| { | |
| "model_name": "facial_emotion_simplecnn", | |
| "model_type": "simple_cnn", | |
| "framework": "pytorch", | |
| "task": "image-classification", | |
| "num_classes": 7, | |
| "labels": { | |
| "0": "angry", | |
| "1": "disgust", | |
| "2": "fear", | |
| "3": "happy", | |
| "4": "sad", | |
| "5": "surprise", | |
| "6": "neutral" | |
| }, | |
| "in_channels": 1, | |
| "input_size": [48, 48], | |
| "preprocessing": { | |
| "resize": [48, 48], | |
| "normalize_mean": [0.5], | |
| "normalize_std": [0.5], | |
| "color_mode": "grayscale" | |
| }, | |
| "training_dataset": "FER2013 (or similar facial emotion dataset)", | |
| "author": "sreenathsree1578", | |
| "license": "mit", | |
| "example_usage": "from facial_emotion import SimpleCNN\nfrom huggingface_hub import hf_hub_download\nimport torch\n\n# Load config\nimport json\nconfig = json.load(open('config.json'))\n\n# Build model\nmodel = SimpleCNN(num_classes=config['num_classes'], in_channels=config['in_channels'])\n\n# Load weights from hub\ncheckpoint = hf_hub_download(repo_id='sreenathsree1578/facial_emotion', filename='pytorch_model.bin')\nmodel.load_state_dict(torch.load(checkpoint, map_location='cpu'))\nmodel.eval()\n\n# Example inference\ntensor = torch.randn(1, 1, 48, 48) # dummy grayscale image\nwith torch.no_grad():\n output = model(tensor)\n pred = torch.argmax(output, dim=1).item()\n print('Predicted class:', config['labels'][str(pred)])" | |
| } | |