# How to perform batch inference on GroundingDino model
**URL:**
**Author:**  [@tariksetia](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Ftariksetia)%5C
**Post date:** [June 7, 2024, 5:30pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fhow-to-perform-batch-inference-on-groundingdino-model%2F90940%2F1 "2024-06-07T17:30:13Z")
```auto
frames = [.....List of PIL.Image....]
inputs = processor(images=frames, text=text, return_tensors="pt").to(device)
with torch.no_grad():
outputs = model(**inputs)
print(outputs)
```
Error:
`RuntimeError: The size of tensor a (3) must match the size of tensor b (6) at non-singleton dimension 2`
---
**Author:**  [@royve](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Froyve)%5C
**Post date:** [July 25, 2024, 12:04am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fhow-to-perform-batch-inference-on-groundingdino-model%2F90940%2F2 "2024-07-25T00:04:52Z")
I have the same problem… any updates?
Thanks!
---
**Author:**  [@qubvel-hf](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fqubvel-hf)%5C
**Post date:** [July 25, 2024, 2:27pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fhow-to-perform-batch-inference-on-groundingdino-model%2F90940%2F3 "2024-07-25T14:27:36Z")
Hi @royve, thanks for the question, would be nice to have minimal reproducing example and environment 🙂
I was able to run a batched inference with the following env and code:
```auto
- `transformers` version: 4.44.0.dev0
- Platform: Linux-6.5.0-1020-aws-x86_64-with-glibc2.35
- Python version: 3.10.12
- PyTorch version (GPU?): 2.4.0+cu118 (True)
- GPU type: NVIDIA A10G
```
```python
import requests
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
model_id = "IDEA-Research/grounding-dino-tiny"
device = "cuda"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device)
image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(image_url, stream=True).raw)
images = [image, image]
texts = [
"a cat. a remote control.",
"a cat. a remote control. a sofa.",
]
inputs = processor(images=images, text=texts, padding=True, return_tensors="pt").to(device)
with torch.no_grad():
outputs = model(**inputs)
w, h = image.size
results = processor.post_process_grounded_object_detection(
outputs,
inputs.input_ids,
box_threshold=0.4,
text_threshold=0.3,
target_sizes=[(h, w), (h, w)],
)
print(results)
```