Image Segmentation
Transformers
Safetensors
inkdetection_resnet3d
feature-extraction
vesuvius-challenge
ink-detection
herculaneum
resnet3d
u-net
3d-segmentation
volumetric-imaging
custom_code
Instructions to use scrollprize/PHerc.1667-iteration-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scrollprize/PHerc.1667-iteration-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="scrollprize/PHerc.1667-iteration-2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("scrollprize/PHerc.1667-iteration-2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "inkdetection_resnet3d", | |
| "architectures": [ | |
| "InkDetectionModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_inkdetection.InkDetectionConfig", | |
| "AutoModel": "modeling_inkdetection.InkDetectionModel" | |
| }, | |
| "in_channels": 1, | |
| "input_depth": 62, | |
| "input_size": 256, | |
| "backbone_depth": 50, | |
| "backbone_channels": [ | |
| 256, | |
| 512, | |
| 1024, | |
| 2048 | |
| ], | |
| "num_classes": 1, | |
| "decoder_upscale": 1, | |
| "train_segment": "l_2", | |
| "train_inklabels": "l_2_inklabels2.png", | |
| "train_steps": 12396, | |
| "train_tiles": 8970, | |
| "train_loss_final": 0.4841, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.57.6" | |
| } |