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library_name: transformers |
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license: apache-2.0 |
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tags: |
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- image-classification |
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- vision-transformer |
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- vit |
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- celeba |
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- research |
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- fine-tuning |
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--- |
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# FaceGuard – ViT (20 CelebA IDs) |
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A Vision Transformer (ViT-Base) fine-tuned for identity classification on a **20-identity subset of the CelebA dataset**. |
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This model predicts **anonymized `celeb_id` integers** (not celebrity names). |
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It powers the demo Space: https://huggingface.co/spaces/hudaakram/FaceGuard-demo |
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--- |
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## Model Details |
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### Model Description |
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- **Architecture:** `google/vit-base-patch16-224` (pretrained on ImageNet-1k) |
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- **Fine-tuned for:** 20-class identity classification (CelebA `celeb_id`s) |
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- **Input:** RGB image (face crop), resized and normalized to 224×224 |
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- **Output:** Probability distribution over 20 anonymized IDs |
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- **Parameters:** ~86M |
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### Sources |
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- **Base model:** https://huggingface.co/google/vit-base-patch16-224 |
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- **Demo Space:** https://huggingface.co/spaces/hudaakram/FaceGuard-demo |
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- **Dataset:** CelebA (community mirror on the Hub) |
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--- |
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## Uses |
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### Direct Use |
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- Research demo for identity classification with anonymized CelebA IDs |
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- Educational example of fine-tuning ViT for image classification |
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### Downstream Use |
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- As a starting point for transfer learning to other **small identity classification tasks** |
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- As an educational reference for hackathons, workshops, or courses |
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### Out-of-Scope Use |
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- ❌ Production face recognition / surveillance |
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- ❌ Identifying real celebrity names (dataset only provides integer IDs) |
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- ❌ Any high-stakes application involving privacy or personal data |
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--- |
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## Bias, Risks, and Limitations |
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- **Bias:** CelebA contains celebrity faces, which are not representative of all demographics. |
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- **Limitations:** Trained on **only 20 identities (~600 images total)** → limited generalization. |
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- **Privacy:** CelebA IDs are anonymized integers, not real names. The model is not capable of returning actual identities. |
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**Recommendation:** Use strictly for **research/educational purposes**. |
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--- |
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## How to Get Started |
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Use the code below to get started with the model. |
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```python |
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from transformers import ViTForImageClassification, AutoImageProcessor |
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from PIL import Image |
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import torch |
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model_id = "hudaakram/FaceGuard-20ID-ViT" |
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processor = AutoImageProcessor.from_pretrained(model_id) |
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model = ViTForImageClassification.from_pretrained(model_id) |
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img = Image.open("face.jpg").convert("RGB") |
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inputs = processor(images=img, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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probs = torch.softmax(logits, dim=-1)[0] |
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id2label = {int(k): v for k, v in model.config.id2label.items()} |
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top5 = probs.topk(5) |
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for score, idx in zip(top5.values, top5.indices): |
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print(f"Label {idx.item()} (celeb_id {id2label[idx.item()]}): {score:.3f}") |
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``` |
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## Training Details |
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### Training Data |
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- **Dataset:** CelebA (top 20 identities by frequency) |
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- **Splits:** Stratified 80% train / 10% validation / 10% test |
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- **Sizes:** Train 501, Val 60, Test 77 |
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### Training Procedure |
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- **Seed:** 42 |
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- **Epochs:** 4 |
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- **Batch size:** 16 |
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- **Learning rate:** 5e-5 |
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- **Optimizer:** AdamW |
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- **Weight decay:** 0.01 |
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- **Precision:** FP16 on GPU (Colab) |
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- **Head resized:** from 1000 classes → 20 classes |
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### Preprocessing |
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- Images resized + center-cropped to 224×224 |
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- Normalized to ImageNet mean/std |
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- Labels mapped from CelebA `celeb_id` → contiguous 0–19 |
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### Training Hyperparameters |
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- **Training regime:** fp16 mixed precision on GPU |
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- **Total epochs:** 4 (~3 minutes each on Colab T4) |
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### Speeds, Sizes, Times |
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- **Checkpoint size:** ~343 MB |
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- **Throughput:** ~10 samples/sec (Colab T4) |
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--- |
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## Evaluation |
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- Validation Accuracy: ~0.93 |
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- Test Accuracy: ~0.83 |
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- Macro AUC: (see ROC below) |
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### Test Split Summary |
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| Split | #Images | #Classes | Min/Class | Median/Class | Max/Class | |
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|-------|---------|----------|-----------|--------------|-----------| |
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| Train | 501 | 20 | 24 | 24 | 28 | |
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| Val | 60 | 20 | 3 | 3 | 3 | |
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| Test | 77 | 20 | 3 | 4 | 4 | |
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### Results |
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**Confusion Matrix (normalized):** |
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**ROC Curves (one-vs-rest):** |
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--- |
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## Environmental Impact |
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- **Hardware:** Google Colab T4 GPU |
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- **Training time:** ~12 minutes total (4 epochs) |
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- **Carbon emissions:** negligible (short fine-tuning run) |
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--- |
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## Technical Specifications |
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### Model Architecture and Objective |
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- Vision Transformer (ViT-Base, patch16, 224×224) |
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- Objective: Cross-entropy classification across 20 labels |
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### Compute Infrastructure |
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- **Hardware:** Google Colab T4 GPU |
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- **Framework:** PyTorch + Hugging Face Transformers |
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--- |
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## Citation |
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**CelebA Dataset:** |
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Z. Liu, P. Luo, X. Wang, and X. Tang. *Deep Learning Face Attributes in the Wild.* ICCV 2015. |
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**ViT:** |
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A. Dosovitskiy et al. *An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.* ICLR 2021. |
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--- |
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## Model Card Authors |
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Hackathon submission by **Huda Akram** |
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## Contact |
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- Hugging Face profile: https://huggingface.co/hudaakram |
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