diff --git a/frigate/config.py b/frigate/config.py
index a2f7f3e63..c0c97cd11 100644
--- a/frigate/config.py
+++ b/frigate/config.py
@@ -46,6 +46,7 @@ class FrigateBaseModel(BaseModel):
 
 class DetectorTypeEnum(str, Enum):
     edgetpu = "edgetpu"
+    yolov5 = "yolov5"
     cpu = "cpu"
 
 
diff --git a/frigate/detectors/yolov5_tfl.py b/frigate/detectors/yolov5_tfl.py
new file mode 100644
index 000000000..82df6794f
--- /dev/null
+++ b/frigate/detectors/yolov5_tfl.py
@@ -0,0 +1,478 @@
+import time
+import os
+import sys
+import logging
+import os
+import sys
+import argparse
+import logging
+import time
+from pathlib import Path
+
+import numpy as np
+import cv2
+
+import yaml
+import pycoral.utils.edgetpu as etpu
+from pycoral.adapters import common
+import json
+import tflite_runtime.interpreter as tflite
+
+from frigate.detectors.detection_api import DetectionApi
+from frigate.util import load_labels
+
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("EdgeTPUModel")
+
+
+class YOLOv5Tfl(DetectionApi):
+    def __init__(self, det_device=None, model_config=None):
+        """
+        Creates an object for running a Yolov5 model on an EdgeTPU or a Desktop
+        Inputs:
+          - model_file: path to edgetpu-compiled tflite file
+          - names_file: yaml names file (yolov5 format)
+          - conf_thresh: detection threshold
+          - iou_thresh: NMS threshold
+          - desktop: option to run model on a desktop
+          - filter_classes: only output certain classes
+          - agnostic_nms: use class-agnostic NMS
+          - max_det: max number of detections
+        """
+
+        self.model_file = model_config.path
+        self.labels = load_labels(model_config.labelmap_path)
+        self.desktop = True  # Should be cpu?
+        self.conf_thresh = 0.25
+        self.iou_thresh = 0.45
+        self.filter_classes = None
+        self.agnostic_nms = False
+        self.max_det = 1000
+
+        logger.info("Confidence threshold: {}".format(self.conf_thresh))
+        logger.info("IOU threshold: {}".format(self.iou_thresh))
+
+        self.inference_time = None
+        self.nms_time = None
+        self.interpreter = None
+        self.colors = Colors()  # create instance for 'from utils.plots import colors'
+
+        self.make_interpreter()
+        input_size = self.get_image_size()
+        x = (255 * np.random.random((3, *input_size))).astype(np.uint8)
+        self.forward(x)
+
+    def make_interpreter(self):
+        """
+        Internal function that loads the tflite file and creates
+        the interpreter that deals with the EdgeTPU hardware.
+        """
+        # Load the model and allocate
+        # Choose desktop or EdgTPU
+        if self.desktop:
+            self.interpreter = tflite.Interpreter(self.model_file)
+        else:
+            self.interpreter = etpu.make_interpreter(self.model_file)
+        self.interpreter.allocate_tensors()
+
+        self.input_details = self.interpreter.get_input_details()
+        self.output_details = self.interpreter.get_output_details()
+
+        logger.debug(self.input_details)
+        logger.debug(self.output_details)
+
+        self.input_zero = self.input_details[0]["quantization"][1]
+        self.input_scale = self.input_details[0]["quantization"][0]
+        self.output_zero = self.output_details[0]["quantization"][1]
+        self.output_scale = self.output_details[0]["quantization"][0]
+
+        # If the model isn't quantized then these should be zero
+        # Check against small epsilon to avoid comparing float/int
+        if self.input_scale < 1e-9:
+            self.input_scale = 1.0
+
+        if self.output_scale < 1e-9:
+            self.output_scale = 1.0
+
+        logger.debug("Input scale: {}".format(self.input_scale))
+        logger.debug("Input zero: {}".format(self.input_zero))
+        logger.debug("Output scale: {}".format(self.output_scale))
+        logger.debug("Output zero: {}".format(self.output_zero))
+
+        logger.info("Successfully loaded {}".format(self.model_file))
+
+    def get_image_size(self):
+        """
+        Returns the expected size of the input image tensor
+        """
+        if self.interpreter is not None:
+            self.input_size = common.input_size(self.interpreter)
+            logger.debug("Expecting input shape: {}".format(self.input_size))
+            return self.input_size
+        else:
+            logger.warning("Interpreter is not yet loaded")
+
+    def detect_raw(self, tensor_input):
+        raw_detections = self.detect_yolov5(tensor_input)
+        return raw_detections
+
+    def detect_yolov5(self, tensor_input):
+        tensor_input = np.squeeze(tensor_input, axis=0)
+        results = self.forward(tensor_input)
+        det = results[0]
+        # logger.info(f"detections {len(det)}")
+        detections = np.zeros((20, 6), np.float32)
+        i = 0
+        for *xyxy, conf, cls in reversed(det):
+            detections[i] = [
+                int(cls),
+                float(conf),
+                xyxy[1],
+                xyxy[0],
+                xyxy[3],
+                xyxy[2],
+            ]
+            i += 1
+
+            logger.info(f"{self.labels[int(cls)], int(cls), float(conf)}")
+
+        return detections
+
+    def forward(self, x: np.ndarray, with_nms=True) -> np.ndarray:
