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-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scrollprize/PHerc.1667-iteration-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="scrollprize/PHerc.1667-iteration-1", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("scrollprize/PHerc.1667-iteration-1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 646 Bytes
9db19e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"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_inklabels.png",
"train_steps": 12396,
"train_tiles": 3396,
"train_loss_final": 0.4219,
"torch_dtype": "float32",
"transformers_version": "4.57.6"
} |