Update links and checkpoint path

README.md

@@ -2,6 +2,8 @@
 
 CropVLM is a CLIP-based zero-shot image classifier adapted for crop and fruit recognition. It compares one image embedding against text embeddings for candidate class names, then returns the class with the highest cosine similarity.
 
+**Links:** [GitHub repository](https://github.com/boudiafA/CropVLM)
+
 ![CropVLM framework overview](docs/figures/cropvlm_framework.png)
 
 This repository contains:
@@ -58,7 +60,7 @@ pip install -r requirements.txt
 This Hugging Face repository includes the CropVLM checkpoint:
 
 ```text
-models/CropCLIP_FullDataset_Acc_0.75.pth
+models/CropVLM.pth
 ```
 
 You can download it with `huggingface_hub`:
@@ -68,7 +70,7 @@ from huggingface_hub import hf_hub_download
 
 checkpoint = hf_hub_download(
     repo_id="boudiafA/CropVLM",
-    filename="models/CropCLIP_FullDataset_Acc_0.75.pth",
+    filename="models/CropVLM.pth",
 )
 ```
 
@@ -80,7 +82,7 @@ Run:
 
 ```bash
 python scripts/gradio_demo.py \
-  --checkpoint models/CropCLIP_FullDataset_Acc_0.75.pth
+  --checkpoint models/CropVLM.pth
 ```
 
 Then open:
@@ -107,7 +109,7 @@ The included examples are `cacao`, `olive`, `cauliflower`, `sugarcane`, and `sun
 from PIL import Image
 from cropvlm import load_cropvlm
 
-classifier = load_cropvlm("models/CropCLIP_FullDataset_Acc_0.75.pth")
+classifier = load_cropvlm("models/CropVLM.pth")
 image = Image.open("examples/cacao.png")
 
 for label, score in classifier.predict(image, top_k=5):
@@ -132,7 +134,7 @@ Run CropVLM and the supported comparison CLIP models:
 ```bash
 python scripts/evaluate_zero_shot.py \
   --dataset /mnt/e/Desktop/Datasets/FruitDataset/Crop_Dataset_testing \
-  --cropvlm-checkpoint models/CropCLIP_FullDataset_Acc_0.75.pth \
+  --cropvlm-checkpoint models/CropVLM.pth \
   --output outputs/zero_shot_results.json \
   --batch-size 64
 ```

scripts/evaluate_zero_shot.py

@@ -336,7 +336,7 @@ def main():
     parser = argparse.ArgumentParser()
     parser.add_argument("--dataset", required=True, help="ImageFolder-style dataset root.")
     parser.add_argument("--output", default="outputs/zero_shot_results.json")
-    parser.add_argument("--cropvlm-checkpoint", default="models/CropCLIP_FullDataset_Acc_0.75.pth")
+    parser.add_argument("--cropvlm-checkpoint", default="models/CropVLM.pth")
     parser.add_argument("--models", nargs="+", default=DEFAULT_MODELS)
     parser.add_argument("--device", default=None)
     parser.add_argument("--batch-size", type=int, default=64)

scripts/gradio_demo.py

@@ -99,7 +99,7 @@ def build_demo(checkpoint: str, device: str | None, prompt_template: str, top_k:
 
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--checkpoint", default="models/CropCLIP_FullDataset_Acc_0.75.pth")
+    parser.add_argument("--checkpoint", default="models/CropVLM.pth")
     parser.add_argument("--device", default=None)
     parser.add_argument("--prompt-template", default="{}")
     parser.add_argument("--top-k", type=int, default=5)