+        """
+        Predict function using the EdgeTPU
+        Inputs:
+            x: (C, H, W) image tensor
+            with_nms: apply NMS on output
+        Returns:
+            prediction array (with or without NMS applied)
+        """
+        tstart = time.time()
+        # Transpose if C, H, W
+        if x.shape[0] == 3:
+            x = x.transpose((1, 2, 0))
+
+        x = x.astype("float32")
+
+        # Scale input, conversion is: real = (int_8 - zero)*scale
+        x = (x / self.input_scale) + self.input_zero
+        x = x[np.newaxis].astype(np.uint8)
+
+        self.interpreter.set_tensor(self.input_details[0]["index"], x)
+        self.interpreter.invoke()
+
+        # Scale output
+        result = (
+            common.output_tensor(self.interpreter, 0).astype("float32")
+            - self.output_zero
+        ) * self.output_scale
+        self.inference_time = time.time() - tstart
+
+        if with_nms:
+
+            tstart = time.time()
+            nms_result = non_max_suppression(
+                result,
+                self.conf_thresh,
+                self.iou_thresh,
+                self.filter_classes,
+                self.agnostic_nms,
+                max_det=self.max_det,
+            )
+            self.nms_time = time.time() - tstart
+
+            return nms_result
+
+        else:
+            return result
+
+    def get_last_inference_time(self, with_nms=True):
+        """
+        Returns a tuple containing most recent inference and NMS time
+        """
+        res = [self.inference_time]
+
+        if with_nms:
+            res.append(self.nms_time)
+
+        return res
+
+
+class Colors:
+    # Ultralytics color palette https://ultralytics.com/
+    def __init__(self):
+        # hex = matplotlib.colors.TABLEAU_COLORS.values()
+        hex = (
+            "FF3838",
+            "FF9D97",
+            "FF701F",
+            "FFB21D",
+            "CFD231",
+            "48F90A",
+            "92CC17",
+            "3DDB86",
+            "1A9334",
+            "00D4BB",
+            "2C99A8",
+            "00C2FF",
+            "344593",
+            "6473FF",
+            "0018EC",
+            "8438FF",
+            "520085",
+            "CB38FF",
+            "FF95C8",
+            "FF37C7",
+        )
+        self.palette = [self.hex2rgb("#" + c) for c in hex]
+        self.n = len(self.palette)
+
+    def __call__(self, i, bgr=False):
+        c = self.palette[int(i) % self.n]
+        return (c[2], c[1], c[0]) if bgr else c
+
+    @staticmethod
+    def hex2rgb(h):  # rgb order (PIL)
+        return tuple(int(h[1 + i : 1 + i + 2], 16) for i in (0, 2, 4))
+
+
+def plot_one_box(
+    box, im, color=(128, 128, 128), txt_color=(255, 255, 255), label=None, line_width=3
+):
+    # Plots one xyxy box on image im with label
+    assert (
+        im.data.contiguous
+    ), "Image not contiguous. Apply np.ascontiguousarray(im) to plot_on_box() input image."
+    lw = line_width or max(int(min(im.size) / 200), 2)  # line width
+
+    c1, c2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
+
+    cv2.rectangle(im, c1, c2, color, thickness=lw, lineType=cv2.LINE_AA)
+    if label:
+        tf = max(lw - 1, 1)  # font thickness
+        txt_width, txt_height = cv2.getTextSize(
+            label, 0, fontScale=lw / 3, thickness=tf
+        )[0]
+        c2 = c1[0] + txt_width, c1[1] - txt_height - 3
+        cv2.rectangle(im, c1, c2, color, -1, cv2.LINE_AA)  # filled
+        cv2.putText(
+            im,
+            label,
+            (c1[0], c1[1] - 2),
+            0,
+            lw / 3,
+            txt_color,
+            thickness=tf,
+            lineType=cv2.LINE_AA,
+        )
+    return im
+
+
+def resize_and_pad(image, desired_size):
+    old_size = image.shape[:2]
+    ratio = float(desired_size / max(old_size))
+    new_size = tuple([int(x * ratio) for x in old_size])
+
+    # new_size should be in (width, height) format
+
+    image = cv2.resize(image, (new_size[1], new_size[0]))
+
+    delta_w = desired_size - new_size[1]
+    delta_h = desired_size - new_size[0]
+
+    pad = (delta_w, delta_h)
+
+    color = [100, 100, 100]
+    new_im = cv2.copyMakeBorder(
+        image, 0, delta_h, 0, delta_w, cv2.BORDER_CONSTANT, value=color
+    )
+
+    return new_im, pad
+
+
+def get_image_tensor(img, max_size, debug=False):
+    """
+    Reshapes an input image into a square with sides max_size
+    """
+    if type(img) is str:
+        img = cv2.imread(img)
+
+    resized, pad = resize_and_pad(img, max_size)
+    resized = resized.astype(np.float32)
+
+    if debug:
+        cv2.imwrite("intermediate.png", resized)
+
+    # Normalise!
+    resized /= 255.0
+
+    return img, resized, pad
+
+
+def xyxy2xywh(x):
+    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
+    y = np.copy(x)
+    y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center
+    y[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y center
+    y[:, 2] = x[:, 2] - x[:, 0]  # width
+    y[:, 3] = x[:, 3] - x[:, 1]  # height
+    return y
+
+
+def xywh2xyxy(x):
+    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
+    y = np.copy(x)
+    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
+    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
+    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
