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models hibou l
Hibou-L is a foundational vision transformer developed for digital pathology, designed to generate high-quality feature representations from histology image patches. These representations can be leveraged for a range of downstream tasks, including classification, segmentation, and detection.
Built on the ViT-B/14 architecture, Hibou-L is a digital pathology foundation model pretrained on a 1.2B image private dataset using the DINOv2 framework. The model processes 224 × 224 input patches and generates high-quality feature representations for downstream histopathological tasks.
To understand the capabilities of Hibou-L, we evaluated it across a range of public digital pathology benchmarks. The model was tested at both patch-level and slide-level granularity to assess its generalization and diagnostic utility across different cancer types and tissue modalities.
| Category | Benchmark | Hibou-L |
|---|---|---|
| Patch-level | CRC-100K | 96.6 |
| PCAM | 95.3 | |
| MHIST | 85.8 | |
| MSI-CRC | 79.3 | |
| MSI-STAD | 82.9 | |
| TIL-DET | 94.2 | |
| Slide-level | BRCA | 94.6 |
| NSCLC | 96.9 | |
| RCC | 99.6 |
Single PDB Input:
data = {
"input_data": {
"columns": ["image"],
"data": [
["base64_encoded_image_string"]
]
}
}Multiple PDB Input:
data = {
"input_data": {
"columns": ["image"],
"data": [
["base64_encoded_image_string_1"],
["base64_encoded_image_string_2"]
]
}
}Output Sample:
[
{
"image_features": [2.6749861240386963, -0.7507642507553101, 0.2108164280653, ...]
}
]Output Processing Example:
def process_hibou_predictions(result):
"""Process Hibou-L embedding predictions."""
if not result:
print("No predictions found")
return
# Handle the response format: [{'image_features': [embedding_list]}]
if isinstance(result, list) and len(result) > 0:
first_result = result[0]
if isinstance(first_result, dict) and 'image_features' in first_result:
embeddings = first_result['image_features']
embedding_dim = len(embeddings)
print(f"Received embeddings with dimension: {embedding_dim}")
# Calculate statistics
embedding_array = np.array(embeddings)
print(f"Embedding statistics:")
print(f" - Mean: {np.mean(embedding_array):.4f}")
print(f" - Std: {np.std(embedding_array):.4f}")
print(f" - Min: {np.min(embedding_array):.4f}")
print(f" - Max: {np.max(embedding_array):.4f}")
return embeddings
else:
print(f"Unexpected result format - missing 'image_features' key")
print(f"Available keys: {list(first_result.keys()) if isinstance(first_result, dict) else 'Not a dict'}")
else:
print(f"Unexpected result format: {type(result)}")
return None
def visualize_embeddings_results(original_image, embeddings, save_path=None):
"""Visualize the embedding results with distribution plot."""
plt.figure(figsize=(15, 6))
plt.subplot(1, 3, 1)
plt.imshow(original_image)
plt.title('Original Image')
plt.axis('off')
plt.subplot(1, 3, 2)
plt.imshow(original_image)
embedding_dim = len(embeddings) if embeddings else 0
plt.title(f'Processed - Embedding dim: {embedding_dim}')
plt.axis('off')
# Show embedding distribution
plt.subplot(1, 3, 3)
if embeddings:
embedding_array = np.array(embeddings)
plt.hist(embedding_array, bins=50, alpha=0.7, color='blue')
plt.title(f'Embedding Distribution\nMean: {np.mean(embedding_array):.3f}\nStd: {np.std(embedding_array):.3f}')
plt.xlabel('Embedding Value')
plt.ylabel('Frequency')
else:
plt.text(0.5, 0.5, 'No embeddings', ha='center', va='center')
plt.axis('off')
plt.tight_layout()
if save_path:
plt.savefig(save_path, bbox_inches='tight', dpi=300)
plt.show()- Supported Data Input Format
-
Input Format: The model accepts histopathology images in png. Images can be provided as base64-encoded image strings.
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Output Format: The model generates dense embedding vectors representing the visual features of histopathology image in 768-dimensional feature vectors.
