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Browse files- README.md +58 -0
- config.json +18 -0
- pytorch_model.pth +3 -0
README.md
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---
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language: id
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tags:
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- image-classification
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- resnet18
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- pytorch
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- computer-vision
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license: mit
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datasets:
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- sumn2u/garbage-classification-v2
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metrics:
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- accuracy
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---
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# Garbage Classification
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Model ini adalah versi **ResNet18** yang di-fine-tune menggunakan dataset **Garbage Classification v2** dari Kaggle. Model ini mencapai hasil berikut pada dataset evaluasi (25% split):
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- **Loss:** 0.0020
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- **Accuracy:** 0.9364
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## Model description
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Model klasifikasi gambar untuk membedakan jenis-jenis sampah guna mendukung sistem pemilahan otomatis. Menggunakan arsitektur ResNet18 dengan teknik Transfer Learning.
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## Intended uses & limitations
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Digunakan untuk mengidentifikasi material tunggal dalam gambar. Limitasi mencakup kesulitan pada latar belakang yang ramai atau objek yang bertumpuk.
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## Training and evaluation data
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Dataset menggunakan 12,260 gambar yang dibagi menjadi 75% Training (9,195 gambar) dan 25% Testing (3,065 gambar).
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- **learning_rate:** 0.0001
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- **train_batch_size:** 32
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- **eval_batch_size:** 32
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- **optimizer:** Adam with weight_decay=1e-4
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- **lr_scheduler_type:** StepLR (step=5, gamma=0.5)
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- **num_epochs:** 15
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### Training results
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| Epoch | Training Loss | Train Accuracy |
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|-------|---------------|----------------|
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| 1 | 0.5585 | 82.53% |
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| 3 | 0.0790 | 97.77% |
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| 6 | 0.0121 | 99.85% |
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| 9 | 0.0043 | 99.98% |
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| 12 | 0.0029 | 99.99% |
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| 15 | 0.0020 | 100.00% |
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## Framework versions
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- **Pytorch:** 2.10.0+cu128
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- **Torchvision:** 0.25.0+cu128
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- **Python:** 3.12.12
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## Author
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**Ina Alyani**
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config.json
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{
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"model_type": "resnet18",
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"num_classes": 10,
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"class_names": [
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"battery",
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"biological",
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"cardboard",
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"clothes",
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"glass",
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"metal",
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"paper",
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"plastic",
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"shoes",
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"trash"
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],
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"task": "image-classification",
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"dataset": "Garbage Classification v2 (Kaggle)"
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}
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pytorch_model.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:765111768eda435650b42fbaf0d62ae5f0efeaca0e90329e5a3b7cd2b16b188b
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size 44806987
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