initial version to train yolo model

examples/meter-detection/README.md

@@ -0,0 +1,172 @@
+# YOLO26 Electric Meter Detection Example
+
+This example trains a YOLO26 object detection model on the Roboflow Universe electric meter dataset using the existing `qc-cli` SageMaker training flow.
+
+The workflow is intentionally command driven. Run each step yourself so you can inspect the dataset, update `config.yaml`, and decide when to submit the SageMaker job.
+
+Dataset:
+
+```text
+https://universe.roboflow.com/kemals-workspace-kbc8l/electric-meter-detection-o4tfi/dataset/1
+```
+
+## Prerequisites
+
+- AWS credentials configured for the profile in `config.yaml`
+- Infrastructure already deployed with `uv run qc-cli infra setup`
+- A Roboflow API key exported as `ROBOFLOW_API_KEY`
+- `curl` and `unzip` available locally
+
+Install or sync the project dependencies:
+
+```bash
+uv sync
+```
+
+Set the Roboflow API key for the current shell:
+
+```bash
+export ROBOFLOW_API_KEY=your-roboflow-api-key
+```
+
+## 1. Download The Dataset
+
+Download version 1 of the dataset in YOLO format. The script uses the Roboflow REST API directly and does not require Python:
+
+```bash
+bash examples/meter-detection/download_dataset.sh
+```
+
+Confirm the extracted dataset has a YOLO data file and image splits:
+
+```bash
+find examples/meter-detection/data/electric-meter-detection -maxdepth 2 -type d | sort
+find examples/meter-detection/data/electric-meter-detection -name data.yaml -print
+```
+
+The expected layout is similar to:
+
+```text
+examples/meter-detection/data/electric-meter-detection/
+  data.yaml
+  train/
+  valid/
+  test/
+```
+
+The `test/` split may be absent depending on the exported dataset version.
+
+## 2. Configure SageMaker Training
+
+Update `config.yaml` so the training section points at this example's source directory:
+
+```yaml
+sagemaker:
+  training:
+    image_uri: 763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-training:2.6-cpu-py312-ubuntu22.04-sagemaker-v1
+    instance_type: ml.g4dn.xlarge
+    instance_count: 1
+    source_dir: examples/meter-detection/source
+    entry_point: train.py
+    hyperparameters:
+      model: yolo26n.pt
+      epochs: 25
+      imgsz: 640
+      batch: 16
+      workers: 2
+```
+
+Use `yolo26n.pt` for a lightweight first YOLO26 run. If those weights are unavailable in the installed Ultralytics package, use `yolo11n.pt` as the established fallback:
+
+```yaml
+      model: yolo11n.pt
+```
+
+The `source/requirements.txt` file is installed by the SageMaker PyTorch container before running `train.py`.
+
+For a CPU smoke test, use a CPU instance and reduce the workload:
+
+```yaml
+sagemaker:
+  training:
+    image_uri: 763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-training:2.6-cpu-py312-ubuntu22.04-sagemaker-v1
+    instance_type: ml.m4.xlarge
+    instance_count: 1
+    source_dir: examples/meter-detection/source
+    entry_point: train.py
+    hyperparameters:
+      model: yolo26n.pt
+      epochs: 1
+      imgsz: 320
+      batch: 4
+      workers: 2
+```
+
+## 3. Check Infrastructure
+
+Confirm the CLI can see the configured SageMaker role and S3 bucket:
+
+```bash
+uv run qc-cli infra status --config config.yaml
+```
+
+## 4. Upload The Dataset
+
+Upload the downloaded Roboflow dataset to the `s3.data_prefix` configured in `config.yaml`:
+
+```bash
+uv run qc-cli upload examples/meter-detection/data/electric-meter-detection --config config.yaml
+```
+
+Directory uploads preserve paths relative to the uploaded directory, so SageMaker receives the dataset root with `data.yaml` plus the split directories.
+
+## 5. Start Training
+
+Submit the SageMaker training job:
+
+```bash
+uv run qc-cli train start --config config.yaml
+```
+
+The command prints the submitted SageMaker job name. Check progress with:
+
+```bash
+uv run qc-cli train status --config config.yaml
+```
+
+Or pass the job name explicitly:
+
+```bash
+uv run qc-cli train status qc-cli-YYYYMMDD-HHMMSS --config config.yaml
+```
+
+## Outputs
+
+When the job completes, SageMaker packages the files written under `/opt/ml/model` into `model.tar.gz`.
+
+This example writes:
+
+```text
+best.pt
+model.onnx
+metrics.json
+```
+
+The archive is stored under the configured `s3.model_prefix`.
+
+## Training Hyperparameters
+
+Values under `sagemaker.training.hyperparameters` are passed to `source/train.py` as command-line arguments.
+
+| Name | Type | Default | Description |
+|---|---:|---:|---|
+| `model` | string | `yolo26n.pt` | Ultralytics model weights or model YAML. |
+| `epochs` | int | `25` | Number of training epochs. |
+| `imgsz` | int | `640` | Square training image size. |
+| `batch` | int | `16` | Images per training batch. |
+| `workers` | int | `2` | DataLoader worker count. |
+| `patience` | int | `20` | Early stopping patience. |
+| `device` | string | auto | Optional Ultralytics device value such as `0` or `cpu`. |
+| `data-yaml` | string | auto | Optional path to `data.yaml`; normally discovered from `SM_CHANNEL_TRAIN`. |
+
+Do not set `train-dir` or `model-dir` in normal SageMaker runs. SageMaker sets those automatically through `SM_CHANNEL_TRAIN` and `SM_MODEL_DIR`.