Push model using huggingface_hub.

README.md

@@ -2,85 +2,8 @@
 tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
-- image-classification
-- densenet121
-language:
-- ja
 ---
 
 This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-
-# Densenet121-Dog-Emotions Model Card
-
-- **学習データでの正答率:** 0.6451
-- **テストデータでの正答率:** 0.5938
-  
-# モデルについて
-このモデルはPytorchのdensenet121をdevzohaib/dog-emotions-predictionデータセットで微調整し、犬の画像を[angry, happy, relaxed, sad]の4つのカテゴリに分類するモデルです。<br>
-使い方は以下のようになっています。
-
-
-## 使い方
-1. モデルの読み込み
-```sh
-from huggingface_hub import PyTorchModelHubMixin
-import torch
-import torch.nn as nn
-from torchvision import models, transforms
-from PIL import Image
-import requests
-
-class CustomDenseNet(nn.Module, PyTorchModelHubMixin):
-  def __init__(self, class_names):
-    super().__init__()
-    self.densenet = models.densenet121(pretrained=True)
-    num_features = self.densenet.classifier.in_features
-    self.densenet.classifier = nn.Linear(num_features, 1000)
-    self.class_names = class_names
-
-  def forward(self, x):
-    outputs = self.densenet(x)
-    _, preds = torch.max(outputs, 1)
-    probabilities = torch.nn.functional.softmax(outputs, dim=1).squeeze(0)
-
-    predicted_class = self.class_names[preds.item()]
-    predicted_probabilities = {self.class_names[i]: probabilities[i].item() for i in range(len(self.class_names))}
-
-    return predicted_class, predicted_probabilities
-
-model_id = "shinyice/densenet121-dog-emotions"
-class_names = ['angry', 'happy', 'relaxed', 'sad']
-model = CustomDenseNet(class_names)
-model = model.from_pretrained(model_id)
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-```
-
-2. 画像分類
-```sh
-def dog_emotion(model, url_mode=False, input_image=None):
-  img_transforms = transforms.Compose([
-      transforms.Resize(224),
-      transforms.ToTensor(),
-      transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
-  if url_mode:
-    image = Image.open(requests.get(input_image, stream=True).raw).convert('RGB')
-  else:
-    image = Image.open(input_image).convert('RGB')
-  image_tensor = img_transforms(image).unsqueeze(0)
-  image_tensor = image_tensor.to(device)
-
-  model.eval()
-  with torch.no_grad():
-      predicted_class, predicted_probabilities = model(image_tensor)
-
-  return predicted_class, predicted_probabilities, image
-
-url_mode = True
-input_image = ""
-
-emotion,probabilities, image = dog_emotion(model=model, url_mode=url_mode, input_image=input_image)
-print(emotion,probabilities)
-image
-```
+- Library: [More Information Needed]
+- Docs: [More Information Needed]

config.json

@@ -5,5 +5,5 @@
     "relaxed",
     "sad"
   ],
-  "num_classes": 4
+  "pretrained_path": "/content/drive/MyDrive/\u79d1\u5b66\u5927\u30cf\u30c3\u30ab\u30bd\u30f3/model.pth"
 }

model.safetensors

@@ -1,3 +1,3 @@
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-size 32340976
+oid sha256:03df04d4dc62935dd86a44f525498e8739d9e0fbdaf5268dfcab95e7ba02af03
+size 32357544