Cabbage Disease Rexnet150

--- tags: - image-classification - timm - pytorch library_name: timm --- # Cabbage Disease Classifier (rexnet_150) A `rexnet_150` ([timm](https://github.com/huggingface/pytorch-image-models)) image classifier fine-tuned to detect cabbage plant diseases. - **Classes (8):** ['Alternaria_Leaf_Spot', 'club root', 'Downy Mildew', 'Cabbage aphid colony', 'ring spot', 'Black Rot', 'Bacterial spot rot', 'No disease'] - **Input size:** 224x224 - **Normalization:** mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] ## How to use See the "Inference: How to Use This Model" section of the training notebook, or the snippet below: ```python import json, torch, timm from huggingface_hub import hf_hub_download from PIL import Image from torchvision import transforms as T REPO_ID = "Huyt/cabbage-disease-rexnet150" config_path = hf_hub_download(repo_id=REPO_ID, filename="config.json") weights_path = hf_hub_download(repo_id=REPO_ID, filename="cabbage_best_model.pth") with open(config_path) as f: config = json.load(f) idx_to_class = {v: k for k, v in config["classes"].items()} device = "cuda" if torch.cuda.is_available() else "cpu" model = timm.create_model(config["model_name"], pretrained=False, num_classes=config["num_classes"]) model.load_state_dict(torch.load(weights_path, map_location=device)) model.to(device).eval() tfs = T.Compose([ T.Resize((config["image_size"], config["image_size"])), T.ToTensor(), T.Normalize(mean=config["mean"], std=config["std"]), ]) im = Image.open("path/to/image.jpg").convert("RGB") x = tfs(im).unsqueeze(0).to(device) with torch.no_grad(): probs = torch.softmax(model(x), dim=1)[0] pred_idx = int(torch.argmax(probs)) print(idx_to_class[pred_idx], probs[pred_idx].item()) ```

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