diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
new file mode 100644
index 0000000..7efa857
--- /dev/null
+++ b/.github/workflows/build.yml
@@ -0,0 +1,19 @@
+name: Build
+on:
+  pull_request:
+  push:
+    branches:
+      - main
+
+jobs:
+  publish:
+    name: Build
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    
+    - name: Build
+      run: ./scripts/build.sh
+
+    - name: Run Test
+      run: docker run nuclio_lighting_flash:latest
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
new file mode 100644
index 0000000..a62592d
--- /dev/null
+++ b/.github/workflows/release.yml
@@ -0,0 +1,42 @@
+name: Tag & Release
+
+on:
+  workflow_dispatch:
+    inputs:
+      TAG_NAME:
+        description: 'Tag name. eg) 1.0.0 or 1.0.0-rc.1'
+        required: true
+
+jobs:
+  release:
+    name: Release ${{ github.event.inputs.TAG_NAME }}
+    runs-on: ubuntu-latest
+    steps:
+      - name: Docker Login
+        uses: docker/login-action@f054a8b539a109f9f41c372932f1ae047eff08c9
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Checkout this repository
+        uses: actions/checkout@v2.3.4
+        with:
+          path: ${{ github.repository }}
+
+      - name: Bump version
+        env:
+          VERSION: ${{ github.event.inputs.TAG_NAME }}
+          DIRECTORY: 
+        run: |
+          docker build . -t nuclio_lighting_flash
+          docker tag nuclio_lighting_flash ghcr.io/greenroom-robotics/nuclio_lighting_flash:latest
+          docker tag nuclio_lighting_flash ghcr.io/greenroom-robotics/nuclio_lighting_flash:${{github.event.inputs.TAG_NAME}}
+          docker push nuclio_lighting_flash ghcr.io/greenroom-robotics/nuclio_lighting_flash:latest
+          docker push nuclio_lighting_flash ghcr.io/greenroom-robotics/nuclio_lighting_flash:${{github.event.inputs.TAG_NAME}}
+
+      - name: Create a release
+        uses: softprops/action-gh-release@v1
+        with:
+          tag_name: ${{ github.event.inputs.TAG_NAME }}
+          generate_release_notes: true
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..95a100c
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,17 @@
+FROM pytorchlightning/pytorch_lightning:1.6.4-py3.9-torch1.9
+
+COPY ./nuclio_lighting_flash /opt/nuclio
+
+# Remote nvidia lists as they have a borked GPG
+RUN rm /etc/apt/sources.list.d/nvidia-ml.list /etc/apt/sources.list.d/cuda.list
+
+# Install opencv deps
+RUN apt-get update
+RUN apt-get install ffmpeg libsm6 libxext6 -y
+
+# Install lighting flash and it's deps
+RUN pip install lightning-flash icevision 'lightning-flash[image]'
+
+WORKDIR /opt/nuclio
+
+CMD python ./test_flash_model_handler.py
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..38714cc
--- /dev/null
+++ b/README.md
@@ -0,0 +1,36 @@
+# Nuclio Lighting Flash
+
+This repo builds a base image "GreenroomRobotics/nuclio_lighting_flash" which can be used to run flash ObjectDetection models in Nuclio / CVAT
+
+
+## Usage
+
+* Use [lightningflash-efficientdet-d0](./example/lightningflash-efficientdet-d0) as a reference example
+* Modify/create your own as you see fit
+* Deploy it:
+```bash
+nuctl deploy --project-name cvat \
+  --path example/lightningflash-efficientdet-d0 \
+  --platform local
+```
+
+
+## Development
+
+### Get started
+
+In order to develop you'll want a nuclio instance running on your local machine... 
+
+* `docker-compose up` to start nuclio.
