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README.md

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+# Model Card: Gym Machines Image Classifier
+
+## Dataset Description
+
+The model was trained and evaluated on the `ysakhale/gym-machines-image-dataset`.
+This dataset contains images of various gym machines, split into 'original' and 'augmented' subsets.
+The augmented subset was used for training and tuning.
+
+
+## Intended Use
+
+This model is intended for image classification of gym machines.
+It can be used to identify different types of gym equipment from images.
+
+
+## Limitations
+
+The model's performance may be limited on images that differ significantly from the training data (e.g., different lighting conditions, angles, or machine types not present in the dataset).
+Potential biases in the dataset might affect the model's ability to generalize to certain real-world scenarios.
+
+
+## Training Hyperparameters
+
+The following hyperparameters were used for training the AutoGluon MultiModalPredictor:
+- presets: "medium_quality"
+- model.names: ["timm_image"]
+- model.timm_image.checkpoint_name: "resnet18"
+
+
+## Evaluation Results
+
+The model was evaluated on the original test set (`df_orig`).
+- Accuracy: 62.50%
+- Weighted F1: 0.5288
+
+
+## Example Usage
+### Loading from pickled predictor
+
+```python
+import cloudpickle
+from huggingface_hub import hf_hub_download
+from autogluon.multimodal import MultiModalPredictor
+import pandas as pd
+
+model_repo_id = "mohitk24/image_autogluon_predictor"
+pkl_filename = "autogluon_image_predictor.pkl"
+
+# Download the pickled predictor
+pkl_local_path = hf_hub_download(
+    repo_id=model_repo_id,
+    repo_type="model",
+    filename=pkl_filename,
+    local_dir="./downloaded_model",
+    local_dir_use_symlinks=False # Set to True if you prefer symlinks
+)
+
+# Load the predictor from the pickle file
+with open(pkl_local_path, "rb") as f:
+    predictor_from_hub = cloudpickle.load(f)
+
+# Example prediction (replace with your image path or DataFrame)
+# Assuming you have a DataFrame 'test_df' with an 'image' column
+# results_quick = predictor_from_hub.predict(test_df[["image"]])
+# print(results_quick)
+```
+
+### Loading from native AutoGluon directory
+
+```python
+import zipfile
+import shutil
+from huggingface_hub import hf_hub_download
+from autogluon.multimodal import MultiModalPredictor
+import pandas as pd
+import pathlib
+
+model_repo_id = "mohitk24/image_autogluon_predictor"
+zip_filename = "autogluon_image_predictor_dir.zip"
+download_dir = pathlib.Path("./downloaded_model_native")
+
+# Download the zipped predictor directory
+zip_local_path = hf_hub_download(
+    repo_id=model_repo_id,
+    repo_type="model",
+    filename=zip_filename,
+    local_dir=str(download_dir),
+    local_dir_use_symlinks=False # Set to True if you prefer symlinks
+)
+
+# Extract the predictor directory
+native_dir = download_dir / "predictor_dir"
+if native_dir.exists():
+    shutil.rmtree(native_dir)
+native_dir.mkdir(parents=True, exist_ok=True)
+
+with zipfile.ZipFile(zip_local_path, "r") as zf:
+    zf.extractall(str(native_dir))
+
+# Load the predictor from the native directory
+predictor_native = MultiModalPredictor.load(str(native_dir))
+
+# Example prediction (replace with your image path or DataFrame)
+# Assuming you have a DataFrame 'test_df' with an 'image' column
+# y_pred_native = predictor_native.predict(test_df[["image"]])
+# print(y_pred_native)
+```
+
+
+## Known Failure Modes
+
+- **Lower performance on original data:** The model was trained on an augmented dataset, which may lead to lower performance on the original, unaugmented images. The evaluation results on the original test set (`df_orig`) reflect this potential drop in performance.
+- **Underrepresented classes:** If certain types of gym machines are not well-represented in the training data, the model may struggle to classify them accurately.
+