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CalorieCLIP

CalorieCLIP is a fine-tuned CLIP model that estimates calories from food images with state-of-the-art accuracy. It outperforms all tested VLMs (including GPT-4o and Claude) while running entirely on-device.

TipoOtherVersãoRevision 26b9bd6Bnota

Fonte do modelo

Tipo
Other
Tamanho do arquivo
579 MB
Versão
vRevision 26b9bd6
Atualizado
18 de set. de 2026
Downloads
26
Fonte
Hugging Face

Trecho da fonte

CalorieCLIP is a fine-tuned CLIP model that estimates calories from food images with state-of-the-art accuracy. It outperforms all tested VLMs (including GPT-4o and Claude) while running entirely on-device.

Fontes

1 fonte
Hugging FacePrimárioVersão 26b9bd644f65323fcddb68a86a2c0c5505c4bc81
huggingface.co/jc-builds/CalorieCLIP/tree/26b9bd644f65323fcddb68a86a2c0c5505c4bc81

Verificado 3 de set.

Artefatos de modelo

1 artefato

calorie_clip.pt

pt · 579 MB · SHA-256 ee05375462af…0e28 · Hugging Face

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Trechos de fonte

2 trechos

--- license: mit language: - en tags: - vision - food - nutrition - calorie-estimation - clip - image-classification - health datasets: - nutrition5k metrics: - mae pipeline_tag: image-to-text library_name: open-clip --- # 🍎 CalorieCLIP: Accurate Food Calorie Estimation <p align="center"> <img src="assets/model_comparison.png" width="700" alt="CalorieCLIP vs Other Models"> </p> **CalorieCLIP** is a fine-tuned CLIP model that estimates calories from food images with state-of-the-art accuracy. It outperforms all tested VLMs (including GPT-4o and Claude) while running entirely on-device. ## 🎯 Key Results | Metric | Value | |--------|-------| | **Mean Absolute Error** | **51.4 calories** | | Within 50 calories | 67.6% | | Within 100 calories | 90.5% | | Inference Speed | <50ms on M1 Mac | <p align="center"> <img src="assets/accuracy_breakdown.png" width="500" alt="Accuracy Breakdown"> </p> ## 🍽️ Example Predictions Real predictions from our validation set across multiple datasets: | Image | Food | Dataset | Actual | Predicted | Error | |-------|------|---------|--------|-----------|-------| | ![Example 1](assets/examples/example_1.png) | Hamburger | Food-101 | 558 | 555 | 3 | | ![Example 2](assets/examples/example_2.png) | Ramen | Food-101 | 431 | 437 | 6 | | ![Example 3](assets/examples/example_3.png) | Greek Salad | Food-101 | 144 | 143 | 1 | | ![Example 4](assets/examples/example_4.png) | Sashimi | Food-101 | 156 | 156 | 0 | | ![Example 5](assets/examples/example_5.png) | Cafeteria Meal | Nutrition5k | 88 | 88 | 0 | | ![Example 6](assets/examples/example_6.png) | Cafeteria Meal | Nutrition5k | 138 | 138 | 0 | | ![Example 7](assets/examples/example_7.png) | Cafeteria Meal | Nutrition5k | 330 | 334 | 3 | | ![Example 8](assets/examples/example_8.png) | Cafeteria Meal | Nutrition5k | 214 | 217 | 4 | ## 🚀 Quick Start ### Installation ```bash pip install open-clip-torch torch pillow ``` ### Python Usage ```python # Clone or download this repo first, then: from calorie_clip import CalorieCLIP # Load model from local directory model = CalorieCLIP.from_pretrained(".") # Predict calories calories = model.predict("food_photo.jpg") print(f"Estimated: {calories:.0f} calories") # Batch prediction images = ["breakfast.jpg", "lunch.jpg", "dinner.jpg"] results = model.predict_batch(images) ``` ### Direct Usage (no wrapper) ```python import torch im...

Source context: 26 downloads · 19 likes · Pipeline image-to-text · Library open-clip · Repo jc-builds/CalorieCLIP