yixiong/FoodClassifier
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修改参数。

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zhangpu
2025-10-31 14:16:34 +08:00
parent bce02fc418
commit 9d8eccde84
5 changed files with 20 additions and 17 deletions
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classifier/embedding_food_classifier_app.py
+2 -2
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@@ -65,7 +65,7 @@ class EmbeddingFoodClassifierApp:
BASE_DIR = os.path.dirname(os.path.abspath(__file__)) BASE_DIR = os.path.dirname(os.path.abspath(__file__))
# model_path = "../model/embedding_20251011_133653/best_embedding_model.pth" # model_path = "../model/embedding_20251011_133653/best_embedding_model.pth"
# model_path = os.path.join(BASE_DIR, "../model/embedding_20251011_133653/best_embedding_model.pth") # 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_100204/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/embedding_20251024_091151/best_embedding_model.pth") # model_path = os.path.join(BASE_DIR, "../model/WholeIngredientRecognition/embedding_20251024_091151/best_embedding_model.pth")
# model_path = os.path.join(BASE_DIR, "../model/DishClassification/embedding_20251022_093635/best_embedding_model.pth") # model_path = os.path.join(BASE_DIR, "../model/DishClassification/embedding_20251022_093635/best_embedding_model.pth")
@@ -73,7 +73,7 @@ class EmbeddingFoodClassifierApp:
# index_dir = "../faiss_vector_db/faiss_index" # index_dir = "../faiss_vector_db/faiss_index"
# index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/faiss_index") # index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/faiss_index")
index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/ProcessedIngredientRecognition/faiss_index") index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/ProcessedIngredientRecognition/faiss_index")
index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/WholeIngredientRecognition/faiss_index") # index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/WholeIngredientRecognition/faiss_index")
# index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/DishClassification/faiss_index") # index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/DishClassification/faiss_index")
if os.path.exists(model_path) and os.path.exists(index_dir): if os.path.exists(model_path) and os.path.exists(index_dir):
faiss_vector_db/build_faiss_index.py
+6 -6
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@@ -480,14 +480,14 @@ def main():
"""主函数""" """主函数"""
# 配置参数 # 配置参数
# MODEL_PATH = "../model/embedding_20251011_133653/best_embedding_model.pth" # MODEL_PATH = "../model/embedding_20251011_133653/best_embedding_model.pth"
MODEL_PATH = "../model/ProcessedIngredientRecognition/embedding_20251029_100204/best_embedding_model.pth" # MODEL_PATH = "../model/ProcessedIngredientRecognition/embedding_20251029_170904/best_embedding_model.pth"
# MODEL_PATH = "../model/WholeIngredientRecognition/embedding_20251024_091151/best_embedding_model.pth" MODEL_PATH = "../model/WholeIngredientRecognition/embedding_20251030_135024/best_embedding_model.pth"
# MODEL_PATH = "../model/DishClassification/embedding_20251022_093635/best_embedding_model.pth" # MODEL_PATH = "../model/DishClassification/embedding_20251022_093635/best_embedding_model.pth"
TRAIN_DIR = "../dataset/ProcessedIngredientRecognition/train" # TRAIN_DIR = "../dataset/ProcessedIngredientRecognition/train"
# TRAIN_DIR = "../dataset/WholeIngredientRecognition/train" TRAIN_DIR = "../dataset/WholeIngredientRecognition/train"
# TRAIN_DIR = "../dataset/DishClassification/train" # TRAIN_DIR = "../dataset/DishClassification/train"
OUTPUT_DIR = "ProcessedIngredientRecognition/faiss_index" # OUTPUT_DIR = "ProcessedIngredientRecognition/faiss_index"
# OUTPUT_DIR = "WholeIngredientRecognition/faiss_index" OUTPUT_DIR = "WholeIngredientRecognition/faiss_index"
# OUTPUT_DIR = "DishClassification/faiss_index" # OUTPUT_DIR = "DishClassification/faiss_index"
BATCH_SIZE = 16 BATCH_SIZE = 16
INDEX_TYPE = 'flat' # 'flat', 'ivf', 'hnsw' INDEX_TYPE = 'flat' # 'flat', 'ivf', 'hnsw'
faiss_vector_db/visualize_embeddings.py
+4 -4
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@@ -163,11 +163,11 @@ def plot_2d(Z: np.ndarray, y: np.ndarray, title: str, out_path: Optional[str] =
def main(): def main():
parser = argparse.ArgumentParser(description="可视化高维 embedding 并进行坍塌诊断") 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("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("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("--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("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("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("--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("--method", type=str, default="pca", choices=["pca", "tsne", "umap"], help="降维方法")
parser.add_argument("--seed", type=int, default=42) parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--max_points", type=int, default=None, help="抽样上限，避免t-SNE/UMAP过慢；None为全量") parser.add_argument("--max_points", type=int, default=None, help="抽样上限，避免t-SNE/UMAP过慢；None为全量")
toAndroid/toAndroidEmbedding.py
+4 -2
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@@ -18,7 +18,8 @@ def main():
# base_model = create_resnet50_embedding(embedding_dim=512, pretrained=True) # base_model = create_resnet50_embedding(embedding_dim=512, pretrained=True)
base_model = create_mobile_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/WholeIngredientRecognition/embedding_20251021_085915/best_embedding_model.pth" model_path = "../model/WholeIngredientRecognition/embedding_20251030_135024/best_embedding_model.pth"
# model_path = "../model/ProcessedIngredientRecognition/embedding_20251029_173607/best_embedding_model.pth"
if not os.path.exists(model_path): if not os.path.exists(model_path):
print(f"错误：模型文件不存在 {model_path}") print(f"错误：模型文件不存在 {model_path}")
@@ -86,7 +87,8 @@ def main():
traced_model = torch.jit.trace(mobile_wrapper, single_input) traced_model = torch.jit.trace(mobile_wrapper, single_input)
# 保存模型 # 保存模型
output_path = "../model/WholeIngredientRecognition/embedding_20251021_085915/best_embedding_model_mobile.pt" output_path = "../model/WholeIngredientRecognition/embedding_20251030_135024/best_embedding_model_mobile.pt"
# output_path = "../model/ProcessedIngredientRecognition/embedding_20251029_173607/best_embedding_model_mobile.pt"
traced_model.save(output_path) traced_model.save(output_path)
print(f"✓ TorchScript模型保存成功: {output_path}") print(f"✓ TorchScript模型保存成功: {output_path}")
train/train_embedding.py
+4 -3
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@@ -63,7 +63,8 @@ TASKS = {
lr=8e-4, lr=8e-4,
# triplet_margin=0.35, # triplet_margin=0.35,
triplet_margin=0.5, triplet_margin=0.5,
center_loss_weight=0.1, # center_loss_weight=0.1,
center_loss_weight=0.7,
aug_strength="medium", aug_strength="medium",
), ),
"processed_ingredient": TaskConfig( "processed_ingredient": TaskConfig(
@@ -820,6 +821,6 @@ if __name__ == "__main__":
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument("task", choices=list(TASKS.keys()), nargs="?", default="dish") parser.add_argument("task", choices=list(TASKS.keys()), nargs="?", default="dish")
args = parser.parse_args() args = parser.parse_args()
main("processed_ingredient") # main("processed_ingredient")
# main("whole_ingredient") main("whole_ingredient")
# main("dish") # main("dish")