yixiong/FoodClassifier
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参数修改。

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zhangpu
2025-12-03 13:43:03 +08:00
parent d4e26dd490
commit 40e90ac7c2
5 changed files with 17 additions and 17 deletions
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classifier/embedding_food_classifier_app.py
+1 -1
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@@ -67,7 +67,7 @@ class EmbeddingFoodClassifierApp:
# model_path = os.path.join(BASE_DIR, "../model/embedding_20251011_133653/best_embedding_model.pth")
# model_path = os.path.join(BASE_DIR, "../model/ProcessedIngredientRecognition/embedding_20251029_170904/best_embedding_model.pth")
# model_path = os.path.join(BASE_DIR, "../model/WholeIngredientRecognition/cosface_20251106_134718/best_cosface_model.pth")
model_path = os.path.join(BASE_DIR, "../model/WholeIngredientRecognition/grid_search_20251120_205839/model_s68.0_m0.38.pth")
model_path = os.path.join(BASE_DIR, "../model/WholeIngredientRecognition/grid_search_20251127_180944/model_s72.0_m0.35.pth")
# model_path = os.path.join(BASE_DIR, "../model/DishClassification/cosface_20251111_153649/best_cosface_model.pth")
# FAISS索引目录
faiss_vector_db/build_faiss_index.py
+6 -6
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@@ -498,16 +498,16 @@ def main():
# MODEL_PATH = "../model/embedding_20251011_133653/best_embedding_model.pth"
# MODEL_PATH = "../model/ProcessedIngredientRecognition/embedding_20251103_172012/best_embedding_model.pth"
# MODEL_PATH = "../model/WholeIngredientRecognition/cosface_20251113_160103/best_cosface_model.pth"
# MODEL_PATH = "../model/WholeIngredientRecognition/grid_search_20251120_205839/model_s68.0_m0.38.pth"
MODEL_PATH = "../model/DishClassification/grid_search_20251121_102723/model_s56.0_m0.4.pth"
MODEL_PATH = "../model/WholeIngredientRecognition/grid_search_20251127_180944/model_s72.0_m0.35.pth"
# MODEL_PATH = "../model/DishClassification/grid_search_20251121_102723/model_s56.0_m0.4.pth"
# MODEL_PATH = "../model/DishClassification/cosface_20251111_153649/best_cosface_model.pth"
# TRAIN_DIR = "../dataset/ProcessedIngredientRecognition/train"
# TRAIN_DIR = "../dataset/WholeIngredientRecognition/train"
TRAIN_DIR = "../dataset/DishClassification/train"
TRAIN_DIR = "../dataset/WholeIngredientRecognition/train"
# TRAIN_DIR = "../dataset/DishClassification/train"
# OUTPUT_DIR = "ProcessedIngredientRecognition/faiss_index"
# OUTPUT_DIR = "WholeIngredientRecognition/faiss_index"
OUTPUT_DIR = "DishClassification/faiss_index"
OUTPUT_DIR = "WholeIngredientRecognition/faiss_index"
# OUTPUT_DIR = "DishClassification/faiss_index"
BATCH_SIZE = 16
INDEX_TYPE = 'flat' # 'flat', 'ivf', 'hnsw'
EMBEDDING_DIM = 512
faiss_vector_db/visualize_embeddings.py
+4 -4
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@@ -162,11 +162,11 @@ def plot_2d(Z: np.ndarray, y: np.ndarray, title: str, out_path: Optional[str] =
def main():
parser = argparse.ArgumentParser(description="可视化高维 embedding 并进行坍塌诊断")
parser.add_argument("--embeddings", type=str, default=os.path.join("DishClassification/faiss_index", "embeddings.json"), help="embeddings.json 路径")
# parser.add_argument("--embeddings", type=str, default=os.path.join("WholeIngredientRecognition/faiss_index", "embeddings.json"), help="embeddings.json 路径")
# parser.add_argument("--embeddings", type=str, default=os.path.join("DishClassification/faiss_index", "embeddings.json"), help="embeddings.json 路径")
parser.add_argument("--embeddings", type=str, default=os.path.join("WholeIngredientRecognition/faiss_index", "embeddings.json"), help="embeddings.json 路径")
# parser.add_argument("--embeddings", type=str, default=os.path.join("ProcessedIngredientRecognition/faiss_index", "embeddings.json"), help="embeddings.json 路径")
parser.add_argument("--labels", type=str, default=os.path.join("DishClassification/faiss_index", "labels.json"), help="labels.json 路径")
# parser.add_argument("--labels", type=str, default=os.path.join("WholeIngredientRecognition/faiss_index", "labels.json"), help="labels.json 路径")
# parser.add_argument("--labels", type=str, default=os.path.join("DishClassification/faiss_index", "labels.json"), help="labels.json 路径")
parser.add_argument("--labels", type=str, default=os.path.join("WholeIngredientRecognition/faiss_index", "labels.json"), help="labels.json 路径")
# parser.add_argument("--labels", type=str, default=os.path.join("ProcessedIngredientRecognition/faiss_index", "labels.json"), help="labels.json 路径")
parser.add_argument("--method", type=str, default="pca", choices=["pca", "tsne", "umap"], help="降维方法")
parser.add_argument("--seed", type=int, default=42)
toAndroid/toAndroidEmbedding.py
+4 -4
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@@ -17,9 +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/DishClassification/grid_search_20251121_102723/model_s56.0_m0.4.pth"
# model_path = "../model/DishClassification/grid_search_20251121_102723/model_s56.0_m0.4.pth"
# model_path = "../model/WholeIngredientRecognition/cosface_20251106_134718/best_cosface_model.pth"
# model_path = "../model/WholeIngredientRecognition/grid_search_20251120_205839/model_s68.0_m0.38.pth"
model_path = "../model/WholeIngredientRecognition/grid_search_20251127_180944/model_s72.0_m0.35.pth"
# model_path = "../model/ProcessedIngredientRecognition/embedding_20251029_173607/best_embedding_model.pth"
if not os.path.exists(model_path):
@@ -93,8 +93,8 @@ def main():
traced_model = torch.jit.trace(mobile_wrapper, single_input)
# 保存模型,一定不要乱改，防止模型覆盖!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
output_path = "../model/DishClassification/grid_search_20251121_102723/best_embedding_model_mobile.pt"
# output_path = "../model/WholeIngredientRecognition/grid_search_20251120_205839/best_embedding_model_mobile.pt"
# output_path = "../model/DishClassification/grid_search_20251121_102723/best_embedding_model_mobile.pt"
output_path = "../model/WholeIngredientRecognition/grid_search_20251127_180944/best_embedding_model_mobile.pt"
# output_path = "../model/WholeIngredientRecognition/cosface_20251106_134718/best_embedding_model_mobile.pt"
# output_path = "../model/ProcessedIngredientRecognition/embedding_20251029_173607/best_embedding_model_mobile.pt"
traced_model.save(output_path)
train/grid_search_cosface.py
+2 -2
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@@ -53,8 +53,8 @@ GRID_PARAMS = {
'm': [0.32, 0.35, 0.38, 0.40,0.45,0.50], # margin参数
},
'whole_ingredient': {
's': [56.0, 60.0, 64.0, 68.0],
'm': [0.32, 0.35, 0.38, 0.40],
's': [56.0, 60.0, 64.0, 68.0, 72.0],
'm': [0.32, 0.35, 0.38, 0.40, 0.45],
},
'processed_ingredient': {
's': [56.0, 60.0, 64.0, 68.0],