diff --git a/examples/mind.py b/examples/mind.py
deleted file mode 100644
index 958d612..0000000
--- a/examples/mind.py
+++ /dev/null
@@ -1,67 +0,0 @@
-from micromind import MicroMind, Metric
-from micromind.networks import PhiNet
-from micromind.utils.parse import parse_arguments
-
-import torch
-import torch.nn as nn
-import torchvision
-import torchvision.transforms as transforms
-
-batch_size = 128
-
-
-class ImageClassification(MicroMind):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        self.modules["classifier"] = PhiNet(
-            (3, 32, 32), include_top=True, num_classes=10
-        )
-
-    def forward(self, batch):
-        return self.modules["classifier"](batch[0])
-
-    def compute_loss(self, pred, batch):
-        return nn.CrossEntropyLoss()(pred, batch[1])
-
-
-if __name__ == "__main__":
-    hparams = parse_arguments()
-    m = ImageClassification(hparams)
-
-    def compute_accuracy(pred, batch):
-        tmp = (pred.argmax(1) == batch[1]).float()
-        return tmp
-
-    transform = transforms.Compose(
-        [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
-    )
-
-    trainset = torchvision.datasets.CIFAR10(
-        root="data/cifar-10", train=True, download=True, transform=transform
-    )
-    trainloader = torch.utils.data.DataLoader(
-        trainset, batch_size=batch_size, shuffle=True, num_workers=1
-    )
-
-    testset = torchvision.datasets.CIFAR10(
-        root="data/cifar-10", train=False, download=True, transform=transform
-    )
-    testloader = torch.utils.data.DataLoader(
-        testset, batch_size=batch_size, shuffle=False, num_workers=1
-    )
-
-    acc = Metric(name="accuracy", fn=compute_accuracy)
-
-    m.train(
-        epochs=10,
-        datasets={"train": trainloader, "val": testloader, "test": testloader},
-        metrics=[acc],
-        debug=hparams.debug,
-    )
-
-    m.test(
-        datasets={"test": testloader},
-    )
-
-    m.export("output_onnx", "onnx", (3, 32, 32))
diff --git a/examples/yolo_cfg/coco8.yaml b/examples/yolo_cfg/coco8.yaml
deleted file mode 100644
index eeb5d9d..0000000
--- a/examples/yolo_cfg/coco8.yaml
+++ /dev/null
@@ -1,101 +0,0 @@
-# Ultralytics YOLO 🚀, AGPL-3.0 license
-# COCO8 dataset (first 8 images from COCO train2017) by Ultralytics
-# Example usage: yolo train data=coco8.yaml
-# parent
-# ├── ultralytics
-# └── datasets
-#     └── coco8  ← downloads here (1 MB)
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco8  # dataset root dir
-train: images/train  # train images (relative to 'path') 4 images
-val: images/val  # val images (relative to 'path') 4 images
-test:  # test images (optional)
-
-# Classes
-names:
-  0: person
-  1: bicycle
-  2: car
-  3: motorcycle
-  4: airplane
-  5: bus
-  6: train
-  7: truck
-  8: boat
-  9: traffic light
-  10: fire hydrant
-  11: stop sign
-  12: parking meter
-  13: bench
-  14: bird
-  15: cat
-  16: dog
-  17: horse
-  18: sheep
-  19: cow
-  20: elephant
-  21: bear
-  22: zebra
-  23: giraffe
-  24: backpack
-  25: umbrella
-  26: handbag
-  27: tie
-  28: suitcase
-  29: frisbee
-  30: skis
-  31: snowboard
-  32: sports ball
-  33: kite
-  34: baseball bat
-  35: baseball glove
-  36: skateboard
-  37: surfboard
-  38: tennis racket
-  39: bottle
-  40: wine glass
-  41: cup
-  42: fork
-  43: knife
-  44: spoon
-  45: bowl
-  46: banana
-  47: apple
-  48: sandwich
-  49: orange
-  50: broccoli
-  51: carrot
-  52: hot dog
-  53: pizza
-  54: donut
-  55: cake
-  56: chair
-  57: couch
-  58: potted plant
-  59: bed
-  60: dining table
-  61: toilet
-  62: tv
-  63: laptop
-  64: mouse
-  65: remote
-  66: keyboard
-  67: cell phone
-  68: microwave
-  69: oven
-  70: toaster
-  71: sink
-  72: refrigerator
-  73: book
-  74: clock
-  75: vase
-  76: scissors
-  77: teddy bear
-  78: hair drier
-  79: toothbrush
-
-
-# Download script/URL (optional)
-download: https://ultralytics.com/assets/coco8.zip
