Update README.md

README.md

@@ -1,3 +1,41 @@
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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# Camera Level
+
+This model predicts an image's cinematic camera level [ground, hip, shoulder, eye, aerial].  The model is a DinoV2 with registers backbone (initiated with `facebook/dinov2-with-registers-large` weights) and trained on a diverse set of five thousand human-annotated images.
+
+## How to use:
+```python
+
+import torch
+from PIL import Image
+from transformers import AutoImageProcessor
+from transformers import AutoModelForImageClassification
+
+image_processor = AutoImageProcessor.from_pretrained("facebook/dinov2-with-registers-large")
+model = AutoModelForImageClassification.from_pretrained('aslakey/camera_level')
+model.eval()
+
+# Model labels: [ground, hip, shoulder, eye, aerial]
+image = Image.open('cinematic_shot.jpg')
+inputs = image_processor(image, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# technically multi-label training, but argmax works too!
+predicted_label = outputs.logits.argmax(-1).item()
+print(model.config.id2label[predicted_label])
+```
+
+## Performance:
+
+
+| Category | Precision | Recall |
+|----------|-----------|--------|
+| ground      | 65%        | 51%     |
+| hip         | 69%       | 62%    |
+| shoulder        | 68%       | 74%    |
+| eye        | 51%       | 39%    |
+| aerial        | 89%       | 76%    |