diff --git a/README.md b/README.md
index f7e3c0bf2a6b..005e5f28d385 100644
--- a/README.md
+++ b/README.md
@@ -160,6 +160,7 @@ can be ran on:
 | [Text detection v4](/serverless/openvino/omz/intel/text-detection-0004/nuclio)                          | detector   | OpenVINO   |  ✔️  |     |
 | [YOLO v5](/serverless/pytorch/ultralytics/yolov5/nuclio)                                                | detector   | PyTorch    |  ✔️  |     |
 | [SiamMask](/serverless/pytorch/foolwood/siammask/nuclio)                                                | tracker    | PyTorch    |  ✔️  |  ✔️  |
+| [TransT](/serverless/pytorch/dschoerk/transt/nuclio)                                                    | tracker    | PyTorch    |  ✔️  |  ✔️  |
 | [f-BRS](/serverless/pytorch/saic-vul/fbrs/nuclio)                                                       | interactor | PyTorch    |  ✔️  |     |
 | [HRNet](/serverless/pytorch/saic-vul/hrnet/nuclio)                                                      | interactor | PyTorch    |     |  ✔️  |
 | [Inside-Outside Guidance](/serverless/pytorch/shiyinzhang/iog/nuclio)                                   | interactor | PyTorch    |  ✔️  |     |
diff --git a/serverless/pytorch/dschoerk/transt/nuclio/function-gpu.yaml b/serverless/pytorch/dschoerk/transt/nuclio/function-gpu.yaml
new file mode 100644
index 000000000000..08a749fe51ab
--- /dev/null
+++ b/serverless/pytorch/dschoerk/transt/nuclio/function-gpu.yaml
@@ -0,0 +1,88 @@
+metadata:
+  name: pth-dschoerk-transt
+  namespace: cvat
+  annotations:
+    name: TransT
+    type: tracker
+    spec:
+    framework: pytorch
+
+spec:
+  description: Fast Online Object Tracking and Segmentation
+  runtime: 'python:3.8'
+  handler: main:handler
+  eventTimeout: 30s
+  env:
+    - name: PYTHONPATH
+      value: /opt/nuclio/trans-t
+
+  build:
+    image: cvat/pth.dschoerk.transt
+    #baseImage: ubuntu:20.04
+
+    # GPU only
+    baseImage: nvidia/cuda:11.7.0-devel-ubuntu20.04
+
+    directives:
+      preCopy:
+        - kind: ENV
+          value: PATH="/root/miniconda3/bin:${PATH}"
+        - kind: ARG
+          value: PATH="/root/miniconda3/bin:${PATH}"
+        - kind: RUN
+          value: rm -f /etc/apt/sources.list.d/cuda.list /etc/apt/sources.list.d/nvidia-ml.list
+        - kind: RUN
+          value: apt update && apt install -y --no-install-recommends wget git ca-certificates libglib2.0-0 libgl1 && rm -rf /var/lib/apt/lists/* # libxrender1 libxext6
+        - kind: RUN
+          value: wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh &&
+            chmod +x Miniconda3-latest-Linux-x86_64.sh && ./Miniconda3-latest-Linux-x86_64.sh -b &&
+            rm -f Miniconda3-latest-Linux-x86_64.sh
+        - kind: WORKDIR
+          value: /opt/nuclio
+        - kind: RUN
+          value: conda create -y -n transt python=3.8
+        - kind: SHELL
+          value: '["conda", "run", "-n", "transt", "/bin/bash", "-c"]'
+        - kind: RUN
+          value: git clone https://github.com/dschoerk/TransT trans-t
+
+        #- kind: RUN
+        #  value: pip install torch==1.7.1+cpu torchvision==0.8.2+cpu torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
+
+        # GPU only
+        - kind: RUN
+          value: pip install torch==1.7.0+cu110 torchvision==0.8.1+cu110 -f https://download.pytorch.org/whl/torch_stable.html
+
+
+        - kind: RUN
+          value: pip install jsonpickle opencv-python
+
+        - kind: RUN
+          value: wget --no-check-certificate 'https://drive.google.com/uc?id=1Pq0sK-9jmbLAVtgB9-dPDc2pipCxYdM5' -O /transt.pth
+
+        - kind: RUN
+          value: apt remove -y git wget
+        - kind: RUN
+          value: cd trans-t
+        - kind: ENTRYPOINT
+          value: '["conda", "run", "-n", "transt"]'
+
+  triggers:
+    myHttpTrigger:
+      maxWorkers: 1
+      kind: 'http'
+      workerAvailabilityTimeoutMilliseconds: 10000
+      attributes:
+        maxRequestBodySize: 33554432 # 32MB
+
+  # GPU only
+  resources:
+    limits:
+      nvidia.com/gpu: 1
+
+  platform:
+    attributes:
+      restartPolicy:
+        name: always
+        maximumRetryCount: 3
+      mountMode: volume
\ No newline at end of file
