DETR's hands on Colab Notebook
时间: 2024-06-09 09:07:28 浏览: 9
Sure! Here is a step-by-step guide on how to use the DETR model in a Colab notebook:
1. Open a new Colab notebook.
2. First, we need to install PyTorch and torchvision by running the following command:
```
!pip install torch torchvision
```
3. Next, we need to install the detectron2 library. We can do this by running the following commands:
```
!pip install cython pyyaml==5.1
!pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
!pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.8/index.html
```
4. We also need to install Pillow, which is used for loading images:
```
!pip install Pillow
```
5. Now, let's download the pretrained DETR model and its configuration file:
```
!wget https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth
!wget https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.yaml
```
6. We will also need a sample image to test the model. You can upload your own image or use one of the images provided by the COCO dataset. For this tutorial, we will use the following image:
```
!wget https://images.unsplash.com/photo-1531745339476-241fd9a5aabe
```
7. Now, let's write the code to load the model and the image, and perform object detection:
```
import torch
import torchvision.transforms as T
from PIL import Image
# Load the model
model = torch.hub.load('facebookresearch/detr', 'detr_resnet50', pretrained=True)
# Load the image
img = Image.open('photo-1531745339476-241fd9a5aabe')
# Transform the image
transform = T.Compose([
T.Resize(800),
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
img = transform(img)
# Add batch dimension
img = img.unsqueeze(0)
# Pass the image through the model
outputs = model(img)
# Get the predicted labels and boxes
pred_logits = outputs['pred_logits'].detach().cpu().numpy()
pred_boxes = outputs['pred_boxes'].detach().cpu().numpy()
# Print the predicted labels and boxes
print(pred_logits)
print(pred_boxes)
```
8. Run the code and you should see the predicted labels and boxes printed in the output.
That's it! You have successfully used the DETR model in a Colab notebook. You can modify the code to use your own images and experiment with different settings to get better results.
相关推荐
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![py](https://img-home.csdnimg.cn/images/20210720083646.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)