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README.md
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---
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datasets:
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- zalando-datasets/fashion_mnist
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language:
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- en
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metrics:
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- accuracy
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pipeline_tag: image-classification
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tags:
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- fashion
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- clothes
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- fashion_mnist
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- CNN
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- Classification
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---
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# BeitForImageClassification
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## Model Structure
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### BeitModel
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- **Embeddings: BeitEmbeddings**
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- Uses patch embeddings with a `Conv2d` layer (3 input channels, 768 output channels, kernel size 16x16, stride 16x16).
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- Includes a dropout layer with probability 0.0.
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- **Encoder: BeitEncoder**
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- Contains 12 `BeitLayer` modules.
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- Each `BeitLayer` includes:
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- **Attention: BeitAttention**
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- `BeitSelfAttention` with linear layers for query, key, and value, dropout, and relative position bias.
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- `BeitSelfOutput` with a linear layer and dropout.
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- **Intermediate: BeitIntermediate**
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- Dense layer increasing dimensions from 768 to 3072, followed by GELU activation.
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- **Output: BeitOutput**
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- Dense layer reducing dimensions back to 768, with dropout.
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- **LayerNorm** applied before and after main operations.
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- **Drop Path** mechanism with varying probability across layers.
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- **Pooler: BeitPooler**
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- Contains a layer normalization.
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### Classifier: Linear
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- Linear layer mapping 768-dimensional embeddings to 10 output classes.
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## Detected Classes
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The model has been trained to detect the following classes:
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1. T-shirt / top
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2. Trouser
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3. Pullover
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4. Dress
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5. Coat
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6. Sandal
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7. Shirt
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8. Sneaker
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9. Bag
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10. Ankle boot
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