+* `./scripts/build.sh` to build `ghcr.io/greenroom-robotics/nuclio_lighting_flash:latest`
+* Deploy the example to your nuclio instance:
+
+```bash
+nuctl deploy --project-name cvat \
+  --path example/lightningflash-efficientdet-d0 \
+  --platform local
+```
+
+### Release a version
+
+* Run the [Release](./.github/workflows/release.yml) workflow on github
\ No newline at end of file
diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 0000000..4f851a7
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,14 @@
+version: '3.3'
+services:
+  nuclio:
+    container_name: nuclio
+    image: quay.io/nuclio/dashboard:1.8.15-amd64
+    restart: always
+    volumes:
+      - /tmp:/tmp
+      - /var/run/docker.sock:/var/run/docker.sock
+    environment:
+      NUCLIO_CHECK_FUNCTION_CONTAINERS_HEALTHINESS: 'true'
+      NUCLIO_DASHBOARD_DEFAULT_FUNCTION_MOUNT_MODE: 'volume'
+    ports:
+      - '8070:8070'
diff --git a/example/lightningflash-efficientdet-d0/function.yaml b/example/lightningflash-efficientdet-d0/function.yaml
new file mode 100644
index 0000000..b0d9646
--- /dev/null
+++ b/example/lightningflash-efficientdet-d0/function.yaml
@@ -0,0 +1,117 @@
+metadata:
+  name: lightningflash-efficientdet-d0
+  namespace: cvat
+  annotations:
+    name: Flash - EfficientDet d0
+    type: detector
+    framework: pytorch
+    head: efficientdet # any flash ObjectDetector head
+    backbone: d0       # any flash ObjectDetector backbone
+    # checkpoint_path: # path to a model checkpoint
+    spec: |
+      [
+        { "id": 1, "name": "person" },
+        { "id": 2, "name": "bicycle" },
+        { "id": 3, "name": "car" },
+        { "id": 4, "name": "motorcycle" },
+        { "id": 5, "name": "airplane" },
+        { "id": 6, "name": "bus" },
+        { "id": 7, "name": "train" },
+        { "id": 8, "name": "truck" },
+        { "id": 9, "name": "boat" },
+        { "id":10, "name": "traffic_light" },
+        { "id":11, "name": "fire_hydrant" },
+        { "id":13, "name": "stop_sign" },
+        { "id":14, "name": "parking_meter" },
+        { "id":15, "name": "bench" },
+        { "id":16, "name": "bird" },
+        { "id":17, "name": "cat" },
+        { "id":18, "name": "dog" },
+        { "id":19, "name": "horse" },
+        { "id":20, "name": "sheep" },
+        { "id":21, "name": "cow" },
+        { "id":22, "name": "elephant" },
+        { "id":23, "name": "bear" },
+        { "id":24, "name": "zebra" },
+        { "id":25, "name": "giraffe" },
+        { "id":27, "name": "backpack" },
+        { "id":28, "name": "umbrella" },
+        { "id":31, "name": "handbag" },
+        { "id":32, "name": "tie" },
+        { "id":33, "name": "suitcase" },
+        { "id":34, "name": "frisbee" },
+        { "id":35, "name": "skis" },
+        { "id":36, "name": "snowboard" },
+        { "id":37, "name": "sports_ball" },
+        { "id":38, "name": "kite" },
+        { "id":39, "name": "baseball_bat" },
+        { "id":40, "name": "baseball_glove" },
+        { "id":41, "name": "skateboard" },
+        { "id":42, "name": "surfboard" },
+        { "id":43, "name": "tennis_racket" },
+        { "id":44, "name": "bottle" },
+        { "id":46, "name": "wine_glass" },
+        { "id":47, "name": "cup" },
+        { "id":48, "name": "fork" },
+        { "id":49, "name": "knife" },
+        { "id":50, "name": "spoon" },
+        { "id":51, "name": "bowl" },
+        { "id":52, "name": "banana" },
+        { "id":53, "name": "apple" },
+        { "id":54, "name": "sandwich" },
+        { "id":55, "name": "orange" },
+        { "id":56, "name": "broccoli" },
+        { "id":57, "name": "carrot" },
+        { "id":58, "name": "hot_dog" },
+        { "id":59, "name": "pizza" },
+        { "id":60, "name": "donut" },
+        { "id":61, "name": "cake" },
+        { "id":62, "name": "chair" },
+        { "id":63, "name": "couch" },
