Image Segmentation
Transformers
Safetensors
fc_clip
panoptic-segmentation
open-vocabulary
zero-shot
clip
convnext
mask2former
fiftyone
custom_code
Instructions to use Voxel51/fc-clip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Voxel51/fc-clip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Voxel51/fc-clip", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Voxel51/fc-clip", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "fc_clip", | |
| "auto_map": { | |
| "AutoConfig": "configuration_fc_clip.FCCLIPConfig", | |
| "AutoModel": "modeling_fc_clip.FCCLIPForPanopticSegmentation" | |
| }, | |
| "clip_model_name": "convnext_large_d_320", | |
| "clip_pretrained": "laion2b_s29b_b131k_ft_soup", | |
| "clip_embedding_dim": 768, | |
| "conv_dim": 256, | |
| "mask_dim": 256, | |
| "transformer_dropout": 0.0, | |
| "transformer_nheads": 8, | |
| "transformer_dim_feedforward": 1024, | |
| "transformer_enc_layers": 6, | |
| "transformer_in_features": ["res3", "res4", "res5"], | |
| "in_features": ["res2", "res3", "res4", "res5"], | |
| "common_stride": 4, | |
| "hidden_dim": 256, | |
| "num_queries": 250, | |
| "nheads": 8, | |
| "dim_feedforward": 2048, | |
| "dec_layers": 9, | |
| "pre_norm": false, | |
| "enforce_input_project": false, | |
| "num_classes": 133, | |
| "num_thing_classes": 80, | |
| "geometric_ensemble_alpha": 0.4, | |
| "geometric_ensemble_beta": 0.8, | |
| "ensemble_on_valid_mask": false, | |
| "object_mask_threshold": 0.8, | |
| "overlap_threshold": 0.8, | |
| "pixel_mean": [122.7709383, 116.7460125, 104.09373615], | |
| "pixel_std": [68.5005327, 66.6321579, 70.32316305] | |
| } | |