diff --git a/examples/yolo_cfg/default.yaml b/examples/yolo_cfg/default.yaml
deleted file mode 100644
index ac5b654..0000000
--- a/examples/yolo_cfg/default.yaml
+++ /dev/null
@@ -1,116 +0,0 @@
-# Ultralytics YOLO 🚀, AGPL-3.0 license
-# Default training settings and hyperparameters for medium-augmentation COCO training
-
-task: detect  # (str) YOLO task, i.e. detect, segment, classify, pose
-mode: train  # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark
-
-# Train settings -------------------------------------------------------------------------------------------------------
-model:  # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml
-data:  # (str, optional) path to data file, i.e. coco128.yaml
-epochs: 100  # (int) number of epochs to train for
-patience: 50  # (int) epochs to wait for no observable improvement for early stopping of training
-batch: 16  # (int) number of images per batch (-1 for AutoBatch)
-imgsz: 640  # (int | list) input images size as int for train and val modes, or list[w,h] for predict and export modes
-save: True  # (bool) save train checkpoints and predict results
-save_period: -1 # (int) Save checkpoint every x epochs (disabled if < 1)
-cache: False  # (bool) True/ram, disk or False. Use cache for data loading
-device:  # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
-workers: 8  # (int) number of worker threads for data loading (per RANK if DDP)
-project:  # (str, optional) project name
-name:  # (str, optional) experiment name, results saved to 'project/name' directory
-exist_ok: False  # (bool) whether to overwrite existing experiment
-pretrained: True  # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
-optimizer: auto  # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
-verbose: True  # (bool) whether to print verbose output
-seed: 0  # (int) random seed for reproducibility
-deterministic: True  # (bool) whether to enable deterministic mode
-single_cls: False  # (bool) train multi-class data as single-class
-rect: False  # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
-cos_lr: False  # (bool) use cosine learning rate scheduler
-close_mosaic: 10  # (int) disable mosaic augmentation for final epochs (0 to disable)
-resume: False  # (bool) resume training from last checkpoint
-amp: True  # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
-fraction: 1.0  # (float) dataset fraction to train on (default is 1.0, all images in train set)
-profile: False  # (bool) profile ONNX and TensorRT speeds during training for loggers
-freeze: None  # (int | list, optional) freeze first n layers, or freeze list of layer indices during training
-# Segmentation
-overlap_mask: True  # (bool) masks should overlap during training (segment train only)
-mask_ratio: 4  # (int) mask downsample ratio (segment train only)
-# Classification
-dropout: 0.0  # (float) use dropout regularization (classify train only)
-
-# Val/Test settings ----------------------------------------------------------------------------------------------------
-val: True  # (bool) validate/test during training
-split: val  # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
-save_json: False  # (bool) save results to JSON file
-save_hybrid: False  # (bool) save hybrid version of labels (labels + additional predictions)
-conf:  # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
-iou: 0.7  # (float) intersection over union (IoU) threshold for NMS
-max_det: 300  # (int) maximum number of detections per image
-half: False  # (bool) use half precision (FP16)
-dnn: False  # (bool) use OpenCV DNN for ONNX inference
-plots: True  # (bool) save plots during train/val
-
-# Prediction settings --------------------------------------------------------------------------------------------------
-source:  # (str, optional) source directory for images or videos
-show: False  # (bool) show results if possible
-save_txt: False  # (bool) save results as .txt file
-save_conf: False  # (bool) save results with confidence scores