+        { "id":64, "name": "potted_plant" },
+        { "id":65, "name": "bed" },
+        { "id":67, "name": "dining_table" },
+        { "id":70, "name": "toilet" },
+        { "id":72, "name": "tv" },
+        { "id":73, "name": "laptop" },
+        { "id":74, "name": "mouse" },
+        { "id":75, "name": "remote" },
+        { "id":76, "name": "keyboard" },
+        { "id":77, "name": "cell_phone" },
+        { "id":78, "name": "microwave" },
+        { "id":79, "name": "oven" },
+        { "id":80, "name": "toaster" },
+        { "id":81, "name": "sink" },
+        { "id":83, "name": "refrigerator" },
+        { "id":84, "name": "book" },
+        { "id":85, "name": "clock" },
+        { "id":86, "name": "vase" },
+        { "id":87, "name": "scissors" },
+        { "id":88, "name": "teddy_bear" },
+        { "id":89, "name": "hair_drier" },
+        { "id":90, "name": "toothbrush" }
+      ]
+
+spec:
+  description: "head: efficientdet, backbone: d0"
+  runtime: 'python:3.6'
+  handler: main:handler
+  eventTimeout: 30s
+  build:
+    image: cvat/nuclio_lighting_flash
+    baseImage: ghcr.io/greenroom-robotics/nuclio_lighting_flash:latest
+
+  triggers:
+    myHttpTrigger:
+      maxWorkers: 2
+      kind: 'http'
+      workerAvailabilityTimeoutMilliseconds: 10000
+      attributes:
+        maxRequestBodySize: 33554432 # 32MB
+
+  platform:
+    attributes:
+      restartPolicy:
+        name: always
+        maximumRetryCount: 3
+      mountMode: volume
diff --git a/example/lightningflash-from-checkpoint/function.yaml b/example/lightningflash-from-checkpoint/function.yaml
new file mode 100644
index 0000000..5689af0
--- /dev/null
+++ b/example/lightningflash-from-checkpoint/function.yaml
@@ -0,0 +1,117 @@
+metadata:
+  name: lightningflash-from-checkpoint
+  namespace: cvat
+  annotations:
+    name: Flash - From Checkout
+    type: detector
+    framework: pytorch
+    # head: efficientdet # any flash ObjectDetector head
+    # backbone: d0       # any flash ObjectDetector backbone
+    checkpoint_path: http://some-path-to-a-model.pt # path to a model checkpoint
+    spec: |
+      [
+        { "id": 1, "name": "person" },
+        { "id": 2, "name": "bicycle" },
+        { "id": 3, "name": "car" },
+        { "id": 4, "name": "motorcycle" },
+        { "id": 5, "name": "airplane" },
+        { "id": 6, "name": "bus" },
+        { "id": 7, "name": "train" },
+        { "id": 8, "name": "truck" },
+        { "id": 9, "name": "boat" },
+        { "id":10, "name": "traffic_light" },
+        { "id":11, "name": "fire_hydrant" },
+        { "id":13, "name": "stop_sign" },
+        { "id":14, "name": "parking_meter" },
+        { "id":15, "name": "bench" },
+        { "id":16, "name": "bird" },
+        { "id":17, "name": "cat" },
+        { "id":18, "name": "dog" },
+        { "id":19, "name": "horse" },
+        { "id":20, "name": "sheep" },
+        { "id":21, "name": "cow" },
+        { "id":22, "name": "elephant" },
+        { "id":23, "name": "bear" },
+        { "id":24, "name": "zebra" },
+        { "id":25, "name": "giraffe" },
+        { "id":27, "name": "backpack" },
+        { "id":28, "name": "umbrella" },
+        { "id":31, "name": "handbag" },
+        { "id":32, "name": "tie" },
+        { "id":33, "name": "suitcase" },
+        { "id":34, "name": "frisbee" },
+        { "id":35, "name": "skis" },
+        { "id":36, "name": "snowboard" },
+        { "id":37, "name": "sports_ball" },
+        { "id":38, "name": "kite" },
+        { "id":39, "name": "baseball_bat" },
+        { "id":40, "name": "baseball_glove" },
+        { "id":41, "name": "skateboard" },
+        { "id":42, "name": "surfboard" },
+        { "id":43, "name": "tennis_racket" },
+        { "id":44, "name": "bottle" },
+        { "id":46, "name": "wine_glass" },
+        { "id":47, "name": "cup" },
+        { "id":48, "name": "fork" },
