Instructions to use stevenbucaille/disk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stevenbucaille/disk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="stevenbucaille/disk", trust_remote_code=True)# Load model directly from transformers import AutoModelForKeypointDetection model = AutoModelForKeypointDetection.from_pretrained("stevenbucaille/disk", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "DiskForKeypointDetection" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_disk.DiskConfig", | |
| "AutoModelForKeypointDetection": "modeling_disk.DiskForKeypointDetection" | |
| }, | |
| "descriptor_decoder_dim": 128, | |
| "detection_threshold": 0.0, | |
| "max_num_keypoints": null, | |
| "model_type": "disk", | |
| "nms_window_size": 5, | |
| "pad_if_not_divisible": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.54.0.dev0", | |
| "weights": "depth" | |
| } | |