Object Detection
Transformers
Safetensors
mmdet-bridge
feature-extraction
mmdetection
mask-rcnn
swin-transformer
instance-segmentation
custom_code
Instructions to use haiquanli/weed_swin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haiquanli/weed_swin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="haiquanli/weed_swin", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("haiquanli/weed_swin", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from haiquanli/weed_swin: direct link, hf CLI and curl.
- Browser
- Download file 346 Bytes
-
https://huggingface.co/haiquanli/weed_swin/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://haiquanli/weed_swin/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/haiquanli/weed_swin/resolve/main/preprocessor_config.json
346 Bytes
| { | |
| "image_processor_type": "MmdetImageProcessor", | |
| "auto_map": { | |
| "AutoImageProcessor": "image_processing_mmdet.MmdetImageProcessor" | |
| }, | |
| "size": { | |
| "height": 768, | |
| "width": 1024 | |
| }, | |
| "do_resize": true, | |
| "keep_ratio": true, | |
| "do_normalize": false, | |
| "do_rescale": false, | |
| "normalization_by": "MMDetection DetDataPreprocessor" | |
| } | |