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---
library_name: transformers
license: mit
language:
- en
pipeline_tag: object-detection
base_model:
- hustvl/yolos-tiny
tags:
- object-detection
- fashion
- search
---
This model is fine-tuned version of hustvl/yolos-tiny.
You can find details of model in this github repo -> [fashion-visual-search](https://github.com/yainage90/fashion-visual-search)
And you can find fashion image feature extractor model -> [yainage90/fashion-image-feature-extractor](https://huggingface.co./yainage90/fashion-image-feature-extractor)
This model was trained using a combination of two datasets: [modanet](https://github.com/eBay/modanet) and [fashionpedia](https://fashionpedia.github.io/home/)
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 0.697400.
``` python
from PIL import Image
import torch
from transformers import YolosImageProcessor, YolosForObjectDetection
device = 'cpu'
if torch.cuda.is_available():
device = torch.device('cuda')
elif torch.backends.mps.is_available():
device = torch.device('mps')
ckpt = 'yainage90/fashion-object-detection-yolos-tiny'
image_processor = YolosImageProcessor.from_pretrained(ckpt)
model = YolosForObjectDetection.from_pretrained(ckpt).to(device)
image = Image.open('<path/to/image>').convert('RGB')
with torch.no_grad():
inputs = image_processor(images=[image], return_tensors="pt")
outputs = model(**inputs.to(device))
target_sizes = torch.tensor([[image.size[1], image.size[0]]])
results = image_processor.post_process_object_detection(outputs, threshold=0.85, target_sizes=target_sizes)[0]
items = []
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
score = score.item()
label = label.item()
box = [i.item() for i in box]
print(f"{model.config.id2label[label]}: {round(score, 3)} at {box}")
items.append((score, label, box))
```
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