+        { "id":49, "name": "knife" },
+        { "id":50, "name": "spoon" },
+        { "id":51, "name": "bowl" },
+        { "id":52, "name": "banana" },
+        { "id":53, "name": "apple" },
+        { "id":54, "name": "sandwich" },
+        { "id":55, "name": "orange" },
+        { "id":56, "name": "broccoli" },
+        { "id":57, "name": "carrot" },
+        { "id":58, "name": "hot_dog" },
+        { "id":59, "name": "pizza" },
+        { "id":60, "name": "donut" },
+        { "id":61, "name": "cake" },
+        { "id":62, "name": "chair" },
+        { "id":63, "name": "couch" },
+        { "id":64, "name": "potted_plant" },
+        { "id":65, "name": "bed" },
+        { "id":67, "name": "dining_table" },
+        { "id":70, "name": "toilet" },
+        { "id":72, "name": "tv" },
+        { "id":73, "name": "laptop" },
+        { "id":74, "name": "mouse" },
+        { "id":75, "name": "remote" },
+        { "id":76, "name": "keyboard" },
+        { "id":77, "name": "cell_phone" },
+        { "id":78, "name": "microwave" },
+        { "id":79, "name": "oven" },
+        { "id":80, "name": "toaster" },
+        { "id":81, "name": "sink" },
+        { "id":83, "name": "refrigerator" },
+        { "id":84, "name": "book" },
+        { "id":85, "name": "clock" },
+        { "id":86, "name": "vase" },
+        { "id":87, "name": "scissors" },
+        { "id":88, "name": "teddy_bear" },
+        { "id":89, "name": "hair_drier" },
+        { "id":90, "name": "toothbrush" }
+      ]
+
+spec:
+  description: "object detector from checkpoint"
+  runtime: 'python:3.6'
+  handler: main:handler
+  eventTimeout: 30s
+  build:
+    image: cvat/nuclio_lighting_flash
+    baseImage: ghcr.io/greenroom-robotics/nuclio_lighting_flash:latest
+
+  triggers:
+    myHttpTrigger:
+      maxWorkers: 2
+      kind: 'http'
+      workerAvailabilityTimeoutMilliseconds: 10000
+      attributes:
+        maxRequestBodySize: 33554432 # 32MB
+
+  platform:
+    attributes:
+      restartPolicy:
+        name: always
+        maximumRetryCount: 3
+      mountMode: volume
diff --git a/nuclio_lighting_flash/fixtures/giraffe.jpg b/nuclio_lighting_flash/fixtures/giraffe.jpg
new file mode 100644
index 0000000..c819e3a
Binary files /dev/null and b/nuclio_lighting_flash/fixtures/giraffe.jpg differ
diff --git a/nuclio_lighting_flash/flash_model_handler.py b/nuclio_lighting_flash/flash_model_handler.py
new file mode 100644
index 0000000..b9253bc
--- /dev/null
+++ b/nuclio_lighting_flash/flash_model_handler.py
@@ -0,0 +1,42 @@
+from flash.image import ObjectDetector, ObjectDetectionData
+from flash.core.trainer import Trainer
+from flash.core.utilities.stages import RunningStage
+from PIL.Image import Image
+
+from nuclio_detection_labels_output import NuclioDetectionLabelsOutput
+
+
+class MockTrainer(Trainer):
+    def __init__(self):
+        super().__init__()
+        self.state.stage = RunningStage.PREDICTING  # type: ignore
+
+
+class FlashModelHandler:
+    def __init__(
+        self,
+        model: ObjectDetector,
+        image_size = 1024,
+        labels = {}
+    ):
+        self.image_size = image_size
+        self.labels = labels
+        self.model = model
+        self.trainer = MockTrainer()
+        self.model.eval()
+
+    def infer(self, image: Image, threshold: float = 0.0):
+        path = "/tmp/image.jpg"
+        image.save(path)
+
+        datamodule = ObjectDetectionData.from_files(
+            predict_files=[path],
+            transform_kwargs={"image_size": self.image_size},
+            batch_size=1,
+        )
+        predictions = self.trainer.predict(
+            self.model,
+            datamodule=datamodule,
+            output=NuclioDetectionLabelsOutput(threshold=threshold, labels=self.labels),
+        )
+        return predictions
\ No newline at end of file