-save_crop: False  # (bool) save cropped images with results
-show_labels: True  # (bool) show object labels in plots
-show_conf: True  # (bool) show object confidence scores in plots
-vid_stride: 1  # (int) video frame-rate stride
-stream_buffer: False  # (bool) buffer all streaming frames (True) or return the most recent frame (False)
-line_width:   # (int, optional) line width of the bounding boxes, auto if missing
-visualize: False  # (bool) visualize model features
-augment: False  # (bool) apply image augmentation to prediction sources
-agnostic_nms: False  # (bool) class-agnostic NMS
-classes:  # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
-retina_masks: False  # (bool) use high-resolution segmentation masks
-boxes: True  # (bool) Show boxes in segmentation predictions
-
-# Export settings ------------------------------------------------------------------------------------------------------
-format: torchscript  # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats
-keras: False  # (bool) use Kera=s
-optimize: False  # (bool) TorchScript: optimize for mobile
-int8: False  # (bool) CoreML/TF INT8 quantization
-dynamic: False  # (bool) ONNX/TF/TensorRT: dynamic axes
-simplify: False  # (bool) ONNX: simplify model
-opset:  # (int, optional) ONNX: opset version
-workspace: 4  # (int) TensorRT: workspace size (GB)
-nms: False  # (bool) CoreML: add NMS
-
-# Hyperparameters ------------------------------------------------------------------------------------------------------
-lr0: 0.01  # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
-lrf: 0.01  # (float) final learning rate (lr0 * lrf)
-momentum: 0.937  # (float) SGD momentum/Adam beta1
-weight_decay: 0.0005  # (float) optimizer weight decay 5e-4
-warmup_epochs: 3.0  # (float) warmup epochs (fractions ok)
-warmup_momentum: 0.8  # (float) warmup initial momentum
-warmup_bias_lr: 0.1  # (float) warmup initial bias lr
-box: 7.5  # (float) box loss gain
-cls: 0.5  # (float) cls loss gain (scale with pixels)
-dfl: 1.5  # (float) dfl loss gain
-pose: 12.0  # (float) pose loss gain
-kobj: 1.0  # (float) keypoint obj loss gain
-label_smoothing: 0.0  # (float) label smoothing (fraction)
-nbs: 64  # (int) nominal batch size
-hsv_h: 0.  # (float) image HSV-Hue augmentation (fraction)
-hsv_s: 0.  # (float) image HSV-Saturation augmentation (fraction)
-hsv_v: 0.  # (float) image HSV-Value augmentation (fraction)
-degrees: 0.0  # (float) image rotation (+/- deg)
-translate: 0.  # (float) image translation (+/- fraction)
-scale: 0.  # (float) image scale (+/- gain)
-shear: 0.0  # (float) image shear (+/- deg)
-perspective: 0.0  # (float) image perspective (+/- fraction), range 0-0.001
-flipud: 0.0  # (float) image flip up-down (probability)
-fliplr: 0.0  # (float) image flip left-right (probability)
-mosaic: 0.0  # (float) image mosaic (probability)
-mixup: 0.0  # (float) image mixup (probability)
-copy_paste: 0.0  # (float) segment copy-paste (probability)
-
-# Custom config.yaml ---------------------------------------------------------------------------------------------------
-cfg:  # (str, optional) for overriding defaults.yaml
-
-# Tracker settings ------------------------------------------------------------------------------------------------------
-tracker: botsort.yaml  # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]
diff --git a/examples/yolo_cfg/coco.names b/recipes/objection_detection/cfg/coco.names
similarity index 100%
rename from examples/yolo_cfg/coco.names
rename to recipes/objection_detection/cfg/coco.names
diff --git a/recipes/objection_detection/cfg/coco128.yaml b/recipes/objection_detection/cfg/coco128.yaml
new file mode 100644
index 0000000..f4fd87f
--- /dev/null
+++ b/recipes/objection_detection/cfg/coco128.yaml
@@ -0,0 +1,142 @@
+# Default training settings and hyperparameters for medium-augmentation COCO training
+
+task: detect  # (str) YOLO task, i.e. detect, segment, classify, pose
