Update README.md

README.md

@@ -1,33 +1,33 @@
----
-license: apache-2.0
----
-
-This is the Offical weights of ConFiDeNet
-
-```python
-from PIL import Image
-import torch
-from transformers import ConFiDeNetForDepthEstimation, ConFiDeNetImageProcessor
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-image = Image.open("<Image Path>").convert("RGB")
-print(image.size)
-# image.save("image.jpg")
-
-image_processor = ConFiDeNetImageProcessor.from_pretrained("<Weight-Path>")
-model = ConFiDeNetForDepthEstimation.from_pretrained("<Weigh-Path>").to(device)
-
-inputs = image_processor(images=image, return_tensors="pt").to(device)
-
-with torch.no_grad():
-    outputs = model(**inputs)
-
-post_processed_output = image_processor.post_process_depth_estimation(
-    outputs, target_sizes=[(image.height, image.width)],
-)
-
-depth = post_processed_output[0]["predicted_depth_uint16"].detach().cpu().numpy()
-depth = Image.fromarray(depth, mode="I;16")
-depth.save("depth.png")
+---
+license: apache-2.0
+---
+
+This is the Offical weights of ConFiDeNet
+
+```python
+from PIL import Image
+import torch
+from transformers import ConFiDeNetForDepthEstimation, ConFiDeNetImageProcessor
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+image = Image.open("<Image Path>").convert("RGB")
+print(image.size)
+# image.save("image.jpg")
+
+image_processor = ConFiDeNetImageProcessor.from_pretrained("onkarsus13/ConFiDeNet-Large-VQ-32")
+model = ConFiDeNetForDepthEstimation.from_pretrained("onkarsus13/ConFiDeNet-Large-VQ-32").to(device)
+
+inputs = image_processor(images=image, return_tensors="pt").to(device)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+post_processed_output = image_processor.post_process_depth_estimation(
+    outputs, target_sizes=[(image.height, image.width)],
+)
+
+depth = post_processed_output[0]["predicted_depth_uint16"].detach().cpu().numpy()
+depth = Image.fromarray(depth, mode="I;16")
+depth.save("depth.png")
 ```