# shreelakshmigbhat/carvision-cnn-yolo architecture

> Architecture diagram of the GitHub repository shreelakshmigbhat/carvision-cnn-yolo, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-27). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/shreelakshmigbhat/carvision-cnn-yolo
- Repository: https://github.com/shreelakshmigbhat/carvision-cnn-yolo

## Overview

This is a single-file, Colab-oriented car-detection workflow using Ultralytics YOLOv8. Its principal path prepares annotated car images, trains a detector, then runs prediction on an image and saves/displays results. The sampled code shows Drive mounting, annotation joins, YOLO label conversion and copying, plus image prediction; README claims model setup, training, and image/video inference. Training and video details are not present in the excerpts, so those capabilities are included without asserting their internal wiring.

Main entry points: [`car_detection_yolo_(1).py`](https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py) (Image prediction)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_data_prep["Dataset preparation"]
  node_dataset_loading["Load dataset"]
  node_annotation_join["Join annotations"]
  node_label_conversion["Convert labels"]
  node_dataset_copy["Arrange dataset"]
end

subgraph group_model_workflow["YOLO workflow"]
  node_model_setup["Configure YOLO"]
  node_training["Train detector"]
  node_trained_model["Trained detector"]
end

subgraph group_results["Detection results"]
  node_image_inference["Image prediction"]
  node_video_inference["Video inference"]
  node_detection_results["Detection results"]
  node_saved_outputs[("Saved predictions")]
end

node_user(("Notebook user"))
node_drive[("Google Drive")]
node_source_dataset[("Car images and annotations")]
node_ultralytics["Ultralytics YOLO"]

node_user -->|"runs notebook"| node_dataset_loading
node_dataset_loading -->|"mounts Drive"| node_drive
node_drive -->|"provides files"| node_source_dataset
node_dataset_loading -->|"reads data"| node_source_dataset
node_dataset_loading -->|"loads annotations"| node_annotation_join
node_annotation_join -->|"supplies boxes"| node_label_conversion
node_label_conversion -->|"writes labels"| node_dataset_copy
node_dataset_copy -->|"copies dataset"| node_drive
node_model_setup -->|"uses library"| node_ultralytics
node_model_setup -->|"configures training"| node_training
node_training -->|"trains with"| node_ultralytics
node_training -->|"produces model"| node_trained_model
node_trained_model -->|"used for prediction"| node_image_inference
node_trained_model -.->|"used for prediction"| node_video_inference
node_image_inference -->|"calls predict"| node_ultralytics
node_video_inference -.->|"runs inference"| node_ultralytics
node_image_inference -->|"returns detections"| node_detection_results
node_video_inference -.->|"produces detections"| node_detection_results
node_image_inference -->|"saves predictions"| node_saved_outputs
node_user -->|"requests detection"| node_image_inference

click node_dataset_loading "https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py"
click node_annotation_join "https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py"
click node_label_conversion "https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py"
click node_dataset_copy "https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py"
click node_model_setup "https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py"
click node_training "https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py"
click node_trained_model "https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py"
click node_image_inference "https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py"
click node_video_inference "https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py"
click node_detection_results "https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_dataset_loading,node_annotation_join,node_label_conversion,node_dataset_copy,node_user toneBlue
class node_model_setup,node_training,node_trained_model,node_drive,node_source_dataset toneAmber
class node_image_inference,node_video_inference,node_detection_results,node_saved_outputs toneMint
class node_ultralytics toneIndigo
```

## Components

### Dataset preparation

- [Load dataset](https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py): `car_detection_yolo_(1).py`
- [Join annotations](https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py): `car_detection_yolo_(1).py`
- [Convert labels](https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py): `car_detection_yolo_(1).py`
- [Arrange dataset](https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py): `car_detection_yolo_(1).py`

### YOLO workflow

- [Configure YOLO](https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py): `car_detection_yolo_(1).py`
- [Train detector](https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py): `car_detection_yolo_(1).py`
- [Trained detector](https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py): `car_detection_yolo_(1).py`

### Detection results

- [Image prediction](https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py): `car_detection_yolo_(1).py`
- [Video inference](https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py): `car_detection_yolo_(1).py`
- [Detection results](https://github.com/shreelakshmigbhat/carvision-cnn-yolo/blob/main/car_detection_yolo_(1).py): `car_detection_yolo_(1).py`
- **Saved predictions**

### Other components

- **Notebook user**
- **Google Drive**
- **Car images and annotations**
- **Ultralytics YOLO**

## Connections

- Notebook user → Load dataset: runs notebook
- Load dataset → Google Drive: mounts Drive
- Google Drive → Car images and annotations: provides files
- Load dataset → Car images and annotations: reads data
- Load dataset → Join annotations: loads annotations
- Join annotations → Convert labels: supplies boxes
- Convert labels → Arrange dataset: writes labels
- Arrange dataset → Google Drive: copies dataset
- Configure YOLO → Ultralytics YOLO: uses library
- Configure YOLO → Train detector: configures training
- Train detector → Ultralytics YOLO: trains with
- Train detector → Trained detector: produces model
- Trained detector → Image prediction: used for prediction
- Trained detector → Video inference: used for prediction
- Image prediction → Ultralytics YOLO: calls predict
- Video inference → Ultralytics YOLO: runs inference
- Image prediction → Detection results: returns detections
- Video inference → Detection results: produces detections
- Image prediction → Saved predictions: saves predictions
- Notebook user → Image prediction: requests detection