docs(ansible): add readme with manual artifacts downloading explanation

ansible/roles/artifacts/README.md

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+# Machine learning models
+
+The Autoware perception stack uses models for inference. These models are automatically downloaded if using `ansible`, but they can also be downloaded manually.
+
+## ONNX model files
+
+### Download instructions
+
+The ONNX model files are stored in a common location, hosted by Web.Auto
+
+Any tool that can download files from the web (e.g. `wget` or `curl`) is the only requirement for downloading these files:
+
+```console
+# yabloc_pose_initializer
+
+$ mkdir -p ~/autoware_data/yabloc_pose_initializer/
+$ wget -P ~/autoware_data/yabloc_pose_initializer/ \
+       https://s3.ap-northeast-2.wasabisys.com/pinto-model-zoo/136_road-segmentation-adas-0001/resources.tar.gz
+
+
+# image_projection_based_fusion
+
+$ mkdir -p ~/autoware_data/image_projection_based_fusion/
+$ wget -P ~/autoware_data/image_projection_based_fusion/ \
+       https://awf.ml.dev.web.auto/perception/models/pointpainting/v4/pts_voxel_encoder_pointpainting.onnx \
+       https://awf.ml.dev.web.auto/perception/models/pointpainting/v4/pts_backbone_neck_head_pointpainting.onnx
+
+
+# lidar_apollo_instance_segmentation
+
+$ mkdir -p ~/autoware_data/lidar_apollo_instance_segmentation/
+$ wget -P ~/autoware_data/lidar_apollo_instance_segmentation/ \
+       https://awf.ml.dev.web.auto/perception/models/lidar_apollo_instance_segmentation/vlp-16.onnx \
+       https://awf.ml.dev.web.auto/perception/models/lidar_apollo_instance_segmentation/hdl-64.onnx \
+       https://awf.ml.dev.web.auto/perception/models/lidar_apollo_instance_segmentation/vls-128.onnx
+
+
+# lidar_centerpoint
+
+$ mkdir -p ~/autoware_data/lidar_centerpoint/
+$ wget -P ~/autoware_data/lidar_centerpoint/ \
+       https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/pts_voxel_encoder_centerpoint.onnx \
+       https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/pts_backbone_neck_head_centerpoint.onnx \
+       https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/pts_voxel_encoder_centerpoint_tiny.onnx \
+       https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/pts_backbone_neck_head_centerpoint_tiny.onnx
+
+
+# tensorrt_yolo
+
+$ mkdir -p ~/autoware_data/tensorrt_yolo/
+$ wget -P ~/autoware_data/tensorrt_yolo/ \
+       https://awf.ml.dev.web.auto/perception/models/yolov3.onnx \
+       https://awf.ml.dev.web.auto/perception/models/yolov4.onnx \
+       https://awf.ml.dev.web.auto/perception/models/yolov4-tiny.onnx \
+       https://awf.ml.dev.web.auto/perception/models/yolov5s.onnx \
+       https://awf.ml.dev.web.auto/perception/models/yolov5m.onnx \
+       https://awf.ml.dev.web.auto/perception/models/yolov5l.onnx \
+       https://awf.ml.dev.web.auto/perception/models/yolov5x.onnx \
+       https://awf.ml.dev.web.auto/perception/models/coco.names
+
+
+# tensorrt_yolox
+
+$ mkdir -p ~/autoware_data/tensorrt_yolox/
+$ wget -P ~/autoware_data/tensorrt_yolox/ \
+       https://awf.ml.dev.web.auto/perception/models/yolox-tiny.onnx \
+       https://awf.ml.dev.web.auto/perception/models/yolox-sPlus-opt.onnx \
+       https://awf.ml.dev.web.auto/perception/models/yolox-sPlus-opt.EntropyV2-calibration.table \
+       https://awf.ml.dev.web.auto/perception/models/label.txt
+
+
+# traffic_light_classifier
+
+$ mkdir -p ~/autoware_data/traffic_light_classifier/
+$ wget -P ~/autoware_data/traffic_light_classifier/ \
+       https://awf.ml.dev.web.auto/perception/models/traffic_light_classifier/v2/traffic_light_classifier_mobilenetv2_batch_1.onnx \
+       https://awf.ml.dev.web.auto/perception/models/traffic_light_classifier/v2/traffic_light_classifier_mobilenetv2_batch_4.onnx \
+       https://awf.ml.dev.web.auto/perception/models/traffic_light_classifier/v2/traffic_light_classifier_mobilenetv2_batch_6.onnx \
+       https://awf.ml.dev.web.auto/perception/models/traffic_light_classifier/v2/traffic_light_classifier_efficientNet_b1_batch_1.onnx \
+       https://awf.ml.dev.web.auto/perception/models/traffic_light_classifier/v2/traffic_light_classifier_efficientNet_b1_batch_4.onnx \
+       https://awf.ml.dev.web.auto/perception/models/traffic_light_classifier/v2/traffic_light_classifier_efficientNet_b1_batch_6.onnx \
+       https://awf.ml.dev.web.auto/perception/models/traffic_light_classifier/v2/lamp_labels.txt
+
+
+# traffic_light_fine_detector
+
+$ mkdir -p ~/autoware_data/traffic_light_fine_detector/
+$ wget -P ~/autoware_data/traffic_light_fine_detector/ \
+       https://awf.ml.dev.web.auto/perception/models/tlr_yolox_s/v2/tlr_yolox_s_batch_1.onnx \
+       https://awf.ml.dev.web.auto/perception/models/tlr_yolox_s/v2/tlr_yolox_s_batch_4.onnx \
+       https://awf.ml.dev.web.auto/perception/models/tlr_yolox_s/v2/tlr_yolox_s_batch_6.onnx \
+       https://awf.ml.dev.web.auto/perception/models/tlr_yolox_s/v2/tlr_labels.txt
+
+
+# traffic_light_ssd_fine_detector
+
+$ mkdir -p ~/autoware_data/traffic_light_ssd_fine_detector/
+$ wget -P ~/autoware_data/traffic_light_ssd_fine_detector/ \
+       https://awf.ml.dev.web.auto/perception/models/mb2-ssd-lite-tlr.onnx \
+       https://awf.ml.dev.web.auto/perception/models/voc_labels_tl.txt
+```