diff --git a/export.py b/export.py
index 297c13a92..227cdda90 100644
--- a/export.py
+++ b/export.py
@@ -84,18 +84,10 @@ MACOS = platform.system() == 'Darwin'  # macOS environment
 def export_formats():
     # YOLOv5 export formats
     x = [
-        ['PyTorch', '-', '.pt', True, True],
-        ['TorchScript', 'torchscript', '.torchscript', True, True],
         ['ONNX', 'onnx', '.onnx', True, True],
         ['OpenVINO', 'openvino', '_openvino_model', True, False],
         ['TensorRT', 'engine', '.engine', False, True],
-        ['CoreML', 'coreml', '.mlmodel', True, False],
-        ['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
-        ['TensorFlow GraphDef', 'pb', '.pb', True, True],
-        ['TensorFlow Lite', 'tflite', '.tflite', True, False],
-        ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
-        ['TensorFlow.js', 'tfjs', '_web_model', False, False],
-        ['PaddlePaddle', 'paddle', '_paddle_model', True, True],]
+    ]
     return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
 
 
@@ -317,16 +309,16 @@ def run(
 ):
     t = time.time()
     include = [x.lower() for x in include]  # to lowercase
-    fmts = tuple(export_formats()['Argument'][1:])  # --include arguments
+    fmts = tuple(export_formats()['Argument'])  # --include arguments
     flags = [x in include for x in fmts]
     assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {fmts}'
-    jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags  # export booleans
+    onnx, xml, engine = flags  # export booleans
     file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)  # PyTorch weights
 
     # Load PyTorch model
     device = select_device(device)
     if half:
-        assert device.type != 'cpu' or coreml, '--half only compatible with GPU export, i.e. use --device 0'
+        assert device.type != 'cpu', '--half only compatible with GPU export, i.e. use --device 0'
         assert not dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both'
     model = attempt_load(weights, device=device, inplace=True, fuse=True)  # load FP32 model
 
@@ -415,7 +407,7 @@ def run(
 
     for _ in range(2):
         y = model(im)  # dry runs
-    if half and not coreml:
+    if half:
         im, model = im.half(), model.half()  # to FP16
     shape = tuple((y[0] if (isinstance(y, tuple) or (isinstance(y, list))) else y).shape)  # model output shape
     metadata = {'stride': int(max(model.stride)), 'names': model.names}  # model metadata
@@ -424,41 +416,12 @@ def run(
     # Exports
     f = [''] * len(fmts)  # exported filenames
     warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning)  # suppress TracerWarning
-    if jit:  # TorchScript
-        f[0], _ = export_torchscript(model, im, file, optimize)
     if engine:  # TensorRT required before ONNX
-        f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose)
+        f[0], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose)
     if onnx or xml:  # OpenVINO requires ONNX
-        f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify)
+        f[1], _ = export_onnx(model, im, file, opset, dynamic, simplify)
     if xml:  # OpenVINO
-        f[3], _ = export_openvino(file, metadata, half)
-    if coreml:  # CoreML
-        f[4], _ = export_coreml(model, im, file, int8, half)
-    if any((saved_model, pb, tflite, edgetpu, tfjs)):  # TensorFlow formats
-        assert not tflite or not tfjs, 'TFLite and TF.js models must be exported separately, please pass only one type.'
-        assert not isinstance(model, ClassificationModel), 'ClassificationModel export to TF formats not yet supported.'
-        f[5], s_model = export_saved_model(model.cpu(),
-                                           im,
-                                           file,
-                                           dynamic,
-                                           tf_nms=nms or agnostic_nms or tfjs,
-                                           agnostic_nms=agnostic_nms or tfjs,
-                                           topk_per_class=topk_per_class,
-                                           topk_all=topk_all,
-                                           iou_thres=iou_thres,
-                                           conf_thres=conf_thres,
-                                           keras=keras)
-        if pb or tfjs:  # pb prerequisite to tfjs
-            f[6], _ = export_pb(s_model, file)
-        if tflite or edgetpu:
-            f[7], _ = export_tflite(s_model, im, file, int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms)
-            if edgetpu:
-                f[8], _ = export_edgetpu(file)
-            add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs))
-        if tfjs:
-            f[9], _ = export_tfjs(file, int8)
-    if paddle:  # PaddlePaddle
-        f[10], _ = export_paddle(model, im, file, metadata)
+        f[2], _ = export_openvino(file, metadata, half)
 
     # Finish
     f = [str(x) for x in f if x]  # filter out '' and None