Transformers
TensorBoard
Safetensors
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dinov2
multilabel-image-classification
multilabel
Generated from Trainer
Instructions to use lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze with Transformers:
# Load model directly from transformers import AutoImageProcessor, NewheadDinov2ForImageClassification processor = AutoImageProcessor.from_pretrained("lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze") model = NewheadDinov2ForImageClassification.from_pretrained("lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze") - Notebooks
- Google Colab
- Kaggle
| {"Acropore_branched": 0.351, "Acropore_digitised": 0.349, "Acropore_sub_massive": 0.123, "Acropore_tabular": 0.415, "Algae_assembly": 0.434, "Algae_drawn_up": 0.193, "Algae_limestone": 0.346, "Algae_sodding": 0.41, "Atra/Leucospilota": 0.586, "Bleached_coral": 0.408, "Blurred": 0.3, "Dead_coral": 0.407, "Fish": 0.466, "Homo_sapiens": 0.402, "Human_object": 0.343, "Living_coral": 0.208, "Millepore": 0.292, "No_acropore_encrusting": 0.227, "No_acropore_foliaceous": 0.462, "No_acropore_massive": 0.333, "No_acropore_solitary": 0.415, "No_acropore_sub_massive": 0.377, "Rock": 0.476, "Sand": 0.548, "Rubble": 0.417, "Sea_cucumber": 0.357, "Sea_urchins": 0.335, "Sponge": 0.152, "Syringodium_isoetifolium": 0.476, "Thalassodendron_ciliatum": 0.209, "Useless": 0.315} |