Object Detection
Transformers
Safetensors
bevformer
vision
3d-object-detection
autonomous-driving
nuscenes
Instructions to use hasanfaesal/bevformer-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hasanfaesal/bevformer-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="hasanfaesal/bevformer-tiny")# pip install -U transformers accelerate # Load model directly from transformers import BevformerFor3DObjectDetection model = BevformerFor3DObjectDetection.from_pretrained("hasanfaesal/bevformer-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hasanfaesal/bevformer-tiny: direct link, hf CLI and curl.
- Browser
- Download file 330 Bytes
-
https://huggingface.co/hasanfaesal/bevformer-tiny/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://hasanfaesal/bevformer-tiny/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hasanfaesal/bevformer-tiny/resolve/main/preprocessor_config.json
330 Bytes
| { | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 123.675, | |
| 116.28, | |
| 103.53 | |
| ], | |
| "image_processor_type": "BevformerImageProcessor", | |
| "image_std": [ | |
| 58.395, | |
| 57.12, | |
| 57.375 | |
| ], | |
| "pad_size_divisor": 32, | |
| "resample": 2, | |
| "size": { | |
| "height": 450, | |
| "width": 800 | |
| } | |
| } | |