Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://github.com/Peterande/D-FINE/issues/108 below:

Performance Issue Fine-Tuning D-FINE S with custom dataset · Issue #108 · Peterande/D-FINE · GitHub

Hi everyone,

I’m fine-tuning the D-FINE S model initialized with Objects365 weights using a custom dataset, but I’m encountering significantly lower performance than expected.

Performance Comparison
In just a single epoch:

• D-FINE S achieves mAP@50=0.384.
• YOLOv11 S (initialized with COCO weights) achieves mAP@50=0.842.

1. Dataset Preparation:

• Converted custom dataset annotations (x1,y1,x2,y2) to COCO format (x1, y1, w, h).
• Since the dataset includes truncated images, I added in the train.py:
  ImageFile.LOAD_TRUNCATED_IMAGES = True
• This dataset contains only one class, category_id=0, so I set num_classes: 1 and remap_mscoco_category: False.

2. Configuration Changes:

• Using dfine_hgnetv2_s_obj2custom.yml as the base configuration.
• Modified only the dataset path and adjusted the batch size to 8 for both training and validation.

3. Training Command:

CUDA_VISIBLE_DEVICES=0 torchrun --master_port=7777 --nproc_per_node=1 train.py \
  -c configs/dfine/custom/objects365/dfine_hgnetv2_s_obj2custom.yml \
  --seed=0 -t dfine_s_obj365.pth

What could be causing the poor performance?
I’d appreciate any insights or suggestions for debugging this issue. Thanks in advance!

PS: I tried fine-tuning for 20 epochs, and the mAP@50 only improves slightly to 0.4. Using COCO weights instead of Objects365 weights does not lead to any improvement either.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4