Instructions to use facebook/detr-resnet-101 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/detr-resnet-101 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="facebook/detr-resnet-101")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-101") model = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-101", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -45,8 +45,9 @@ import requests
|
|
| 45 |
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
| 46 |
image = Image.open(requests.get(url, stream=True).raw)
|
| 47 |
|
| 48 |
-
|
| 49 |
-
|
|
|
|
| 50 |
|
| 51 |
inputs = processor(images=image, return_tensors="pt")
|
| 52 |
outputs = model(**inputs)
|
|
|
|
| 45 |
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
| 46 |
image = Image.open(requests.get(url, stream=True).raw)
|
| 47 |
|
| 48 |
+
# you can specify the revision tag if you don't want the timm dependency
|
| 49 |
+
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-101", revision="no_timm")
|
| 50 |
+
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-101", revision="no_timm")
|
| 51 |
|
| 52 |
inputs = processor(images=image, return_tensors="pt")
|
| 53 |
outputs = model(**inputs)
|