Add Custom Inception-style CNN project card

README.md

@@ -1,3 +1,79 @@
 ---
 license: mit
+language:
+- en
+pipeline_tag: image-classification
+library_name: keras
+metrics:
+- accuracy
+tags:
+- computer-vision
+- satellite-imagery
+- remote-sensing
+- xview
+- cnn
+- inception
+- tensorflow
+- trained-from-scratch
 ---
+
+# Custom Inception-style CNN for Satellite Image Classification
+
+A custom convolutional neural network trained from scratch to classify satellite image crops into 13 xView categories. Developed as a Deep Learning course project at Universidad Politécnica de Madrid (UPM), this architecture uses parallel convolutional branches to extract features at multiple spatial scales.
+
+## Architecture
+
+- **Input:** 128 × 128 RGB image crops.
+- **Output:** softmax probabilities over 13 classes.
+- **Parameters:** 4,529,325, as recorded in the notebook.
+- **Feature extractor:** convolutional stem followed by seven custom Inception-style modules.
+- **Parallel branches:** 1 × 1 convolution; 1 × 1 followed by 3 × 3 convolution; 1 × 1 followed by two 3 × 3 convolutions; and max pooling followed by 1 × 1 convolution.
+- **Classification head:** global average pooling, dropout (0.4), and a dense softmax layer.
+- **Framework:** TensorFlow / Keras.
+
+This is a custom Inception-inspired architecture, not the standard InceptionV3 model. It classifies individual image crops rather than detecting objects in full satellite scenes.
+
+## Training
+
+The notebook uses Adam with an initial learning rate of 0.001 and categorical cross-entropy with label smoothing of 0.1. Training is configured for up to 50 epochs with a batch size of 64. The saved training log identifies epoch 47 as the best epoch by validation accuracy.
+
+## Results
+
+The notebook compares three custom CNN architectures on the same validation split:
+
+| Architecture | Validation accuracy |
+| --- | --- |
+| ResNet-style | 18.67% |
+| VGG-style | 68.53% |
+| **Inception-style** | **72.69%** |
+
+The selected Inception-style model also achieved **75.63% macro recall** and **75.48% macro precision** in the recorded validation evaluation.
+
+These results come from the original experiments in `CNN Best model.ipynb`. They refer to the course's 13-class classification setup, not the full xView object detection benchmark.
+
+## Classes
+
+Cargo plane, small car, bus, truck, motorboat, fishing vessel, dump truck, excavator, building, helipad, storage tank, shipping container, and pylon.
+
+## Project materials
+
+- Training and evaluation notebook: `CNN Best model.ipynb`.
+- Project report: `Report_ImageRecognitionAndObjectDetectiononthexViewSatelliteDataset.pdf`.
+
+The notebook documents the architecture definitions, training experiments, confusion matrices, and per-class evaluation. Results from any subsequent training run should be evaluated independently.
+
+## Authors
+
+Melen Laclais, Léo Lamy, and Adrián García-Pozuelo Fornieles.
+
+## License and attribution
+
+The MIT license designation applies to original project code only. Third-party code and course materials retain their respective terms.
+
+xView imagery and annotations remain under **CC BY-NC-SA 4.0**, including any dataset images reproduced in notebooks or the report.
+
+- [xView dataset](https://xviewdataset.org/)
+- [Official xView dataset license terms](https://challenge.xviewdataset.org/rules)
+- [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+
+Dataset reference: Lam et al., *xView: Objects in Context in Overhead Imagery* (2018).