Update README.md

README.md

@@ -1,7 +1,5 @@
 ---
 tags:
-- image-classification
-- timm
 - chart
 - charts
 - fintwit
@@ -16,8 +14,6 @@ tags:
 - plots
 - financial plots
 - cryptocurrency
-- image-recognition
-- recognition
 library_name: ultralytics
 license: mit
 datasets:
@@ -47,11 +43,43 @@ pipeline_tag: object-detection
 base_model: Ultralytics/YOLO12n
 ---
 
-# Chart Info Detector (YOLO12n)
+# Chart Info Detector
 
-Latest model from run `12n_img1792_e80_20251109-133749`.
+This is a fintuned model for detecting objects in financial charts. It uses YOLO12n as its base model, making it a fast and small model. This model is trained on my own datasets of financial charts posted on Twitter, which I labeled myself.
 
-- Test mAP50 (all classes): **0.7531**
-- Trained with Ultralytics YOLO on `StephanAkkerman/chart-info-yolo`.
+## Inteded uses
+chart-info-detector is inteded for finding relevant information from financial chart images.
 
-See `results.csv` for full metrics.
+An example of a labelled financial chart:
+![training_data](https://cdn-uploads.huggingface.co/production/uploads/648728961eee18b6bd1836bb/7bDB8GG0h02tYycOBRJIV.png)
+
+## Usage
+
+To use this model you need to install the `ultralytics` library with Python. You can then download the weights of this repo and load them into the model.
+The image size during training was set to 1792, so be sure to use this too.
+
+```py
+from ultralytics import YOLO
+from huggingface_hub import hf_hub_download
+
+# Load the pre-trained model
+model = YOLO(hf_hub_download(
+    repo_id="StephanAkkerman/chart-info-detector",
+    filename="weights/best.pt",
+    repo_type="model",
+))
+
+# Perform object detection on an image
+results = model.predict(
+    source=img_path,
+    imgsz=1536,
+    conf=0.25,
+    iou=0.5,
+    verbose=False,
+)
+
+r = results[0]
+
+# Show the results 
+r.show()
+```