Atlantic underwater images with 9 fish species + background).
+
+- **Best baseline (no class weights)**  
+  mAP@0.5 **0.808 ± 0.007** · mAP@[0.5:0.95] **0.494 ± 0.008** · P **0.807 ± 0.036** · R **0.764 ± 0.014**
+- **Paper:** TODO – add link when available
+  
+> Dataset: [`WhiddenLab/N-MARINE`](https://huggingface.co/datasets/WhiddenLab/N-MARINE)  
+> Supplementary + scripts: https://github.com/Pentaerythrittetranitrat/N-MARINE_dataset_supplementary
+
+## Model list
+
+- `yolov7-nmarine-no-class-weights.pt` — best overall (recommended)
+- `yolov7-nmarine-class-weights.pt` — improves some rare classes but slightly reduces overall mAP
+
+Each model outputs **9 classes** (species IDs in `metadata/classes.csv` within the dataset/supplementary repo).
+
+## Intended use
+
+- Benchmarking object detection on North Atlantic underwater imagery
+- Studying class imbalance, visibility limits (turbidity/occlusion), and domain shifts
+- Generating crops for downstream **species classification** tasks
+
+## Training data and splits
+
+- **Data:** N-MARINE (23,936 images, 9 species + background)
+- **Split protocol:** fixed **15% video-level** test; 5-fold CV within train videos
+- **Pretraining:** COCO weights (YOLOv7)
+- **Image size:** 640×640 letterboxed
+- **Epochs:** 50
+- **Batch size:** 32
+- **Other:** default YOLOv7 augmentations & hyperparams unless noted
+
+### Class weights variant
+Inverse-frequency class weights slightly improved **Spinytail Skate** but reduced aggregate mAP.
+
+## Quick inference
+
+> These weights are YOLOv7-format PyTorch checkpoints. Use the YOLOv7 repository or a compatible runner.
+
+### CLI (YOLOv7)
+
+```bash
+# 1) Clone YOLOv7 (example URL; use the official repo you trained with)
+git clone https://github.com/WongKinYiu/yolov7.git
+cd yolov7
+pip install -r requirements.txt
+
+# 2) Run inference
+python detect.py \
+  --weights /path/to/yolov7-nmarine-no-class-weights.pt \
+  --source /path/to/images_or_video \
+  --img-size 640 \
+  --conf-thres 0.25 \
+  --iou-thres 0.65 \
+  --save-txt --save-conf