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README.md

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+---
+tags:
+- object-detection
+- fashion
+- conditional-detr
+license: apache-2.0
+datasets:
+- baselefre/new_embeddings_fixed_cats
+---
+
+# Fashion Object Detection Model
+
+Fine-tuned Conditional DETR model for detecting 8 fashion categories:
+- bag
+- bottom  
+- dress
+- hat
+- outer
+- shoes
+- top
+- accessory
+
+## Model Details
+- Base model: microsoft/conditional-detr-resnet-50
+- Training dataset: baselefre/new_embeddings_fixed_cats
+- Checkpoint: 18000 steps
+
+## Usage
+
+```python
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
+from PIL import Image
+import torch
+
+# Load model
+processor = AutoImageProcessor.from_pretrained("baselefre/objectdetectionaugmentedclean")
+model = AutoModelForObjectDetection.from_pretrained("baselefre/objectdetectionaugmentedclean")
+
+# Load image
+image = Image.open("your_image.jpg")
+
+# Inference
+inputs = processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+target_sizes = torch.tensor([image.size[::-1]])
+results = processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[0]
+
+# Print detections
+for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+    print(f"{model.config.id2label[label.item()]}: {score:.2f} at {box.tolist()}")
+```