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Data Sources and Technical Details: For comprehensive information about training datasets, model architecture, and validation results, refer to the official hibou repository
Version: 2
task : embeddings industry : health-and-life-sciences Preview licenseDescription : Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity; "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of outstanding shares, or (iii) beneficial ownership. "You" or "Your" means an individual or Legal Entity exercising permissions granted by this License. "Source" form means the preferred form for making modifications, including software source code, documentation source, and configuration files. "Object" form means any form resulting from mechanical transformation or translation of a Source form, including compiled object code, generated documentation, and conversions to other media types. "Work" means the work of authorship, whether in Source or Object form, made available under the License. "Derivative Works" means any work based on or derived from the Work with modifications representing an original authorship; excluding works that remain separable or merely link to the Work. "Contribution" means any work submitted to Licensor for inclusion in the Work. "Contributor" means Licensor and any entity whose Contribution is included in the Work. 2. Grant of Copyright License: each Contributor grants You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work in Source or Object form. 3. Grant of Patent License: each Contributor grants You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated) patent license to make, use, sell, import, and otherwise transfer the Work. This license applies only to patent claims necessarily infringed by the Contribution. If You initiate patent litigation alleging infringement by the Work, Your patent licenses terminate. 4. Redistribution: You may distribute the Work or Derivative Works if You (a) provide a copy of this License, (b) state changes made, (c) retain copyright/patent/trademark/attribution notices, and (d) include NOTICE file contents if applicable. You may add Your own attribution notices. 5. Submission of Contributions: unless stated otherwise, Contributions submitted are under this License. 6. Trademarks: this License does not grant rights to use Licensor’s trademarks. 7. Disclaimer of Warranty: the Work is provided "AS IS", without warranties or conditions, express or implied, including title, non-infringement, merchantability, or fitness for a purpose. 8. Limitation of Liability: Contributors are not liable for damages arising from this License or use of the Work. 9. Accepting Warranty or Additional Liability: You may offer support/warranty/indemnity for a fee, but only on Your own behalf and must indemnify Contributors. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work: include the following notice. Copyright [yyyy] [name of copyright owner]. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy at http://www.apache.org/licenses/LICENSE-2.0. Unless required by law or agreed to in writing, software distributed under the License is provided on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, express or implied. See the License for the specific language governing permissions and limitations under the License. inference_supported_envs : ['hf'] license : apache-2.0 author : HistAI hiddenlayerscanned SharedComputeCapacityEnabled inference_compute_allow_list : ['Standard_NC4as_T4_v3', 'Standard_NC8as_T4_v3', 'Standard_NC16as_T4_v3', 'Standard_NC64as_T4_v3', 'Standard_NC6s_v3', 'Standard_NC12s_v3', 'Standard_NC24s_v3', 'Standard_NC24ads_A100_v4', 'Standard_NC48ads_A100_v4', 'Standard_NC96ads_A100_v4', 'Standard_ND96asr_v4', 'Standard_ND96amsr_A100_v4', 'Standard_ND40rs_v2', 'Standard_NC40ads_H100_v5', 'Standard_NC80adis_H100_v5', 'Standard_ND96isr_H100_v5']
View in Studio: https://ml.azure.com/registries/azureml/models/hibou-l/version/2
License: apache-2.0
inference-min-sku-spec: 4|1|28|64
inference-recommended-sku: Standard_NC4as_T4_v3, Standard_NC8as_T4_v3, Standard_NC16as_T4_v3, Standard_NC64as_T4_v3, Standard_NC6s_v3, Standard_NC12s_v3, Standard_NC24s_v3, Standard_NC24ads_A100_v4, Standard_NC48ads_A100_v4, Standard_NC96ads_A100_v4, Standard_ND96asr_v4, Standard_ND96amsr_A100_v4, Standard_ND40rs_v2, Standard_NC40ads_H100_v5, Standard_NC80adis_H100_v5, Standard_ND96isr_H100_v5
languages: en
SharedComputeCapacityEnabled: True