Oxford 17 Flowers Cnn

--- license: apache-2.0 tags: - image-classification - pytorch - resnet datasets: - darjja/oxford-17-flowers --- # Oxford 17 Flowers — ResNet18 (97.8% test accuracy) ResNet18 fine-tuned on the [Oxford 17 Flowers](https://huggingface.co/datasets/darjja/oxford-17-flowers) dataset. Classifies RGB images into 17 flower categories. Successor of the from-scratch educational CNN at [darjja/oxford-17-flowers-cnn](https://huggingface.co/darjja/oxford-17-flowers-cnn) — same CLI, much higher accuracy: | Model | Test accuracy | |---|---| | From-scratch CNN, 64×64 (original) | 62.13% | | From-scratch CNN + augmentation & label smoothing | 66.91% | | **ResNet18 fine-tuned, 224×224 (this repo)** | **97.79%** | ## Quick start ```bash pip install torch torchvision huggingface_hub pillow ``` Download the script and run it on any image: ```bash huggingface-cli download sergiudanstan/oxford-17-flowers-cnn predict.py --local-dir . python predict.py your_flower.jpg python predict.py your_flower.jpg --top 5 ``` The script downloads `flower_cnn.pth`, `config.json`, and `model.py` from this repo automatically on first run. ## Training recipe - ImageNet-pretrained ResNet18 (torchvision `IMAGENET1K_V1`), full fine-tune at 224×224 - Differential learning rates: backbone 1e-4, new head 1e-3 (AdamW, weight decay 1e-4) - CosineAnnealingLR, label smoothing 0.1, early stopping on validation accuracy - Augmentation: RandomResizedCrop(0.7–1.0), horizontal flip, rotation ±15°, ColorJitter 0.2 - Split: 924 train / 164 val (15%, seed 42) / 272 official test - ~3 minutes on a laptop RTX 5050 ## Files in this repo | File | Purpose | |------|---------| | `predict.py` | CLI script — pass any image path as argument | | `model.py` | Model definition (ResNet18 wrapper, `FlowerCNN`) | | `flower_cnn.pth` | Fine-tuned weights (~45 MB) | | `config.json` | Image size, class names | ## Classes Bluebell, Buttercup, Coltsfoot, Cowslip, Crocus, Daffodil, Daisy, Dandelion, Fritillary, Iris, Lilyvalley, Pansy, Snowdrop, Sunflower, Tigerlily, Tulip, Windflower

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