如何基于无边界框标注但含分割标签的训练集训练Detectron2模型？Faster RCNN训练精度骤降问题求助

Hey there, let's work through this problem together— that 9% accuracy drop is definitely frustrating, but we can fix it by leveraging your segmentation labels properly with Detectron2.

First, let's break down why merging your datasets tanked performance: your training set has no explicit bounding box (bbox) labels, so when you mixed it with the fully annotated validation set, the model received conflicting/empty signals for most training samples. It couldn't learn meaningful bbox features from unlabeled (or improperly labeled) data, leading to the massive accuracy drop.

Here's a step-by-step solution to use your segmentation labels effectively:

Step 1: Generate High-Quality Pseudo Bounding Boxes from Segmentation Masks #

Every segmentation mask has a natural bbox: its axis-aligned bounding rectangle. You can generate these pseudo bboxes in your custom dataset loader to match Detectron2's expected annotation format.

Add this logic to your dataset's get_dicts function:

from detectron2.structures import BoxMode
import numpy as np

def get_custom_dicts(img_dir):
    # Your existing code to load images and segmentation masks
    for img_path, seg_masks, class_ids in your_data_loader():
        img = cv2.imread(img_path)
        height, width = img.shape[:2]
        objs = []
        
        for mask, cls_id in zip(seg_masks, class_ids):
            # Calculate the bounding box from the segmentation mask
            y_coords, x_coords = np.where(mask > 0)
            if len(x_coords) == 0 or len(y_coords) == 0:
                continue  # Skip empty masks
            x_min, x_max = np.min(x_coords), np.max(x_coords)
            y_min, y_max = np.min(y_coords), np.max(y_coords)
            
            objs.append({
                "bbox": [x_min, y_min, x_max, y_max],
                "bbox_mode": BoxMode.XYXY_ABS,
                "segmentation": mask,  # Keep the segmentation mask for extra supervision
                "category_id": cls_id
            })
        
        yield {
            "file_name": img_path,
            "height": height,
            "width": width,
            "annotations": objs
        }

This ensures your training set now has valid bbox annotations derived directly from its segmentation labels, matching the format of your validation set.

Step 2: Use a Two-Stage Training Strategy #

Directly mixing pseudo-labeled and fully labeled data can introduce noise. Instead, split training into two phases to avoid confusing the model:

Phase 1: Pre-Train with the Pseudo-Labeled Training Set #

First, let the model learn general object features using your training set's pseudo bboxes and segmentation masks. Modify your Detectron2 config like this:

from detectron2.config import get_cfg

cfg = get_cfg()
cfg.merge_from_file("detectron2/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml")
cfg.DATASETS.TRAIN = ("your_pseudo_labeled_train_set",)
cfg.DATASETS.TEST = ()  # No validation during pre-training
cfg.MODEL.ROI_HEADS.NUM_CLASSES = your_class_count
cfg.MODEL.MASK_ON = True  # Enable mask head to leverage segmentation labels
cfg.SOLVER.MAX_ITER = 12000  # Extend iterations to let the model learn from more data
cfg.SOLVER.BASE_LR = 0.002  # Standard learning rate for pre-training

Train the model with this config to build a foundation of object recognition.

Phase 2: Fine-Tune with the Fully Annotated Validation Set #

Next, take the pre-trained weights and fine-tune on your small but high-quality validation set to calibrate bbox accuracy. Adjust the config:

cfg.DATASETS.TRAIN = ("your_fully_annotated_val_set",)
cfg.DATASETS.TEST = ("your_fully_annotated_val_set",)
cfg.MODEL.WEIGHTS = "path/to/your_pretrained_model.pth"  # Load phase 1 weights
cfg.SOLVER.BASE_LR = 0.0001  # Use a small learning rate to preserve pre-trained features
cfg.SOLVER.MAX_ITER = 4000  # Fewer iterations needed for fine-tuning
cfg.SOLVER.WARMUP_ITERS = 500  # Warm up to avoid destabilizing the model

This phase will refine the model's bbox predictions using the accurate validation labels, bringing your accuracy back up.

Step 3: Validate Pseudo BBox Quality #

Before training, double-check that your pseudo bboxes are accurate. Visualize a few samples to ensure they align with the segmentation masks:

import cv2
from detectron2.utils.visualizer import Visualizer

for record in get_custom_dicts("your_train_img_dir"):
    img = cv2.imread(record["file_name"])
    v = Visualizer(img[:, :, ::-1], metadata=your_dataset_metadata, scale=0.6)
    out = v.draw_dataset_dict(record)
    cv2.imshow("Pseudo BBox Check", out.get_image()[:, :, ::-1])
    cv2.waitKey(0)

If you see misaligned bboxes (e.g., overly large boxes including too much background), tweak the mask-to-bbox logic (e.g., use contour approximation instead of axis-aligned rectangles).

Step 4: Optional: Try Semi-Supervised Training #

If you want to use both datasets simultaneously, Detectron2 supports semi-supervised training. You can configure it to use your pseudo-labeled training set as unlabeled data and your validation set as labeled data. Key config tweaks include:

cfg.DATASETS.TRAIN = ("your_fully_annotated_val_set",)
cfg.DATASETS.UNLABELED_TRAIN = ("your_pseudo_labeled_train_set",)
cfg.MODEL.SEMISUPNET.LOSS_WEIGHT = 0.5  # Balance loss between labeled and unlabeled data
cfg.MODEL.SEMISUPNET.Trainer = "SemisupTrainer"

This lets the model learn from the large training set while using the validation set to correct for pseudo-label noise.

内容的提问来源于stack exchange，提问作者AMRUTA BADGUJAR