Instructions to use zaibiii/aircraft-classification-densenet201 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use zaibiii/aircraft-classification-densenet201 with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://zaibiii/aircraft-classification-densenet201") - Notebooks
- Google Colab
- Kaggle
π©οΈ Aircraft Classification Model
This model is based on DenseNet201 architecture, fine-tuned on the FGVC Aircraft (10,000 images) dataset.
It can classify aircraft into different variants with high accuracy.
π Dataset
- Name: FGVC Aircraft Dataset
- Images: 10,000+
- Source: FGVC Aircraft
ποΈ Model Architecture
Base Model: DenseNet201 (pre-trained on ImageNet)
Custom Layers:
- GlobalAveragePooling2D
- Dense(1024) + Mish activation + BatchNorm + Dropout
- Dense(512) + Mish activation + BatchNorm + Dropout
- Final Dense layer with Softmax activation
Optimizer: Adam
Learning Rate Schedule: CosineDecay
Batch Size: 8
Epochs: 50
π Performance
- Achieved high validation accuracy.
- Used EarlyStopping, ReduceLROnPlateau, and ModelCheckpoint for optimal training.
π οΈ How to Use
import tensorflow as tf
# Load the model
model = tf.keras.models.load_model('aircraft_model_finetuned.h5', custom_objects={"mish": mish})
# Predict
prediction = model.predict(preprocessed_image)
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