Instructions to use PekingU/rtdetr_r101vd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PekingU/rtdetr_r101vd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="PekingU/rtdetr_r101vd")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("PekingU/rtdetr_r101vd") model = AutoModelForObjectDetection.from_pretrained("PekingU/rtdetr_r101vd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add model from convert_rt_detr_original_pytorch_checkpoint_to_pytorch.py
Browse files- model.safetensors +3 -0
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6e84ee96c6e65b7de0f29980e5cf6c89c9e497a752d7a3dc53f84d5ea7224285
|
| 3 |
+
size 307331000
|