Image Segmentation
Transformers
PyTorch
repcontur
feature-extraction
medical
radiology
ct
3d
tumor-segmentation
referring-segmentation
report-generation
visual-question-answering
custom_code
Instructions to use Oncosoft-Research/RepConTUR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Oncosoft-Research/RepConTUR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Oncosoft-Research/RepConTUR", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Oncosoft-Research/RepConTUR", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Oncosoft-Research/RepConTUR: direct link, hf CLI and curl.
- Browser
- Download file 2.82 kB
-
https://huggingface.co/Oncosoft-Research/RepConTUR/resolve/main/config.json
- Command line
-
hf download hf://Oncosoft-Research/RepConTUR/config.json
-
curl -L -o config.json https://huggingface.co/Oncosoft-Research/RepConTUR/resolve/main/config.json
2.82 kB
| { | |
| "architectures": [ | |
| "RepConTURModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_repcontur.RepConTURConfig", | |
| "AutoModel": "modeling_repcontur.RepConTURModel" | |
| }, | |
| "ct_encoder": { | |
| "codebook_size": 8192, | |
| "dim": 512, | |
| "dim_head": 32, | |
| "encoder_type": "ctvit", | |
| "heads": 8, | |
| "image_size": 240, | |
| "patch_size": 20, | |
| "spatial_depth": 4, | |
| "temporal_depth": 4, | |
| "temporal_patch_size": 10 | |
| }, | |
| "ct_pooler": { | |
| "mlp_depth": 2, | |
| "n_heads": 8, | |
| "n_queries": 256 | |
| }, | |
| "decoder_layer": 4, | |
| "dtype": "bfloat16", | |
| "generation_kwargs": {}, | |
| "img_context_token_id": 151667, | |
| "internvl_model_path": "OpenGVLab/InternVL2_5-1B", | |
| "llm_hidden_size": 896, | |
| "model_type": "repcontur", | |
| "num_heads": 32, | |
| "num_maskformer_stages": 5, | |
| "query_dim": 2048, | |
| "seg_token_idx": 151674, | |
| "special_tokens": [ | |
| "[SEG]", | |
| "<p>", | |
| "</p>", | |
| "<vp>", | |
| "</vp>" | |
| ], | |
| "template": { | |
| "INSTRUCTION": "<|user|>\n{input}<|end|>\n<|assistant|>\n", | |
| "SEP": "\n", | |
| "STOP_WORDS": [ | |
| "<|end|>" | |
| ], | |
| "SUFFIX": "<|end|>", | |
| "SUFFIX_AS_EOS": true, | |
| "SYSTEM": "<|system|>\n{system}<|end|>\n" | |
| }, | |
| "text_embedding_dim": 2560, | |
| "transformers_version": "4.57.1", | |
| "vocab_size": 151679, | |
| "voxtell_arch_kwargs": { | |
| "conv_bias": true, | |
| "conv_op": "torch.nn.modules.conv.Conv3d", | |
| "dropout_op": null, | |
| "dropout_op_kwargs": null, | |
| "features_per_stage": [ | |
| 32, | |
| 64, | |
| 128, | |
| 256, | |
| 320, | |
| 320 | |
| ], | |
| "kernel_sizes": [ | |
| [ | |
| 3, | |
| 3, | |
| 3 | |
| ], | |
| [ | |
| 3, | |
| 3, | |
| 3 | |
| ], | |
| [ | |
| 3, | |
| 3, | |
| 3 | |
| ], | |
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| 3, | |
| 3, | |
| 3 | |
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| 3, | |
| 3 | |
| ], | |
| [ | |
| 3, | |
| 3, | |
| 3 | |
| ] | |
| ], | |
| "n_blocks_per_stage": [ | |
| 1, | |
| 3, | |
| 4, | |
| 6, | |
| 6, | |
| 6 | |
| ], | |
| "n_conv_per_stage_decoder": [ | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1 | |
| ], | |
| "n_stages": 6, | |
| "nonlin": "torch.nn.LeakyReLU", | |
| "nonlin_kwargs": { | |
| "inplace": true | |
| }, | |
| "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d", | |
| "norm_op_kwargs": { | |
| "affine": true, | |
| "eps": 1e-05 | |
| }, | |
| "strides": [ | |
| [ | |
| 1, | |
| 1, | |
| 1 | |
| ], | |
| [ | |
| 2, | |
| 2, | |
| 2 | |
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| 2 | |
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| [ | |
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| 2 | |
| ], | |
| [ | |
| 2, | |
| 2, | |
| 2 | |
| ], | |
| [ | |
| 2, | |
| 2, | |
| 2 | |
| ] | |
| ] | |
| }, | |
| "voxtell_decoder_layer": 4, | |
| "voxtell_kw_requires_import": [ | |
| "conv_op", | |
| "norm_op", | |
| "dropout_op", | |
| "nonlin" | |
| ], | |
| "voxtell_num_heads": 32, | |
| "voxtell_num_maskformer_stages": 5, | |
| "voxtell_query_dim": 2048, | |
| "voxtell_text_embedding_dim": 2560 | |
| } | |