2 hf

README.md

@@ -9,6 +9,4 @@ app_file: app.py
 pinned: false
 license: mit
 short_description: Frame-level guzheng playing technique detector
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---

model.py

@@ -3,7 +3,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 import torchvision.models as models
 import numpy as np
-from modelscope.msdatasets import MsDataset
+from datasets import load_dataset
 
 
 class Interpolate(nn.Module):
@@ -79,12 +79,7 @@ class EvalNet:
         raise ValueError("[Backbone not found] Please check if --model is correct!")
 
     def _model_info(self, backbone: str):
-        backbone_list = MsDataset.load(
-            "monetjoe/cv_backbones",
-            split=self.imgnet_ver,
-            cache_dir="./__pycache__",
-            trust_remote_code=True,
-        )
+        backbone_list = load_dataset("monetjoe/cv_backbones", split="train")
         backbone_info = self._get_backbone(backbone, backbone_list)
         return (
             str(backbone_info["type"]),
@@ -233,12 +228,7 @@ class t_EvalNet:
         raise ValueError("[Backbone not found] Please check if --model is correct!")
 
     def _model_info(self, backbone: str):
-        backbone_list = MsDataset.load(
-            "monetjoe/cv_backbones",
-            split=self.imgnet_ver,
-            cache_dir="./__pycache__",
-            trust_remote_code=True,
-        )
+        backbone_list = load_dataset("monetjoe/cv_backbones", split="train")
         backbone_info = self._get_backbone(backbone, backbone_list)
         return (
             str(backbone_info["type"]),

requirements.txt

@@ -2,5 +2,4 @@ torch
 pillow
 librosa
 matplotlib
-torchvision
-modelscope==1.15
+torchvision