Add code example
This commit is contained in:
parent
073311fe2e
commit
0631208742
14
README.md
14
README.md
|
@ -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:
|
||||
|
||||
```python
|
||||
from transformers import ViTFeatureExtractor, ViTModel
|
||||
from transformers import DetrFeatureExtractor, DetrForObjectDetection
|
||||
from PIL import Image
|
||||
import requests
|
||||
|
||||
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
|
||||
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")
|
||||
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.
|
||||
|
|
Loading…
Reference in New Issue