+mode: train  # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark
+
+# Train settings -------------------------------------------------------------------------------------------------------
+imgsz: 640  # (int | list) input images size as int for train and val modes, or list[w,h] for predict and export modes
+rect: False  # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
+cache: False  # (bool) True/ram, disk or False. Use cache for data loading
+single_cls: False  # (bool) train multi-class data as single-class
+fraction: 1.0  # (float) dataset fraction to train on (default is 1.0, all images in train set)
+
+# Segmentation
+overlap_mask: True  # (bool) masks should overlap during training (segment train only)
+mask_ratio: 4  # (int) mask downsample ratio (segment train only)
+
+# Prediction settings --------------------------------------------------------------------------------------------------
+classes:  # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
+
+# Hyperparameters ------------------------------------------------------------------------------------------------------
+box: 7.5  # (float) box loss gain
+cls: 0.5  # (float) cls loss gain (scale with pixels)
+dfl: 1.5  # (float) dfl loss gain
+
+hsv_h: 0.  # (float) image HSV-Hue augmentation (fraction)
+hsv_s: 0.  # (float) image HSV-Saturation augmentation (fraction)
+hsv_v: 0.  # (float) image HSV-Value augmentation (fraction)
+degrees: 0.0  # (float) image rotation (+/- deg)
+translate: 0.  # (float) image translation (+/- fraction)
+scale: 0.  # (float) image scale (+/- gain)
+shear: 0.0  # (float) image shear (+/- deg)
+perspective: 0.0  # (float) image perspective (+/- fraction), range 0-0.001
+flipud: 1.0  # (float) image flip up-down (probability)
+fliplr: 1.0  # (float) image flip left-right (probability)
+mosaic: 1.0  # (float) image mosaic (probability)
+mixup: 1.0  # (float) image mixup (probability)
+copy_paste: 1.0  # (float) segment copy-paste (probability)
+
+
+# # DATASET --------------------------------------------------------------------------------------------------------------
+
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# COCO8 dataset (first 8 images from COCO train2017) by Ultralytics
+# Example usage: yolo train data=coco8.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco8  ← downloads here (1 MB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco128  # dataset root dir
+train: images/train2017  # train images (relative to 'path') 128 images
+val: images/train2017  # val images (relative to 'path') 128 images
+test:  # test images (optional)
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+
+# Download script/URL (optional)
+download: https://ultralytics.com/assets/coco128.zip
diff --git a/examples/yolo_cfg/config.yaml b/recipes/objection_detection/cfg/coco8.yaml
similarity index 100%
rename from examples/yolo_cfg/config.yaml
rename to recipes/objection_detection/cfg/coco8.yaml
diff --git a/micromind/networks/detect.py b/recipes/objection_detection/inference.py
similarity index 100%
rename from micromind/networks/detect.py
rename to recipes/objection_detection/inference.py
diff --git a/examples/yolo.py b/recipes/objection_detection/train.py
similarity index 98%
rename from examples/yolo.py
rename to recipes/objection_detection/train.py
index f63f9cc..f8432b6 100644
--- a/examples/yolo.py
+++ b/recipes/objection_detection/train.py
@@ -149,7 +149,7 @@ def __init__(self, m_cfg, *args, **kwargs):
         w, r, d = 1, 1, 1
         model = YOLOv8(w, r, d, 80)
         model.load_state_dict(
-            torch.load("../micromind/networks/yolov8l.pt"), strict=True
+            torch.load("../../micromind/networks/yolov8l.pt"), strict=True
         )
         self.modules["yolo"] = model
 
@@ -225,7 +225,7 @@ def mAP(self, pred, batch):
 
 
 if __name__ == "__main__":
-    m_cfg, data_cfg = load_config("yolo_cfg/config.yaml")
+    m_cfg, data_cfg = load_config("cfg/coco8.yaml")
     batch_size = 8
 
     mode = "train"