Image Classification
Keras
LiteRT
TF-Keras
Safetensors
English
efficientnetv2-s
efficientnetv2
fgic
transfer-learning
gem-pooling
focal-loss
swa
grad-cam
calibration
temperature-scaling
computer-vision
tensorflow.js
Eval Results (legacy)
Instructions to use 0xgr3y/Arch-Building-Image-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use 0xgr3y/Arch-Building-Image-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://0xgr3y/Arch-Building-Image-Classification") - Notebooks
- Google Colab
- Kaggle
Upload V16 results/ — 12 visualization PNGs
Browse files- .gitattributes +8 -0
- results/augmentation_examples.png +3 -0
- results/confidence_per_class.png +0 -0
- results/confusion_matrix.png +3 -0
- results/gradcam_heatmaps.png +3 -0
- results/greyscope-labs-arch-building-classification-hunggingface-cnn-densenet121.png +3 -0
- results/inference_keras.png +3 -0
- results/inference_tflite.png +3 -0
- results/misclassification_examples.png +3 -0
- results/per_class_accuracy.png +0 -0
- results/reliability_diagram.png +0 -0
- results/roc_curves.png +0 -0
- results/training_curves.png +3 -0
- results/tsne_embedding.png +3 -0
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