Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- Javiai/failures-3D-print
+language:
+- en
+pipeline_tag: object-detection
+---
+
+#3D Failures detection model based on Yolov5
+
+
+## How to use
+
+### Download the model
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+
+repo_id = "Javiai/3dprintfails-yolo5vs"
+filenam = "model_torch.pt"
+
+model_path = hf_hub_download(repo_id=repo_id, filename=filename)
+```
+
+### Combine with the original model
+
+```python
+model = torch.hub.load('Ultralytics/yolov5', 'custom', model_path, verbose = False)
+```
+
+### Prepare an image
+
+#### From the original dataset
+```python
+
+from datasets import load_dataset
+
+dataset = load_dataset('Javiai/failures-3D-print')
+
+image = dataset["train"][0]["image"]
+```
+
+#### From local
+
+```python
+
+from PIL import Image
+
+image = Image.load("path/to/image")
+
+```
+
+### Inference and show the detection
+
+```python
+
+from PIL import ImageDraw
+
+draw = ImageDraw.Draw(image)
+
+detections = model(image)
+
+categories = [        
+  {'name': 'error', 'color': (0,0,255)},
+  {'name': 'extrusor', 'color': (0,255,0)},
+  {'name': 'part', 'color': (255,0,0)},
+  {'name': 'spaghetti', 'color': (0,0,255)}
+]
+
+for detection in detections.xyxy[0]:
+  x1, y1, x2, y2, p, category_id = detection
+  x1, y1, x2, y2, category_id = int(x1), int(y1), int(x2), int(y2), int(category_id)
+  draw.rectangle((x1, y1, x2, y2), 
+                 outline=categories[category_id]['color'], 
+                 width=1)
+  draw.text((x1, y1), categories[category_id]['name'], 
+            categories[category_id]['color'])
+
+image
+
+```
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63c9c08a5fdc575773c7549b/3ZSkBvN0o8sSpQjGxdwJx.png)
+