Add model card with 92.07% accuracy metrics

README.md

@@ -1,3 +1,58 @@
----
-license: mit
----
+---
+license: mit
+base_model: ultralytics/yolov8n-cls
+tags:
+- ultralytics
+- yolo
+- vision
+- image-classification
+- pytorch
+- insects
+- pollinators
+- biodiversity
+- ecology
+- conservation
+datasets:
+- custom
+language:
+- en
+metrics:
+- accuracy
+model-index:
+- name: pollinator-classifier
+  results:
+  - task:
+      type: image-classification
+      name: Image Classification
+    dataset:
+      name: Custom Pollinator Insects Dataset
+      type: custom
+    metrics:
+    - type: accuracy
+      value: 0.9207
+      name: Top-1 Accuracy
+    - type: accuracy
+      value: 0.9912
+      name: Top-5 Accuracy
+pipeline_tag: image-classification
+---
+
+# Pollinator Insect Classifier 🔬
+
+High-precision classifier for 10 pollinator insect species using YOLOv8 Nano with **92.07% accuracy**.
+
+## Quick Start
+
+```python
+from ultralytics import YOLO
+from huggingface_hub import hf_hub_download
+
+# Download model
+model_path = hf_hub_download("leonelgv/pollinator-classifier", "yolo8n.pt")
+
+# Load and predict
+model = YOLO(model_path)
+results = model("insect_image.jpg")
+```
+
+See files in this repository for complete usage examples and training details.