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| Tag | Precision | Recall | Average Precision (AP) |
|---|---|---|---|
| 1982 Donruss | 100.0% | 100.0% | 100.0% |
| 1984 Topps | 100.0% | 100.0% | 100.0% |
| 1987 Fleer | 100.0% | 95.5% | 95.5% |
| 1987 Topps | 100.0% | 100.0% | 100.0% |
| 1988 Donruss | 91.3% | 100.0% | 100.0% |
| 1988 Fleer | 100.0% | 76.2% | 95.2% |
| 1988 Fleer Pack | 100.0% | 87.5% | 100.0% |
| 1988 Score | 100.0% | 100.0% | 100.0% |
| 1988 Topps | 96.0% | 100.0% | 100.0% |
| 1988 Topps Pack | 100.0% | 100.0% | 100.0% |
| 1989 Bowman | 100.0% | 94.7% | 100.0% |
| 1989 Donruss | 100.0% | 94.7% | 100.0% |
| 1989 Fleer | 100.0% | 100.0% | 100.0% |
| 1989 Score | 100.0% | 100.0% | 100.0% |
| 1989 Topps | 95.0% | 90.5% | 90.5% |
| 1989 Topps Pack | 100.0% | 83.3% | 91.0% |
| 1989 Upper Deck | 100.0% | 92.3% | 100.0% |
| 1990 Donruss | 100.0% | 100.0% | 100.0% |
| 1990 Fleer | 88.6% | 96.9% | 95.7% |
| 1990 Fleer Pack | 100.0% | 100.0% | 100.0% |
| 1990 Leaf | 100.0% | 100.0% | 100.0% |
| 1990 Leaf Pack | 100.0% | 80.0% | 100.0% |
| 1990 Topps | 100.0% | 100.0% | 100.0% |
| 1990 Upper Deck High Series Pack | 100.0% | 91.3% | 100.0% |
| 1991 Fleer Ultra | 100.0% | 97.5% | 100.0% |
| 1991 Leaf | 100.0% | 90.9% | 95.5% |
| 1991 Leaf Pack | 100.0% | 75.0% | 100.0% |
| 1991 Topps | 92.9% | 81.3% | 97.2% |
| 1991 Topps Pack | 100.0% | 91.7% | 100.0% |
| 1991 Upper Deck | 100.0% | 89.5% | 94.7% |
| 1991 Upper Deck Low Series Pack | 100.0% | 96.0% | 98.9% |
| 1992 Fleer | 100.0% | 100.0% | 100.0% |
| 1992 Fleer Ultra | 100.0% | 100.0% | 100.0% |
| 1992 Fleer Pack | 100.0% | 50.0% | 91.7% |
| 1992 O-Pee-Chee Premiere | 100.0% | 94.7% | 99.7% |
| 1992 Pinnacle | 100.0% | 94.4% | 99.7% |
| 1992 Pinnacle Pack | 100.0% | 85.7% | 100.0% |
| 1992 Upper Deck | 95.5% | 87.5% | 87.5% |
| 1992 Upper Deck High Series Pack | 100.0% | 100.0% | 100.0% |
| 1993 Fleer | 100.0% | 95.0% | 99.8% |
| 1993 Fleer Series 1 Pack | 100.0% | 100.0% | 100.0% |
| 1993 Fleer Series 2 Pack | 95.0% | 100.0% | 100.0% |
| 1993 Topps | 86.4% | 86.4% | 98.1% |
| 1994 Leaf | 95.7% | 100.0% | 100.0% |
| 1994 Pinnacle | 100.0% | 100.0% | 100.0% |
| 1994 Score | 100.0% | 100.0% | 100.0% |
| 1995 Leaf | 100.0% | 100.0% | 100.0% |
| 1995 Select | 95.5% | 100.0% | 100.0% |
| 1996 Pinnacle | 100.0% | 100.0% | 100.0% |
pip install onnxruntimemodel.onnx file from https://huggingface.co/enusbaum/JunkWaxHero/tree/main/onnx.1import onnxruntime as ort
2import numpy as np
3
4# Path to the downloaded ONNX model
5MODEL_PATH = 'path/to/junkwaxhero/onnx/junkwaxhero.onnx'
6
7# Initialize the ONNX runtime session
8session = ort.InferenceSession(MODEL_PATH)
9
10# Get input and output names
11input_name = session.get_inputs()[0].name
12output_name = session.get_outputs()[0].name
13
14# Example function to run inference
15def predict(image):
16 image = np.expand_dims(image, axis=0).astype(np.float32) # Add batch dimension and ensure correct type
17 predictions = session.run([output_name], {input_name: image})
18 return predictions1from PIL import Image
2
3# Load and preprocess the image
4def load_image(image_path):
5 image = Image.open(image_path)
6 image = image.resize((width, height)) # Resize to the input size expected by the model
7 image = np.array(image)
8 return image
9
10# Example usage
11image = load_image('path/to/baseball_card.jpg')
12predictions = predict(image)
13
14# Get the predicted label
15predicted_label = labels[np.argmax(predictions)]
16print(f'Predicted Set: {predicted_label}')Note: Replace(width, height)with the actual dimensions expected by your model.