diff --git a/serverless/pytorch/dschoerk/transt/nuclio/function.yaml b/serverless/pytorch/dschoerk/transt/nuclio/function.yaml
new file mode 100644
index 000000000000..de9dc7973b6b
--- /dev/null
+++ b/serverless/pytorch/dschoerk/transt/nuclio/function.yaml
@@ -0,0 +1,88 @@
+metadata:
+  name: pth-dschoerk-transt
+  namespace: cvat
+  annotations:
+    name: TransT
+    type: tracker
+    spec:
+    framework: pytorch
+
+spec:
+  description: Fast Online Object Tracking and Segmentation
+  runtime: 'python:3.8'
+  handler: main:handler
+  eventTimeout: 30s
+  env:
+    - name: PYTHONPATH
+      value: /opt/nuclio/trans-t
+
+  build:
+    image: cvat/pth.dschoerk.transt
+    baseImage: ubuntu:20.04
+
+    # GPU only
+    # baseImage: nvidia/cuda:11.1-devel-ubuntu20.04
+
+    directives:
+      preCopy:
+        - kind: ENV
+          value: PATH="/root/miniconda3/bin:${PATH}"
+        - kind: ARG
+          value: PATH="/root/miniconda3/bin:${PATH}"
+        - kind: RUN
+          value: rm -f /etc/apt/sources.list.d/cuda.list /etc/apt/sources.list.d/nvidia-ml.list
+        - kind: RUN
+          value: apt update && apt install -y --no-install-recommends wget git ca-certificates libglib2.0-0 libgl1 && rm -rf /var/lib/apt/lists/* # libxrender1 libxext6
+        - kind: RUN
+          value: wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh &&
+            chmod +x Miniconda3-latest-Linux-x86_64.sh && ./Miniconda3-latest-Linux-x86_64.sh -b &&
+            rm -f Miniconda3-latest-Linux-x86_64.sh
+        - kind: WORKDIR
+          value: /opt/nuclio
+        - kind: RUN
+          value: conda create -y -n transt python=3.8
+        - kind: SHELL
+          value: '["conda", "run", "-n", "transt", "/bin/bash", "-c"]'
+        - kind: RUN
+          value: git clone --depth 1 --branch v1.0 https://github.com/dschoerk/TransT trans-t
+
+        - kind: RUN
+          value: pip install torch==1.7.1+cpu torchvision==0.8.2+cpu torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
+
+        # GPU only
+        #- kind: RUN
+        #  value: pip install torch==1.7.0+cu110 torchvision==0.8.1+cu110 -f https://download.pytorch.org/whl/torch_stable.html
+
+
+        - kind: RUN
+          value: pip install jsonpickle opencv-python
+
+        - kind: RUN
+          value: wget --no-check-certificate 'https://drive.google.com/uc?id=1Pq0sK-9jmbLAVtgB9-dPDc2pipCxYdM5' -O /transt.pth
+
+        - kind: RUN
+          value: apt remove -y git wget
+        - kind: RUN
+          value: cd trans-t
+        - kind: ENTRYPOINT
+          value: '["conda", "run", "-n", "transt"]'
+
+  triggers:
+    myHttpTrigger:
+      maxWorkers: 1
+      kind: 'http'
+      workerAvailabilityTimeoutMilliseconds: 10000
+      attributes:
+        maxRequestBodySize: 33554432 # 32MB
+
+  # GPU only
+  # resources:
+  #   limits:
+  #     nvidia.com/gpu: 1
+
+  platform:
+    attributes:
+      restartPolicy:
+        name: always
+        maximumRetryCount: 3
+      mountMode: volume
\ No newline at end of file
diff --git a/serverless/pytorch/dschoerk/transt/nuclio/main.py b/serverless/pytorch/dschoerk/transt/nuclio/main.py
new file mode 100644
index 000000000000..131514f39de8
--- /dev/null
+++ b/serverless/pytorch/dschoerk/transt/nuclio/main.py
@@ -0,0 +1,145 @@
+import json
+import base64
+from PIL import Image
+import io
+import numpy as np
+import traceback
+import jsonpickle
+import torch
+
+from pysot_toolkit.bbox import get_axis_aligned_bbox
+from pysot_toolkit.trackers.tracker import Tracker
+from pysot_toolkit.trackers.net_wrappers import NetWithBackbone
+
+def create_tracker():
+    use_gpu = torch.cuda.is_available()
+
+    net_path = '/transt.pth'  # Absolute path of the model
+    net = NetWithBackbone(net_path=net_path, use_gpu=use_gpu)
+    tracker = Tracker(name='transt', net=net, window_penalty=0.49, exemplar_size=128, instance_size=256)
+    return tracker
+
+def init_tracker(tracker, img, bbox):
+    cx, cy, w, h = get_axis_aligned_bbox(np.array(bbox))
+    gt_bbox_ = [cx - w / 2, cy - h / 2, w, h]
+    init_info = {'init_bbox': gt_bbox_}
+    tracker.initialize(img, init_info)
+
+    return tracker
+
+def track(tracker, img):
+    outputs = tracker.track(img)
+    prediction_bbox = outputs['target_bbox']
