zhangjiaheng001 commited on
Commit
a0a12f9
1 Parent(s): bbe469e

界面中文化

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Files changed (3) hide show
  1. .gitignore +1 -0
  2. app.py +8 -5
  3. class_names_chinese.txt +101 -0
.gitignore ADDED
@@ -0,0 +1 @@
 
 
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+ __pycache__
app.py CHANGED
@@ -8,7 +8,7 @@ from timeit import default_timer as timer
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  from typing import Tuple, Dict
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  # Setup class names
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- with open("class_names.txt", "r") as f: # reading them in from class_names.txt
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  class_names = [food_name.strip() for food_name in f.readlines()]
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  ### 2. Model and transforms preparation ###
@@ -53,8 +53,8 @@ def predict(img) -> Tuple[Dict, float]:
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  vit.eval()
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  with torch.inference_mode():
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  # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
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- pred_probs = torch.softmax((effnetb2(img)+vit(img))/2, dim=1)
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-
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  #with torch.inference_mode():
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  # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
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  #pred_probs_vit = torch.softmax(vit(img), dim=1)
@@ -73,8 +73,11 @@ def predict(img) -> Tuple[Dict, float]:
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  # Create title, description and article strings
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  #title = "FoodVision Big 🍔👁"
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- description = "An EfficientNetB2 feature extractor computer vision model to classify images of food into [101 different classes](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/food101_class_names.txt)."
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- article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
 
 
 
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  title = "食品分类器 🍔👁"
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  from typing import Tuple, Dict
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  # Setup class names
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+ with open("class_names_chinese.txt", "r") as f: # reading them in from class_names.txt
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  class_names = [food_name.strip() for food_name in f.readlines()]
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  ### 2. Model and transforms preparation ###
 
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  vit.eval()
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  with torch.inference_mode():
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  # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
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+ pred_probs = torch.softmax((effnetb2(img_effnetb2)+vit(img_vit))/2, dim=1)
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+
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  #with torch.inference_mode():
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  # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
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  #pred_probs_vit = torch.softmax(vit(img), dim=1)
 
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  # Create title, description and article strings
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  #title = "FoodVision Big 🍔👁"
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+ description = "训练集使用的food101数据集,其中包含101种不同类型食品,\
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+ [原项目](https://www.learnpytorch.io/09_pytorch_model_deployment/)在测试集上的精度大约为60%,\
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+ 我这边主要简单的替换了其中的分类模型,使得精度提到80%以上,同时也进了中文化处理,\
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+ 注意此分类器只包含了101个品种的食物,如披萨,饺子,炸薯条,炒饭,巧克力慕斯等等,[详细品类详见此处](class_names_chinese.txt)."
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+ article = "Created at [09. PyTorch Model Deployment]."
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  title = "食品分类器 🍔👁"
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class_names_chinese.txt ADDED
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+ 苹果派
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+ 小排骨
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+ 巴卡拉
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+ 生牛肉片
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+ 鞑靼牛肉
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+ 甜菜沙拉
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+ 贝奈特饼
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+ 拌饭
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+ 面包布丁
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+ 早餐卷饼
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+ 布鲁斯凯塔
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+ 凯撒沙拉
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+ 香炸奶酪卷
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+ 卡普雷塞沙拉
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+ 胡萝卜蛋糕
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+ 酸橘汁腌鱼
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+ 奶酪盘
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+ 乳酪蛋糕
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+ 咖喱鸡
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+ 鸡肉玉米饼
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+ 鸡翅
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+ 巧克力蛋糕
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+ 巧克力慕斯
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+ 西班牙油条
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+ 蛤蜊杂烩
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+ 俱乐部三明治
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+ 蟹饼
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+ 焦糖布丁
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+ 法式夫人
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+ 纸杯蛋糕
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+ 魔鬼蛋
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+ 甜甜圈
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+ 水饺
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+ 毛豆
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+ 班尼迪克蛋
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+ 田螺
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+ 沙拉三明治
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+ 烤里脊肉片
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+ 鱼和薯条
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+ 鹅肝
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+ 炸薯条
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+ 法式洋葱汤
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+ 法式吐司
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+ 炸鱿鱼
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+ 炒饭
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+ 冰冻酸奶
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+ 大蒜面包
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+ 汤团
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+ 希腊式沙拉
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+ 烤奶酪三明治
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+ 烤三文鱼
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+ 鳄梨
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+ 饺子
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+ 汉堡包
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+ 酸辣汤
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+ 热狗
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+ 墨西哥煎蛋
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+ 鹰嘴豆泥
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+ 冰淇淋
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+ 烤宽面条
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+ 龙虾浓汤
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+ 龙虾卷三明治
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+ 通心粉和奶酪
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+ 马卡龙
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+ 味噌汤
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+ 青口贝
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+ 玉米片
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+ 煎蛋卷
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+ 洋葱圈
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+ 生蚝
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+ 泰语垫
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+ 西班牙海鲜饭
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+ 薄煎饼
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+ 意式奶冻
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+ 北京烤鸭
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+ 河粉
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+ 比萨
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+ 猪排
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+ 肉汁奶酪薯条
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+ 牛排
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+ 拉猪肉三明治
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+ 拉面
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+ 馄饨
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+ 红色天鹅绒蛋糕
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+ 烩饭
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+ 萨莫萨三角饺
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+ 生鱼片
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+ 扇贝
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+ 海藻沙拉
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+ 虾和粉打窝沙食
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+ 肉酱意粉
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+ 意粉培根蛋面
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+ 春卷
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+ 牛扒
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+ 草莓脆饼
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+ 寿司
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+ 炸玉米饼
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+ 章鱼烧
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+ 提拉米苏
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+ 金枪鱼鞑靼
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+ 威化饼