Update README.md

README.md

@@ -1,3 +1,69 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- yolo
+- object-detection
+- cargo
+- packages
+- forklift
+- truck
+datasets:
+- custom-cargo-package-dataset
+model-index:
+- name: YOLOv8 Cargo Package Counter
+  results:
+  - task:
+      type: object-detection
+    dataset:
+      name: custom-cargo-package-dataset
+      type: object-detection
+      split: train
+    metrics:
+    - type: precision
+      value: 0.77187
+    - type: recall
+      value: 0.11111
+    - type: mAP50
+      value: 0.09188
+    - type: mAP50-95
+      value: 0.06383
+    - type: F1
+      value: 0.19426
+language:
+- en
+base_model: YOLOv8
+pipeline_tag: object-detection
+metrics:
+- precision
+- recall
+- f1
+---
+
+# YOLOv8 Cargo Package Counter
+
+This repository contains a YOLOv8-based model trained to detect and count cargo packages, forklifts, and trucks in images. The model was trained on a custom dataset with three classes: `cargo-package`, `forklift`, and `truck`. It can be used for various cargo logistics and package counting tasks.
+
+## Model Description
+
+YOLOv8 is a state-of-the-art object detection architecture, known for its speed and accuracy. This model was trained using a custom dataset containing images of cargo packages, forklifts, and trucks, making it specialized for logistics and transportation industries.
+
+- **Model Architecture**: YOLOv8
+- **Number of Classes**: 3 (`cargo-package`, `forklift`, `truck`)
+- **Training**: The model was trained using both `best.pt` (the best performing model during training) and `last.pt` (the final checkpoint).
+- **Use Case**: Object detection and counting of cargo packages, forklifts, and trucks in warehouses, transportation hubs, or logistics centers.
+
+## How to Use
+
+You can load the model using the `ultralytics` library, as shown below:
+
+```python
+from ultralytics import YOLO
+
+# Load the model from Hugging Face
+model = YOLO('https://huggingface.co/your-username/yolov8-cargo-package-counter/resolve/main/best.pt')
+
+# Run inference on an image
+results = model('path_to_image.jpg')
+
+# Display the results
+results.show()