diff --git a/requirements.txt b/requirements.txt
index 92f483d5..d5c478ac 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -10,8 +10,9 @@ scipy>=1.4.1
 tqdm>=4.41.0
 addict>=2.4.0
 tensorboard>=2.7.0
-pycocotools>=2.0
+pycocotools==2.0.8
 onnx>=1.10.0  # ONNX export
 onnx-simplifier>=0.3.6 # ONNX simplifier
+onnxscript>=0.5.4
 thop  # FLOPs computation
 # pytorch_quantization>=2.1.1
diff --git a/yolov6/utils/checkpoint.py b/yolov6/utils/checkpoint.py
index c2f6239b..6b67e45f 100644
--- a/yolov6/utils/checkpoint.py
+++ b/yolov6/utils/checkpoint.py
@@ -10,7 +10,7 @@
 
 def load_state_dict(weights, model, map_location=None):
     """Load weights from checkpoint file, only assign weights those layers' name and shape are match."""
-    ckpt = torch.load(weights, map_location=map_location)
+    ckpt = torch.load(weights, map_location=map_location, weights_only=False)
     state_dict = ckpt['model'].float().state_dict()
     model_state_dict = model.state_dict()
     state_dict = {k: v for k, v in state_dict.items() if k in model_state_dict and v.shape == model_state_dict[k].shape}
@@ -22,7 +22,7 @@ def load_state_dict(weights, model, map_location=None):
 def load_checkpoint(weights, map_location=None, inplace=True, fuse=True):
     """Load model from checkpoint file."""
     LOGGER.info("Loading checkpoint from {}".format(weights))
-    ckpt = torch.load(weights, map_location=map_location)  # load
+    ckpt = torch.load(weights, map_location=map_location, weights_only=False)  # load
     model = ckpt['ema' if ckpt.get('ema') else 'model'].float()
     if fuse:
         LOGGER.info("\nFusing model...")
@@ -49,7 +49,7 @@ def strip_optimizer(ckpt_dir, epoch):
         ckpt_path = osp.join(ckpt_dir, '{}_ckpt.pt'.format(s))
         if not osp.exists(ckpt_path):
             continue
-        ckpt = torch.load(ckpt_path, map_location=torch.device('cpu'))
+        ckpt = torch.load(ckpt_path, map_location=torch.device('cpu'), weights_only=False)
         if ckpt.get('ema'):
             ckpt['model'] = ckpt['ema']  # replace model with ema
         for k in ['optimizer', 'ema', 'updates']:  # keys