turhancan97
/

vit-tiny-lora-food101

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 ---
+license: apache-2.0
+library_name: peft
+base_model: WinKawaks/vit-tiny-patch16-224
+tags:
+  - lora
+  - peft
+  - image-classification
+  - vit
+  - food101
+datasets:
+  - food101
+pipeline_tag: image-classification
+metrics:
+  - accuracy
 ---
+# ViT-tiny LoRA adapter on Food-101
+A LoRA adapter that teaches [`WinKawaks/vit-tiny-patch16-224`](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) to classify images from the [Food-101](https://huggingface.co/datasets/food101) dataset (101 food categories) while leaving the original pretrained weights mathematically untouched.
+- **Base model:** `WinKawaks/vit-tiny-patch16-224` (~5.7M params)
+- **Dataset:** [Food-101](https://huggingface.co/datasets/food101) (75,750 train / 25,250 test, 101 classes)
+- **Method:** LoRA on attention `query` + `value` projections + a fresh 101-way classification head
+- **Demo Space:** [`turhancan97/vit-tiny-imagenet-demo`](https://huggingface.co/spaces/turhancan97/vit-tiny-imagenet-demo)
+## How it works
+The backbone is never fine-tuned. Instead a low-rank update $\Delta W = BA$ (with rank $r = 8$) is added to each attention projection, and a separate 101-class linear head is trained on top of the pooled CLS features. The full artifact is tiny (~1–2 MB) and additive — disabling the adapter at inference time recovers the exact original ImageNet-1k model.
+```text
+adapter_config.json           # PEFT LoRA config
+adapter_model.safetensors     # LoRA weights (B, A matrices)
+classifier.pt                 # 101-way Linear head (state_dict)
+labels.json                   # {"0": "apple_pie", "1": "baby_back_ribs", ...}
+preprocessor_config.json      # image processor (224x224, standard ImageNet norm)
+```
+## Training
+Trained with the script at [`turhancan97/vit-tiny-imagenet-demo/train_lora.py`](https://huggingface.co/spaces/turhancan97/vit-tiny-imagenet-demo/blob/main/train_lora.py):
+```bash
+python train_lora.py \
+  --rank 8 --alpha 16 --dropout 0.1 \
+  --target-modules query value \
+  --epochs 5 --batch-size 64 --lr 5e-4 \
+  --warmup-ratio 0.03 --weight-decay 0.0 \
+  --push-to-hub turhancan97/vit-tiny-lora-food101
+```
+**Hyperparameters**
+| Setting | Value |
+|---|---|
+| LoRA rank | 8 |
+| LoRA alpha | 16 |
+| LoRA dropout | 0.1 |
+| Target modules | `query`, `value` |
+| Optimizer | AdamW (HF `Trainer` default) |
+| Learning rate | 5e-4 |
+| Batch size | 64 |
+| Epochs | 5 |
+| Warmup ratio | 0.03 |
+| Weight decay | 0.0 |
+| Precision | FP16 |
+| Augmentation | RandomResizedCrop(0.8–1.0), RandomHorizontalFlip |
+**Trainable parameters:** ~93k of ~5.6M total (**~1.7%**).
 ## Evaluation
+Evaluated on the Food-101 test split (25,250 images).
+| Metric | Value |
+|---|---|
+| Top-1 accuracy | _fill in from `eval_metrics.json`_ |
+| Top-5 accuracy | _fill in from `eval_metrics.json`_ |
+## Usage
+The adapter uses the standard PEFT format plus a sidecar `classifier.pt` and `labels.json`. Minimal loader:
+```python
+import json
+import torch
+from huggingface_hub import hf_hub_download
+from peft import PeftModel
+from torch import nn
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+BASE = "WinKawaks/vit-tiny-patch16-224"
+ADAPTER = "turhancan97/vit-tiny-lora-food101"
+processor = AutoImageProcessor.from_pretrained(BASE, use_fast=True)
+base = AutoModelForImageClassification.from_pretrained(BASE)
+model = PeftModel.from_pretrained(base, ADAPTER)
+id2label = {int(k): v for k, v in json.loads(
+    open(hf_hub_download(ADAPTER, "labels.json")).read()
+).items()}
+head_state = torch.load(
+    hf_hub_download(ADAPTER, "classifier.pt"), map_location="cpu", weights_only=True
+)
+head = nn.Linear(base.config.hidden_size, len(id2label))
+head.load_state_dict(head_state)
+model.base_model.model.classifier = head
+model.eval()
+```
+**Inference:**
+```python
+from PIL import Image
+image = Image.open("my_food.jpg").convert("RGB")
+inputs = processor(images=image, return_tensors="pt")
+with torch.inference_mode():
+    logits = model(**inputs).logits[0]
+topk = logits.softmax(-1).topk(5)
+for score, idx in zip(topk.values, topk.indices):
+    print(f"{id2label[idx.item()]:30s} {score.item():.3f}")
+```
+**Switching back to the base model** (ImageNet-1k, 1000 classes) without unloading:
+```python
+with model.disable_adapter():
+    logits = base(**inputs).logits  # uses the pristine pretrained weights
+```
+## Intended use
+- Educational / demo use for showing how LoRA adds new capabilities to a frozen backbone.
+- Classifying photos of prepared food into the Food-101 taxonomy.
+## Limitations
+- Only 101 food categories; anything outside the taxonomy will be misclassified.
+- Trained on Food-101 which is mostly western/restaurant-style dishes, with label noise in the original data.
+- ViT-tiny is a low-capacity backbone; a larger base model would likely get higher accuracy with the same adapter recipe.
+## License
+Apache-2.0, matching the base model and the [Food-101 dataset license](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/).
+## Citation
+If you use this adapter, please cite the underlying works:
+```bibtex
+@inproceedings{hu2022lora,
+  title={{LoRA}: Low-Rank Adaptation of Large Language Models},
+  author={Hu, Edward J. and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu},
+  booktitle={ICLR},
+  year={2022}
+}
+@inproceedings{bossard2014food101,
+  title={Food-101 -- Mining Discriminative Components with Random Forests},
+  author={Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},
+  booktitle={ECCV},
+  year={2014}
+}
+```