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Add code example

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Niels Rogge 2021-06-01 10:14:31 +00:00 committed by huggingface-web
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@ -28,16 +28,22 @@ You can use the raw model for object detection. See the [model hub](https://hugg
Here is how to use this model: Here is how to use this model:
```python ```python
from transformers import ViTFeatureExtractor, ViTModel from transformers import DetrFeatureExtractor, DetrForObjectDetection
from PIL import Image from PIL import Image
import requests import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg' url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw) image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k') feature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-50')
model = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-50')
inputs = feature_extractor(images=image, return_tensors="pt") inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs) outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
# model predicts bounding boxes and corresponding COCO classes
logits = outputs.logits
bboxes = outputs.pred_boxes
``` ```
Currently, both the feature extractor and model support PyTorch. Currently, both the feature extractor and model support PyTorch.