+
+    left = prediction_bbox[0]
+    top = prediction_bbox[1]
+    right = prediction_bbox[0] + prediction_bbox[2]
+    bottom = prediction_bbox[1] + prediction_bbox[3]
+    return tracker, (top, left, bottom, right)
+
+
+
+def init_context(context):
+    context.logger.info("Init context...  0%")
+    model = create_tracker()
+    context.user_data.model = model
+    context.logger.info("Init context...100%")
+
+def log(msg):
+    #with open("/log.log", "a") as logf:
+    #    logf.write(msg+'\n')
+    pass
+
+def encode_state(model):
+    state = {}
+    state['model.net.net.zf'] = jsonpickle.encode(model.net.net.zf)
+    state['model.net.net.pos_template'] = jsonpickle.encode(model.net.net.pos_template)
+
+    #attrs = ['windows', 'center_pos', 'size', 'channel_average', 'mean', 'std', 'inplace', 'features_initialized']
+
+    state['model.window'] = jsonpickle.encode(model.window)
+    state['model.center_pos'] = jsonpickle.encode(model.center_pos)
+    state['model.size'] = jsonpickle.encode(model.size)
+    state['model.channel_average'] = jsonpickle.encode(model.channel_average)
+    state['model.mean'] = jsonpickle.encode(model.mean)
+    state['model.std'] = jsonpickle.encode(model.std)
+    state['model.inplace'] = jsonpickle.encode(model.inplace)
+    state['model.features_initialized'] = jsonpickle.encode(getattr(model, 'features_initialized', False))
+
+    return state
+
+def decode_state(model, state):
+
+    model.net.net.zf = jsonpickle.decode(state['model.net.net.zf'])
+    model.net.net.pos_template = jsonpickle.decode(state['model.net.net.pos_template'])
+
+    model.window = jsonpickle.decode(state['model.window'])
+    model.center_pos = jsonpickle.decode(state['model.center_pos'])
+    model.size = jsonpickle.decode(state['model.size'])
+    model.channel_average = jsonpickle.decode(state['model.channel_average'])
+    model.mean = jsonpickle.decode(state['model.mean'])
+    model.std = jsonpickle.decode(state['model.std'])
+    model.inplace = jsonpickle.decode(state['model.inplace'])
+
+    model.features_initialized = False
+    if 'model.features_initialized' in state:
+        model.features_initialized = jsonpickle.decode(state['model.features_initialized'])
+
+    return model
+
+def handler(context, event):
+
+    try:
+        context.logger.info("Run TransT model")
+        data = event.body
+        buf = io.BytesIO(base64.b64decode(data["image"]))
+        shapes = data.get("shapes")
+        states = data.get("states")
+
+        image = Image.open(buf).convert('RGB')
+        image = np.array(image)[:, :, ::-1].copy()
+
+        #cv2.imwrite('/test.jpg', image)
+
+        results = {
+            'shapes': [],
+            'states': []
+        }
+        for i, shape in enumerate(shapes):
+            if i >= len(states) or states[i] is None:
+                init_shape = (shape[0], shape[1], shape[2]-shape[0], shape[3]-shape[1]) # x1,y1,x2,y2 -> x,y,w,h
+
+                log('tracker init')
+                log(str(init_shape))
+
+                #cv2.imwrite('/init_img.jpg', image)
+
+                context.user_data.model = init_tracker(context.user_data.model, image, init_shape)
+                state = encode_state(context.user_data.model)
+            else:
+                state = states[i]
+                context.user_data.model = decode_state(context.user_data.model, state)
+                context.user_data.model, (top, left, bottom, right) = track(context.user_data.model, image)
+
+                shape = (left, top, right, bottom)
+                state = encode_state(context.user_data.model)
+
+                #cv2.imwrite('/track_img.jpg', image)
+
+                log('tracked')
+                log(str(shape))
+
+            results['shapes'].append(shape)
+            results['states'].append(state)
+
+        return context.Response(body=json.dumps(results), headers={},
+            content_type='application/json', status_code=200)
+
+    except Exception as e: # cavemen debugging
+        logf = open("/error.log", "w")
+        logf.write(str(e))
+        logf.write(traceback.format_exc())
+
+        return context.Response(headers={},
+            content_type='application/json', status_code=666)
+