From e74c42b5d45e9218ad01d2829bc8c1a7a7af6c14 Mon Sep 17 00:00:00 2001 From: zhangpu <1250681871@qq.com> Date: Thu, 16 Oct 2025 14:41:59 +0800 Subject: [PATCH] =?UTF-8?q?=E8=B0=83=E6=95=B4=E6=A8=A1=E5=9E=8B=E8=B7=AF?= =?UTF-8?q?=E5=BE=84=E3=80=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 1 + classifier/embedding_food_classifier_app.py | 1 + toAndroid/toAndroidEmbedding.py | 7 ++++--- 3 files changed, 6 insertions(+), 3 deletions(-) diff --git a/.gitignore b/.gitignore index a64e015..7895a9e 100644 --- a/.gitignore +++ b/.gitignore @@ -5,3 +5,4 @@ /faiss_vector_db/faiss_index/ /faiss_vector_db/faiss_index092901/ /faiss_vector_db/faiss_index101001/ +/faiss_vector_db/faiss_index*/ diff --git a/classifier/embedding_food_classifier_app.py b/classifier/embedding_food_classifier_app.py index 617594c..ee8782c 100644 --- a/classifier/embedding_food_classifier_app.py +++ b/classifier/embedding_food_classifier_app.py @@ -1135,6 +1135,7 @@ class EmbeddingFoodClassifierApp: # 提取查询图片的特征向量 query_embedding = self.model.extract_embedding(pil_image, normalize=True) + # print('特征向量:', query_embedding) query_embedding = query_embedding.reshape(1, -1).astype(np.float32) # 在FAISS索引中搜索最相似的k张图片 diff --git a/toAndroid/toAndroidEmbedding.py b/toAndroid/toAndroidEmbedding.py index 5ac1b3a..7d3c21f 100644 --- a/toAndroid/toAndroidEmbedding.py +++ b/toAndroid/toAndroidEmbedding.py @@ -17,8 +17,9 @@ def main(): # 1. 加载训练好的embedding模型权重 # base_model = create_resnet50_embedding(embedding_dim=512, pretrained=True) base_model = create_mobile_resnet50_embedding(embedding_dim=512, pretrained=True) - model_path = "../model/embedding_20250930_102826/best_embedding_model.pth" - + # model_path = "../model/embedding_20250930_102826/best_embedding_model.pth" + model_path = "../model/embedding_20251011_133653/best_embedding_model.pth" + if not os.path.exists(model_path): print(f"错误：模型文件不存在 {model_path}") return @@ -85,7 +86,7 @@ def main(): traced_model = torch.jit.trace(mobile_wrapper, single_input) # 保存模型 - output_path = "../model/embedding_20250930_102826/best_embedding_model_mobile.pt" + output_path = "../model/embedding_20251011_133653/best_embedding_model_mobile.pt" traced_model.save(output_path) print(f"✓ TorchScript模型保存成功: {output_path}")