diff --git a/nuclio_lighting_flash/flash_model_handler_in_memory.py b/nuclio_lighting_flash/flash_model_handler_in_memory.py
new file mode 100644
index 0000000..f119627
--- /dev/null
+++ b/nuclio_lighting_flash/flash_model_handler_in_memory.py
@@ -0,0 +1,64 @@
+from typing import Union
+import torch
+from torchvision import transforms
+from flash.image import ObjectDetector
+from flash.core.data.io.output import Output
+from flash.core.data.io.input import DataKeys
+from flash.core.data.data_module import DataModule
+from flash.core.data.io.input import DataKeys
+from flash.core.data.io.output_transform import OutputTransform
+from flash.core.trainer import Trainer
+from flash.core.utilities.stages import RunningStage
+from flash.core.integrations.icevision.adapter import to_icevision_record
+
+# WORK IN PROGRESS
+
+IMAGE_SIZE = 1024
+
+
+class MockTrainer(Trainer):
+    def __init__(self):
+        super().__init__()
+        self.state.stage = RunningStage.PREDICTING  # type: ignore
+
+
+class FlashModelHandler:
+    def __init__(self, model_path: str, output: Union[str, Output] = Output()):
+        self.model = ObjectDetector(
+            head="efficientdet", backbone="d0", num_classes=91, image_size=IMAGE_SIZE
+        )
+        self.output_transform_final = Output()
+        self.trainer = MockTrainer()
+        self.data_module = DataModule(batch_size=1)
+        self.data_module.trainer = self.trainer  # type: ignore
+        self.model.eval()
+
+        self.output_transform = OutputTransform()
+
+    def infer(self, image):
+        image_input = {}
+        image_input[DataKeys.INPUT] = image
+        inputs = {
+            DataKeys.INPUT: [
+                [transforms.ToTensor()(image).unsqueeze_(0)],
+                [to_icevision_record(image_input)],
+            ],
+            DataKeys.METADATA: {"size": [224, 224]},
+        }
+
+        with torch.no_grad():
+            inputs = self.model.transfer_batch_to_device(inputs, self.model.device, 0)  # type: ignore
+            inputs = self.data_module.on_after_batch_transfer(inputs, 0)
+            preds = self.model.predict_step(inputs, 0)
+            print("================")
+            print(preds)
+            # preds = self.output_transform(preds)
+            # preds = preds[0][DataKeys.PREDS]
+            # preds_output = self.output_transform_final(preds)
+
+            # return {
+            #     "confidence": 1,
+            #     "label": preds_output,
+            #     "points": [0, 0, 10, 10],
+            #     "type": "rectangle",
+            # }
diff --git a/nuclio_lighting_flash/main.py b/nuclio_lighting_flash/main.py
new file mode 100644
index 0000000..8dd6cf7
--- /dev/null
+++ b/nuclio_lighting_flash/main.py
@@ -0,0 +1,55 @@
+from __future__ import annotations
+import json
+import base64
+from PIL import Image
+import io
+import yaml
+
+from flash.image import ObjectDetector
+from flash_model_handler import FlashModelHandler
+
+
+def init_context(context):
+    context.logger.info("Init context...  0%")
+
+    # Read labels
+    with open("/opt/nuclio/function.yaml", "rb") as function_file:
+        functionconfig = yaml.safe_load(function_file)
+    annotations = labels_spec = functionconfig["metadata"]["annotations"]
+
+    labels_spec = annotations["spec"]
+    labels = {item["id"]: item["name"] for item in json.loads(labels_spec)}
+
+    # Read the DL model
+    # Either "checkpoint_path" or "head" and "backbone" should be specified
+    model = (
+        ObjectDetector.load_from_checkpoint(annotations["checkpoint_path"])
+        if "checkpoint_path" in annotations
+        else ObjectDetector(
+            head=annotations["head"],
+            backbone=annotations["backbone"],
+            num_classes=len(labels),
+            image_size=1024,
+        )
+    )
+    model_handler = FlashModelHandler(model=model, image_size=1024, labels=labels)
