Instructions to use abhash-rai/traffic-congestion-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use abhash-rai/traffic-congestion-classifier with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://abhash-rai/traffic-congestion-classifier") - Notebooks
- Google Colab
- Kaggle
Traffic Congestion Model
This model is designed for processing grayscale images with duplicated channels (single-channel replication). It requires input images of size 200 by 200 pixels.
Classes
The model makes predictions on the following classes:
congested_traffic: Indicates traffic congestion.traffic_unrelated: Indicates an absence of congestion-related traffic.uncongested_traffic: Indicates clear or uncongested traffic conditions.
You can use this model for tasks related to traffic congestion detection and classification.
Training Notebook
If you're interested in the details of how this model was trained, you can find the training notebook here.
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