Image Classification
Transformers
Safetensors
English
vit
vision
biology
ecology
phenology
plants
plant-phenology
leaf-phenology
iNaturalist
Eval Results (legacy)
Instructions to use phenobase/phenovisionL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phenobase/phenovisionL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="phenobase/phenovisionL") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("phenobase/phenovisionL") model = AutoModelForImageClassification.from_pretrained("phenobase/phenovisionL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add id2label [green_leaves, colored_leaves, breaking_leaf_buds] + multi_label_classification
Browse files- config.json +8 -6
config.json
CHANGED
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@@ -8,26 +8,28 @@
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"hidden_dropout_prob": 0.0,
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"hidden_size": 1024,
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"id2label": {
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"0": "
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"1": "
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"2": "
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},
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"image_size": 224,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"label2id": {
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"
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"
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"
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},
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"layer_norm_eps": 1e-12,
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"model_type": "vit",
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"num_attention_heads": 16,
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"num_channels": 3,
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"num_hidden_layers": 24,
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"patch_size": 16,
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"pooler_act": "tanh",
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"pooler_output_size": 768,
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"qkv_bias": true,
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"torch_dtype": "float32",
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"transformers_version": "4.52.4"
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"hidden_dropout_prob": 0.0,
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"hidden_size": 1024,
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"id2label": {
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"0": "green_leaves",
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"1": "colored_leaves",
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"2": "breaking_leaf_buds"
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},
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"image_size": 224,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"label2id": {
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"green_leaves": 0,
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"colored_leaves": 1,
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"breaking_leaf_buds": 2
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},
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"layer_norm_eps": 1e-12,
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"model_type": "vit",
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"num_attention_heads": 16,
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"num_channels": 3,
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"num_hidden_layers": 24,
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"num_labels": 3,
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"patch_size": 16,
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"pooler_act": "tanh",
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"pooler_output_size": 768,
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"problem_type": "multi_label_classification",
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"qkv_bias": true,
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"torch_dtype": "float32",
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"transformers_version": "4.52.4"
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