My Journey with AI & Fashion MNIST
Urooj Fatima developed an image classification model using TensorFlow and Keras to distinguish fashion items from the Fashion-MNIST dataset. Initially, the model misclassified sneakers as bags or t-shirts due to limitations in spatial recognition. Through improved preprocessing and architectural understanding, the model's accuracy was enhanced, highlighting the importance of data quality and appropriate neural network design.
- ▪Urooj Fatima built an image classification model using TensorFlow and Keras.
- ▪The model initially struggled to differentiate sneakers from bags due to lack of spatial intelligence in the Dense (MLP) architecture.
- ▪Preprocessing improvements included Gaussian blurs, contrast enhancement, and proper image normalization to 28x28 pixels.
- ▪Understanding the limitations of non-convolutional models led to better performance and insights into AI model design.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3824216) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Urooj Fatima Posted on May 1 My Journey with AI & Fashion MNIST #ai #deeplearning #coding #tensorflow Can a computer "see" the difference between a Sneaker and a Bag? 👟 vs 👜 Well, it’s a lot harder than it looks! The Build: I recently built an Image Classification model using TensorFlow and Keras to categorize fashion items from the famous Fashion-MNIST dataset. The Brain: A Sequential Neural Network with Flatten, Dense, and Dropout layers.
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