+    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
+    return y
+
+
+def nms(dets, scores, thresh):
+    """
+    dets is a numpy array : num_dets, 4
+    scores ia  nump array : num_dets,
+    """
+
+    x1 = dets[:, 0]
+    y1 = dets[:, 1]
+    x2 = dets[:, 2]
+    y2 = dets[:, 3]
+
+    areas = (x2 - x1 + 1e-9) * (y2 - y1 + 1e-9)
+    order = scores.argsort()[::-1]  # get boxes with more ious first
+
+    keep = []
+    while order.size > 0:
+        i = order[0]  # pick maxmum iou box
+        other_box_ids = order[1:]
+        keep.append(i)
+
+        xx1 = np.maximum(x1[i], x1[other_box_ids])
+        yy1 = np.maximum(y1[i], y1[other_box_ids])
+        xx2 = np.minimum(x2[i], x2[other_box_ids])
+        yy2 = np.minimum(y2[i], y2[other_box_ids])
+
+        # print(list(zip(xx1, yy1, xx2, yy2)))
+
+        w = np.maximum(0.0, xx2 - xx1 + 1e-9)  # maximum width
+        h = np.maximum(0.0, yy2 - yy1 + 1e-9)  # maxiumum height
+        inter = w * h
+
+        ovr = inter / (areas[i] + areas[other_box_ids] - inter)
+
+        inds = np.where(ovr <= thresh)[0]
+        order = order[inds + 1]
+
+    return np.array(keep)
+
+
+def non_max_suppression(
+    prediction,
+    conf_thres=0.25,
+    iou_thres=0.45,
+    classes=None,
+    agnostic=False,
+    multi_label=False,
+    labels=(),
+    max_det=300,
+):
+    nc = prediction.shape[2] - 5  # number of classes
+    xc = prediction[..., 4] > conf_thres  # candidates
+
+    # Checks
+    assert (
+        0 <= conf_thres <= 1
+    ), f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0"
+    assert (
+        0 <= iou_thres <= 1
+    ), f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0"
+
+    # Settings
+    min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height
+    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
+    time_limit = 10.0  # seconds to quit after
+    redundant = True  # require redundant detections
+    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
+    merge = False  # use merge-NMS
+
+    t = time.time()
+    output = [np.zeros((0, 6))] * prediction.shape[0]
+    for xi, x in enumerate(prediction):  # image index, image inference
+        # Apply constraints
+        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
+        x = x[xc[xi]]  # confidence
+
+        # Cat apriori labels if autolabelling
+        if labels and len(labels[xi]):
+            l = labels[xi]
+            v = np.zeros((len(l), nc + 5))
+            v[:, :4] = l[:, 1:5]  # box
+            v[:, 4] = 1.0  # conf
+            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls
+            x = np.concatenate((x, v), 0)
+
+        # If none remain process next image
+        if not x.shape[0]:
+            continue
+
+        # Compute conf
+        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf
+
+        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
+        box = xywh2xyxy(x[:, :4])
+
+        # Detections matrix nx6 (xyxy, conf, cls)
+        if multi_label:
+            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
+            x = np.concatenate(
+                (box[i], x[i, j + 5, None], j[:, None].astype(float)), axis=1
+            )
+        else:  # best class only
+            conf = np.amax(x[:, 5:], axis=1, keepdims=True)
+            j = np.argmax(x[:, 5:], axis=1).reshape(conf.shape)
+            x = np.concatenate((box, conf, j.astype(float)), axis=1)[
+                conf.flatten() > conf_thres
+            ]
+
+        # Filter by class
+        if classes is not None:
+            x = x[(x[:, 5:6] == np.array(classes)).any(1)]
+
+        # Apply finite constraint
+        # if not torch.isfinite(x).all():
+        #     x = x[torch.isfinite(x).all(1)]
+
+        # Check shape
+        n = x.shape[0]  # number of boxes
+        if not n:  # no boxes
+            continue
+        elif n > max_nms:  # excess boxes
+            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence
+
+        # Batched NMS
+        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
+        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
+
+        i = nms(boxes, scores, iou_thres)  # NMS
+
+        if i.shape[0] > max_det:  # limit detections
+            i = i[:max_det]
+        if merge and (1 < n < 3e3):  # Merge NMS (boxes merged using weighted mean)
+            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
+            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
+            weights = iou * scores[None]  # box weights
+            x[i, :4] = np.dot(weights, x[:, :4]).astype(float) / weights.sum(
+                1, keepdim=True
+            )  # merged boxes
+            if redundant:
+                i = i[iou.sum(1) > 1]  # require redundancy
+
+        output[xi] = x[i]
+        if (time.time() - t) > time_limit:
+            print(f"WARNING: NMS time limit {time_limit}s exceeded")
+            break  # time limit exceeded
+
+    return output
diff --git a/frigate/http.py b/frigate/http.py
index 5b67575e1..b8a98d62e 100644
--- a/frigate/http.py
+++ b/frigate/http.py
@@ -646,6 +646,7 @@ def latest_frame(camera_name):
         "regions": request.args.get("regions", type=int),
     }
     resize_quality = request.args.get("quality", default=70, type=int)
+    save_output = request.args.get("save_output", default=0, type=int)
 
