Siddanna
/

transparent-tube-classifier

@@ -1,199 +1,149 @@
 ---
 library_name: transformers
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 ---
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 ---
 library_name: transformers
+license: apache-2.0
+tags:
+  - image-classification
+  - dinov2
+  - vision
+  - tube-classification
+  - manufacturing
+datasets:
+  - Siddanna/transparent-tube-dataset
+base_model:
+  - facebook/dinov2-base
+pipeline_tag: image-classification
 ---
+# Transparent Tube Classifier
+A binary image classifier that distinguishes between:
+- **transparent_alone** 🧪 — A transparent tube by itself
+- **transparent_with_blue** 🧪💙 — A transparent tube paired with a blue tube
 ## Model Details
+| Property | Value |
+|---|---|
+| **Base Model** | [facebook/dinov2-base](https://huggingface.co/facebook/dinov2-base) (ViT-B/14, 86.6M params) |
+| **Training Method** | Linear probe (frozen backbone + trained classifier head) |
+| **Training Dataset** | [Siddanna/transparent-tube-dataset](https://huggingface.co/datasets/Siddanna/transparent-tube-dataset) |
+| **Accuracy** | **100%** on test set |
+| **Loss** | 0.0014 |
+| **Image Size** | 256×256 (DINOv2 default) |
+| **License** | Apache 2.0 |
+## Quick Start
+### Using Pipeline (Easiest)
+```python
+from transformers import pipeline
+classifier = pipeline("image-classification", model="Siddanna/transparent-tube-classifier")
+result = classifier("your_tube_image.jpg")
+print(result)
+# [{'label': 'transparent_with_blue', 'score': 0.99}, {'label': 'transparent_alone', 'score': 0.01}]
+```
+### Manual Inference
+```python
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+# Load model and processor
+model = AutoModelForImageClassification.from_pretrained("Siddanna/transparent-tube-classifier")
+processor = AutoImageProcessor.from_pretrained("Siddanna/transparent-tube-classifier")
+# Load and classify image
+image = Image.open("your_tube_image.jpg")
+inputs = processor(image, return_tensors="pt")
+with torch.no_grad():
+    logits = model(**inputs).logits
+predicted_class = logits.argmax(-1).item()
+label = model.config.id2label[predicted_class]
+confidence = torch.softmax(logits, dim=-1)[0][predicted_class].item()
+print(f"Prediction: {label} (confidence: {confidence:.2%})")
+```
 ## Training Details
+### Architecture
+- **Base**: DINOv2-base (Vision Transformer B/14), pretrained on LVD-142M (142M curated images)
+- **Head**: Linear classifier (768 → 2)
+- **Method**: Linear probe — backbone is frozen, only the classification head is trained
+- **Why DINOv2?**: DINOv2's global self-attention captures the full image context, which is critical for detecting whether a blue tube is present anywhere in the scene alongside the transparent tube
+### Hyperparameters
+- Learning rate: `1e-3` (with cosine schedule)
+- Warmup steps: 50
+- Batch size: 16
+- Weight decay: 0.01
+- Training epochs: 4 (converged at epoch 1)
+### Data Augmentations
+- RandomResizedCrop (scale 0.7-1.0)
+- RandomHorizontalFlip
+- RandomRotation (±15°)
+- ColorJitter (brightness=0.3, contrast=0.3, saturation=0.2, hue=0.05)
+### Training Curves
+| Epoch | Train Loss | Eval Loss | Eval Accuracy |
+|---|---|---|---|
+| 1 | 0.032 | 0.019 | **100%** |
+| 2 | 0.011 | 0.002 | **100%** |
+| 3 | 0.002 | 0.001 | **100%** |
+| 4 | 0.004 | 0.010 | 99.5% |
+## For Production Use with Real Images
+The model is currently trained on **synthetic data**. For best results with your actual tubes:
+### Step 1: Collect Real Photos
+Take 50-100+ photos per class of your actual tubes:
+```
+data/
+├── train/
+│   ├── transparent_alone/     # Photos of transparent tube alone
+│   └── transparent_with_blue/ # Photos of transparent + blue tube
+└── test/
+    ├── transparent_alone/
+    └── transparent_with_blue/
+```
+### Step 2: Re-train
+```python
+# Clone the training script
+# Option A: Linear probe (fast, good with 50+ images/class)
+python train.py --data_dir ./data --freeze_backbone --hub_model_id your-username/tube-classifier
+# Option B: Full fine-tune (better with 200+ images/class)
+python train.py --data_dir ./data --learning_rate 5e-5 --hub_model_id your-username/tube-classifier
+```
+### Tips for Collecting Good Training Data
+- **Vary backgrounds**: different surfaces, lighting conditions
+- **Vary angles**: slightly different camera positions
+- **Vary distances**: close-up and farther away shots
+- **Include edge cases**: partially occluded tubes, different orientations
+- **Match deployment conditions**: use the same camera/environment you'll deploy in
+## Demo
+Try the model: [**Transparent Tube Classifier Demo**](https://huggingface.co/spaces/Siddanna/transparent-tube-classifier-demo)
+## Citation
+```bibtex
+@misc{transparent-tube-classifier,
+  title={Transparent Tube Classifier},
+  author={Siddanna},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/Siddanna/transparent-tube-classifier}
+}
+```