+    context.user_data.model = model_handler
+
+    context.logger.info("Init context...100%")
+
+
+def handler(context, event):
+    context.logger.info("Run Lighting Flash model")
+    data = event.body
+    buf = io.BytesIO(base64.b64decode(data["image"]))
+    threshold = float(data.get("threshold", 0.5))
+    image = Image.open(buf)
+
+    results = context.user_data.model.infer(image, threshold)
+
+    return context.Response(
+        body=json.dumps(results),
+        headers={},
+        content_type="application/json",
+        status_code=200,
+    )
diff --git a/nuclio_lighting_flash/nuclio_detection_labels_output.py b/nuclio_lighting_flash/nuclio_detection_labels_output.py
new file mode 100644
index 0000000..f9a96cb
--- /dev/null
+++ b/nuclio_lighting_flash/nuclio_detection_labels_output.py
@@ -0,0 +1,63 @@
+from typing import Any, Dict, List, Optional, Union
+
+from flash.core.data.io.input import DataKeys
+from flash.core.data.io.output import Output
+from flash.core.model import Task
+from flash.core.registry import FlashRegistry
+from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, lazy_import, requires
+from flash.core.utilities.providers import _FIFTYONE
+
+class NuclioDetectionLabelsOutput(Output):
+    """A :class:`.Output` which converts model outputs to Nuclio detection format.
+
+    Args:
+        labels: A list of labels, assumed to map the class index to the label for that class.
+        threshold: a score threshold to apply to candidate detections.
+    """
+
+    def __init__(
+        self,
+        labels: Optional[List[str]] = None,
+        threshold: Optional[float] = None,
+    ):
+        super().__init__()
+        self._labels = labels
+        self.threshold = threshold
+
+    @classmethod
+    def from_task(cls, task: Task, **kwargs) -> Output:
+        return cls(labels=getattr(task, "labels", None))
+
+    def transform(self, sample: Dict[str, Any]) -> List[Dict[str, Any]]:
+        detections = []
+
+        preds = sample[DataKeys.PREDS]
+
+        for bbox, label, score in zip(preds["bboxes"], preds["labels"], preds["scores"]):
+            confidence = score.tolist()
+
+            if self.threshold is not None and confidence < self.threshold:
+                continue
+
+            box = [
+                bbox["xmin"],
+                bbox["ymin"],
+                bbox["xmin"] + bbox["width"],
+                bbox["ymin"] + bbox["height"],
+            ]
+
+            label = label.item()
+            if self._labels is not None:
+                label = self._labels[label]
+            else:
+                label = str(int(label))
+
+            detections.append(
+              {
+                  "confidence": confidence,
+                  "label": label,
+                  "points": box,
+                  "type": "rectangle",
+              }
+            )
+        return detections
diff --git a/nuclio_lighting_flash/test_flash_model_handler.py b/nuclio_lighting_flash/test_flash_model_handler.py
new file mode 100644
index 0000000..8f27fca
--- /dev/null
+++ b/nuclio_lighting_flash/test_flash_model_handler.py
@@ -0,0 +1,23 @@
+from PIL import Image
+from flash.image import ObjectDetector
+import os
+
+from flash_model_handler import FlashModelHandler
+
+model = ObjectDetector(
+    head="efficientdet",
+    backbone="d0",
+    num_classes=91,
+    image_size=1024
+)
+model_handler = FlashModelHandler(
+    model=model,
+    image_size=1024,
+    labels={
+        25: "giraffe"
+    }
+)
+image = Image.open(os.path.join(os.getcwd(), "./fixtures/giraffe.jpg"))
+
+result = model_handler.infer(image, 0)
+print(result)
diff --git a/scripts/build.sh b/scripts/build.sh
new file mode 100755
index 0000000..d81c6c3
--- /dev/null
+++ b/scripts/build.sh
@@ -0,0 +1 @@
+docker build . -t ghcr.io/greenroom-robotics/nuclio_lighting_flash
\ No newline at end of file