     if camera_name in current_app.frigate_config.cameras:
         frame = current_app.detected_frames_processor.get_current_frame(
@@ -658,6 +659,8 @@ def latest_frame(camera_name):
         width = int(height * frame.shape[1] / frame.shape[0])
 
         frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
+        if save_output == 1:
+            cv2.imwrite(f'/tmp/{camera_name}.{time.time_ns() // 1000000}.jpeg', frame)
 
         ret, jpg = cv2.imencode(
             ".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), resize_quality]
diff --git a/frigate/object_detection.py b/frigate/object_detection.py
index b4f2c2c60..4cb1c37f2 100644
--- a/frigate/object_detection.py
+++ b/frigate/object_detection.py
@@ -13,7 +13,7 @@ from setproctitle import setproctitle
 from frigate.config import DetectorTypeEnum, InputTensorEnum
 from frigate.detectors.edgetpu_tfl import EdgeTpuTfl
 from frigate.detectors.cpu_tfl import CpuTfl
-
+from frigate.detectors.yolov5_tfl import YOLOv5Tfl
 from frigate.util import EventsPerSecond, SharedMemoryFrameManager, listen, load_labels
 
 logger = logging.getLogger(__name__)
@@ -45,8 +45,11 @@ class LocalObjectDetector(ObjectDetector):
         self.fps = EventsPerSecond()
         if labels is None:
             self.labels = {}
-        else:
-            self.labels = load_labels(labels)
+        # else:
+        #     self.labels = load_labels(labels)
+
+        if model_config.labelmap_path:
+            self.labels = load_labels(model_config.labelmap_path)
 
         if model_config:
             self.input_transform = tensor_transform(model_config.input_tensor)
@@ -57,6 +60,10 @@ class LocalObjectDetector(ObjectDetector):
             self.detect_api = EdgeTpuTfl(
                 det_device=det_device, model_config=model_config
             )
+        elif det_type == DetectorTypeEnum.yolov5:
+            self.detect_api = YOLOv5Tfl(
+                det_device=det_device, model_config=model_config
+            )
         else:
             logger.warning(
                 "CPU detectors are not recommended and should only be used for testing or for trial purposes."