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