SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Fix code snippet for Grounding DINO (#32229)

Browse Source 
Fix code snippet for grounding-dino
This commit is contained in:
Pavel Iakubovskii
2024-07-25 19:20:47 +01:00
committed by GitHub
parent 3a83ec48a6
commit 9d6c0641c4
1 changed files with 28 additions and 21 deletions
Download Patch File Download Diff File Expand all files Collapse all files

49
docs/source/en/model_doc/grounding-dino.md
View File

@@ -41,33 +41,40 @@ The original code can be found [here](https://github.com/IDEA-Research/Grounding
Here's how to use the model for zero-shot object detection: Here's how to use the model for zero-shot object detection:
```python ```python
import requests >>> import requests
import torch >>> import torch
from PIL import Image >>> from PIL import Image
from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection, >>> from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
model_id = "IDEA-Research/grounding-dino-tiny" >>> model_id = "IDEA-Research/grounding-dino-tiny"
>>> device = "cuda"
processor = AutoProcessor.from_pretrained(model_id) >>> processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device) >>> model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device)
image_url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(image_url, stream=True).raw) >>> image = Image.open(requests.get(image_url, stream=True).raw)
# Check for cats and remote controls >>> # Check for cats and remote controls
text = "a cat. a remote control." >>> text = "a cat. a remote control."
inputs = processor(images=image, text=text, return_tensors="pt").to(device) >>> inputs = processor(images=image, text=text, return_tensors="pt").to(device)
with torch.no_grad(): >>> with torch.no_grad():
outputs = model(**inputs) ... outputs = model(**inputs)
results = processor.post_process_grounded_object_detection( >>> results = processor.post_process_grounded_object_detection(
outputs, ... outputs,
inputs.input_ids, ... inputs.input_ids,
box_threshold=0.4, ... box_threshold=0.4,
text_threshold=0.3, ... text_threshold=0.3,
target_sizes=[image.size[::-1]] ... target_sizes=[image.size[::-1]]
) ... )
>>> print(results)
[{'boxes': tensor([[344.6959, 23.1090, 637.1833, 374.2751],
[ 12.2666, 51.9145, 316.8582, 472.4392],
[ 38.5742, 70.0015, 176.7838, 118.1806]], device='cuda:0'),
'labels': ['a cat', 'a cat', 'a remote control'],
'scores': tensor([0.4785, 0.4381, 0.4776], device='cuda:0')}]
``` ```
## Grounded SAM ## Grounded SAM