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 config.json from hasanfaesal/bevformer-tiny: direct link, hf CLI and curl.
- Browser
- Download file 1.99 kB
-
https://huggingface.co/hasanfaesal/bevformer-tiny/resolve/main/config.json
- Command line
-
hf download hf://hasanfaesal/bevformer-tiny/config.json
-
curl -L -o config.json https://huggingface.co/hasanfaesal/bevformer-tiny/resolve/main/config.json
1.99 kB
| { | |
| "architectures": [ | |
| "BevformerFor3DObjectDetection" | |
| ], | |
| "as_two_stage": false, | |
| "backbone_config": { | |
| "_name_or_path": "", | |
| "architectures": null, | |
| "chunk_size_feed_forward": 0, | |
| "depths": [ | |
| 3, | |
| 4, | |
| 6, | |
| 3 | |
| ], | |
| "downsample_in_bottleneck": false, | |
| "downsample_in_first_stage": false, | |
| "dtype": null, | |
| "embedding_size": 64, | |
| "hidden_act": "relu", | |
| "hidden_sizes": [ | |
| 256, | |
| 512, | |
| 1024, | |
| 2048 | |
| ], | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1" | |
| }, | |
| "is_encoder_decoder": false, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1 | |
| }, | |
| "layer_type": "bottleneck", | |
| "model_type": "resnet", | |
| "num_channels": 3, | |
| "out_features": [ | |
| "stage4" | |
| ], | |
| "out_indices": [ | |
| 4 | |
| ], | |
| "output_attentions": false, | |
| "output_hidden_states": false, | |
| "problem_type": null, | |
| "return_dict": true, | |
| "stage_names": [ | |
| "stem", | |
| "stage1", | |
| "stage2", | |
| "stage3", | |
| "stage4" | |
| ] | |
| }, | |
| "bev_height": 50, | |
| "bev_width": 50, | |
| "can_bus_dim": 18, | |
| "can_bus_norm": true, | |
| "d_model": 256, | |
| "dropout": 0.1, | |
| "dtype": "float32", | |
| "ffn_dim": 512, | |
| "hidden_act": "relu", | |
| "id2label": { | |
| "0": "car", | |
| "1": "truck", | |
| "2": "construction_vehicle", | |
| "3": "bus", | |
| "4": "trailer", | |
| "5": "barrier", | |
| "6": "motorcycle", | |
| "7": "bicycle", | |
| "8": "pedestrian", | |
| "9": "traffic_cone" | |
| }, | |
| "label2id": null, | |
| "model_type": "bevformer", | |
| "num_attention_heads": 8, | |
| "num_cameras": 6, | |
| "num_decoder_layers": 6, | |
| "num_encoder_layers": 3, | |
| "num_feature_levels": 1, | |
| "num_points_in_bev": 8, | |
| "num_points_in_pillar": 4, | |
| "num_queries": 900, | |
| "point_cloud_range": [ | |
| -51.2, | |
| -51.2, | |
| -5.0, | |
| 51.2, | |
| 51.2, | |
| 3.0 | |
| ], | |
| "rotate_center": [ | |
| 100.0, | |
| 100.0 | |
| ], | |
| "rotate_prev_bev": true, | |
| "transformers_version": "5.21.0.dev0", | |
| "use_can_bus": true, | |
| "use_shift": true, | |
| "with_box_refine": true | |
| } | |