Instructions to use joyjitroy/Plant_Seedlings_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use joyjitroy/Plant_Seedlings_Classification with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://joyjitroy/Plant_Seedlings_Classification") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- en
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pipeline_tag: image-classification
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license: mit
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datasets:
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- ssharma2020/Plant-Seedlings-Dataset
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metrics:
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- accuracy
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library_name: keras
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sample = np.expand_dims(images[0], axis=0) # (1, H, W, 3)
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prob = model.predict(sample, verbose=0)[0]
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pred_idx = int(np.argmax(prob))
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print(f"[Dataset sample 0] Predicted: {idx_to_class[pred_idx]} | confidence: {prob[pred_idx]:.4f}")
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- en
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pipeline_tag: image-classification
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license: mit
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metrics:
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- accuracy
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library_name: keras
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sample = np.expand_dims(images[0], axis=0) # (1, H, W, 3)
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prob = model.predict(sample, verbose=0)[0]
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pred_idx = int(np.argmax(prob))
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print(f"[Dataset sample 0] Predicted: {idx_to_class[pred_idx]} | confidence: {prob[pred_idx]:.4f}")
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