Instructions to use yainage90/fashion-object-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yainage90/fashion-object-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="yainage90/fashion-object-detection")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("yainage90/fashion-object-detection") model = AutoModelForObjectDetection.from_pretrained("yainage90/fashion-object-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: mit
language:
- en
pipeline_tag: object-detection
This model is fine-tuned version of microsoft/conditional-detr-resnet-50.
You can find details of model in this fashion-visual-search
I used modanet and fashionpedia for training this model.
The labels are ['bag', 'bottom', 'dress', 'hat', 'shoes', 'outer', 'top']
In the 96th epoch out of total of 100 epochs, the best score was achieved with mAP 0.7542. Therefore, it is believed that there is a little room for performance improvement.