Hugging Face's logo

johnatanvq
/
fruits-yolo-model

Object Detection
ultralytics
ONNX
computer-vision
yolo11s
fruits
Model card Files
xet
 Community
johnatanvq commited on
Commit
841f5ba
·
verified ·
1 Parent(s): 56e6b04

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +6 -6
README.md CHANGED
@@ -10,14 +10,14 @@ datasets:
10
  - Johnatanvq/fruitsdata
11
  ---
12
 
13
- # 🍎🥕🍊 Fruits Detection Models (YOLOv11 + OAK Deployment)
14
 
15
  This repository provides two versions of a YOLO-based model trained to detect **apples, carrots, and oranges**.
16
  The models were trained on the [Fruits Dataset](https://huggingface.co/datasets/johnatanvq/fruits-dataset), which contains **160 annotated images** with variations in **angles, distances, lighting, shadows, quantities, and surfaces**.
17
 
18
  ---
19
 
20
- ## 📂 Repository Structure
21
 
22
  fruits-yolo-model/ </br>
23
  ├── my_model_PC/ </br>
@@ -30,15 +30,15 @@ fruits-yolo-model/ </br>
30
  └── my_model.json </br>
31
  ---
32
 
33
- ## 🧾 Training & Conversion
34
  Training: The model was trained with the Fruits Dataset.
35
 
36
  Conversion: The .pt weights were exported to ONNX and then converted via Luxonis tools into the .blob format for OAK deployment.
37
 
38
  Source Code: Training scripts and conversion pipeline are documented here:
39
- 👉 GitHub: [fruit_detection_model](https://github.com/Johnatanvq/fruit_detection_model)
40
 
41
- ## 📑 License
42
  This model is released under the CC-BY 4.0 license.
43
  You are free to share, use, and adapt the models, including for commercial purposes, as long as you provide proper attribution.
44
 
@@ -47,7 +47,7 @@ If you use these models, please cite them as:
47
 
48
  *Fruits Detection Models (YOLOv11 + OAK Deployment), by **Johnatanvq**, trained on the Fruits Dataset, licensed under CC-BY 4.0.*
49
 
50
- ## 📝 Notes
51
  The dataset is compact (160 images) but provides strong variation for robust training.
52
 
53
  my_model.pt is suitable for PyTorch inference and further training.
 
10
  - Johnatanvq/fruitsdata
11
  ---
12
 
13
+ # Fruits Detection Models (YOLOv11 + OAK Deployment)
14
 
15
  This repository provides two versions of a YOLO-based model trained to detect **apples, carrots, and oranges**.
16
  The models were trained on the [Fruits Dataset](https://huggingface.co/datasets/johnatanvq/fruits-dataset), which contains **160 annotated images** with variations in **angles, distances, lighting, shadows, quantities, and surfaces**.
17
 
18
  ---
19
 
20
+ ## Repository Structure
21
 
22
  fruits-yolo-model/ </br>
23
  ├── my_model_PC/ </br>
 
30
  └── my_model.json </br>
31
  ---
32
 
33
+ ## Training & Conversion
34
  Training: The model was trained with the Fruits Dataset.
35
 
36
  Conversion: The .pt weights were exported to ONNX and then converted via Luxonis tools into the .blob format for OAK deployment.
37
 
38
  Source Code: Training scripts and conversion pipeline are documented here:
39
+ GitHub: [fruit_detection_model](https://github.com/Johnatanvq/fruit_detection_model)
40
 
41
+ ## License
42
  This model is released under the CC-BY 4.0 license.
43
  You are free to share, use, and adapt the models, including for commercial purposes, as long as you provide proper attribution.
44
 
 
47
 
48
  *Fruits Detection Models (YOLOv11 + OAK Deployment), by **Johnatanvq**, trained on the Fruits Dataset, licensed under CC-BY 4.0.*
49
 
50
+ ## Notes
51
  The dataset is compact (160 images) but provides strong variation for robust training.
52
 
53
  my_model.pt is suitable for PyTorch inference and further training.