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README.md

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-# Garbage Classifier · EfficientNet‑V2‑S (torchvision)
+---
 
-Finetuned model for 10‑class garbage image classification.
+pipeline_tag: image-classification
+library_name: torchvision
+tags:
+  - image-classification
+  - efficientnet
+  - efficientnet-v2
+  - garbage
+  - waste-sorting
+metrics:
+  - accuracy
 
-> **Owner:** `attilaultzindur`   •   **Pushed:** 2025-05-05
+---
+
+# Garbage Classifier – EfficientNet‑V2‑S
+
+A finetuned EfficientNet‑V2‑S model that recognises **10 waste categories**
+(battery, glass, plastic, etc.) for smart recycling and sorting applications.
+
+| id | class      |
+| -: | ---------- |
+|  0 | battery    |
+|  1 | biological |
+|  2 | cardboard  |
+|  3 | clothes    |
+|  4 | glass      |
+|  5 | metal      |
+|  6 | paper      |
+|  7 | plastic    |
+|  8 | shoes      |
+|  9 | trash      |
+
+---
+
+## Quick Start
+
+```python
+from huggingface_hub import InferenceClient
+
+client = InferenceClient("attilaultzindur/garbage_classifier_effnetv2s_ft")
+
+with open("your_image.jpg", "rb") as f:
+    predictions = client.post(data={"inputs": f.read()})
+print(predictions)
+```
+
+Example output:
+
+```json
+[
+  {"label": "plastic", "score": 0.997},
+  {"label": "metal",   "score": 0.002},
+  …
+]
+```
+
+---
+
+## Model Details
+
+| Field                    | Value                                                     |
+| ------------------------ | --------------------------------------------------------- |
+| Architecture             | EfficientNet‑V2‑S (torchvision)                           |
+| Input size               | `3 × 224 × 224`                                           |
+| Normalisation            | mean = \[0.485 0.456 0.406], std = \[0.229 0.224 0.225]   |
+| Classification head      | `Linear(1280→256) → ReLU → Dropout(0.5) → Linear(256→10)` |
+| Best validation accuracy | **97.6 %** after 20 epochs                                |
+
+### Training summary
+
+* **Dataset:** [Garbage Classification v2 (Kaggle)](https://www.kaggle.com/datasets/sumn2u/garbage-classification-v2)
+  split 80 % train / 20 % val
+* **Augmentations:** RandomResizedCrop, ColorJitter, RandomAffine, HorizontalFlip
+* **Optimiser:** Adam, LR = 1 e‑4
+* **Frozen layers:** first 70 % of feature blocks
+* **Hardware:** single NVIDIA GPU
+
+---
+
+## Reproduce
+
+The full training script is provided in `train_script.py`.
+Run it with the same hyper‑parameters to reproduce the checkpoint.
+
+---
+
+## Licence
+
+No licence has been specified yet.
+Add an appropriate open‑source licence before using the model in production.