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Update to image processor

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Niels Rogge 2022-12-16 20:05:33 +00:00 committed by huggingface-web
parent cc12a6ee9d
commit c6715f5703
1 changed files with 7 additions and 8 deletions
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@ -37,7 +37,7 @@ 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 DetrFeatureExtractor, DetrForObjectDetection from transformers import DetrImageProcessor, DetrForObjectDetection
import torch import torch
from PIL import Image from PIL import Image
import requests import requests
@ -45,24 +45,23 @@ 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 = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-101") processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-101")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-101") model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-101")
inputs = feature_extractor(images=image, return_tensors="pt") inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs) outputs = model(**inputs)
# convert outputs (bounding boxes and class logits) to COCO API # convert outputs (bounding boxes and class logits) to COCO API
# let's only keep detections with score > 0.9
target_sizes = torch.tensor([image.size[::-1]]) target_sizes = torch.tensor([image.size[::-1]])
results = feature_extractor.post_process(outputs, target_sizes=target_sizes)[0] results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()] box = [round(i, 2) for i in box.tolist()]
# let's only keep detections with score > 0.9 print(
if score > 0.9:
print(
f"Detected {model.config.id2label[label.item()]} with confidence " f"Detected {model.config.id2label[label.item()]} with confidence "
f"{round(score.item(), 3)} at location {box}" f"{round(score.item(), 3)} at location {box}"
) )
``` ```
This should output (something along the lines of): This should output (something along the lines of):
``` ```