yainage90
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fashion-object-detection-yolos-tiny

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 ---
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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 microsoft/conditional-detr-resnet-50.
+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=0.4, 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))
+```
+![sample_image](sample_image.png)