Update README.md

README.md

@@ -1,3 +1,76 @@
+
 ---
-license: apache-2.0
+tags:
+- yolov5
+- yolo
+- vision
+- object-detection
+- pytorch
+library_name: yolov5
+library_version: 7.0.7
+inference: false
+ 
+
+model-index:
+- name: mbari-org/yolov5x-megafish
+  results:
+  - task:
+      type: object-detection 
+
+    metrics:
+      - type: precision  # since mAP@0.5 is not available on hf.co/metrics
+        value: 0.0  # min: 0.0 - max: 1.0
+        name: mAP@0.5
 ---
+ 
+
+### How to use
+
+- Install [yolov5](https://github.com/fcakyon/yolov5-pip):
+
+```bash
+pip install -U yolov5
+```
+
+- Load model and perform prediction:
+
+```python
+import yolov5
+
+# load model
+model = yolov5.load('mbari-org/yolov5x-megafish')
+  
+# set model parameters
+model.conf = 0.25  # NMS confidence threshold
+model.iou = 0.45  # NMS IoU threshold
+model.agnostic = False  # NMS class-agnostic
+model.multi_label = False  # NMS multiple labels per box
+model.max_det = 1000  # maximum number of detections per image
+
+# set image
+img = 'http://dsg.mbari.org/images/dsg/external/Chordata/Osteichthyes/Sebastes_aleutianus-melanostictus_complx_01.png'
+
+# perform inference
+results = model(img, size=640)
+
+# (optional) inference with test time augmentation
+# results = model(img, augment=True)
+
+# parse results
+predictions = results.pred[0]
+boxes = predictions[:, :4] # x1, y1, x2, y2
+scores = predictions[:, 4]
+categories = predictions[:, 5]
+
+# show detection bounding boxes on image
+results.show()
+
+# save results into "results/" folder
+results.save(save_dir='results/')
+```
+
+- Finetune the model on your custom dataset:
+
+```bash
+yolov5 train --data data.yaml --img 1280 --batch 16 --weights mbari-org/yolov5x-megafish --epochs 10
+```