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--- |
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library_name: transformers.js |
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license: gpl-3.0 |
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pipeline_tag: object-detection |
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--- |
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https://github.com/WongKinYiu/yolov9 with ONNX weights to be compatible with Transformers.js. |
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## Usage (Transformers.js) |
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If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using: |
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```bash |
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npm i @xenova/transformers |
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``` |
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**Example:** Perform object-detection with `Xenova/yolov9-c_all`. |
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```js |
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import { AutoModel, AutoProcessor, RawImage } from '@xenova/transformers'; |
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// Load model |
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const model = await AutoModel.from_pretrained('Xenova/yolov9-c_all', { |
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// quantized: false, // (Optional) Use unquantized version. |
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}) |
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// Load processor |
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const processor = await AutoProcessor.from_pretrained('Xenova/yolov9-c_all'); |
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// processor.feature_extractor.size = { shortest_edge: 128 } // (Optional) Update resize value |
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// Read image and run processor |
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const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg'; |
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const image = await RawImage.read(url); |
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const inputs = await processor(image); |
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// Run object detection |
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const threshold = 0.3; |
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const { outputs } = await model(inputs); |
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const predictions = outputs.tolist(); |
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for (const [xmin, ymin, xmax, ymax, score, id] of predictions) { |
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if (score < threshold) break; |
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const bbox = [xmin, ymin, xmax, ymax].map(x => x.toFixed(2)).join(', ') |
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console.log(`Found "${model.config.id2label[id]}" at [${bbox}] with score ${score.toFixed(2)}.`) |
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} |
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// Found "car" at [156.96, 133.09, 223.66, 167.14] with score 0.91. |
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// Found "car" at [63.22, 119.01, 139.68, 145.72] with score 0.88. |
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// Found "bicycle" at [0.80, 182.37, 39.26, 203.85] with score 0.85. |
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// Found "bicycle" at [124.02, 184.35, 163.21, 206.10] with score 0.85. |
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// Found "bicycle" at [158.31, 169.96, 194.58, 189.22] with score 0.80. |
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// Found "person" at [135.04, 166.16, 156.00, 204.01] with score 0.75. |
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// Found "person" at [192.14, 90.51, 205.68, 116.73] with score 0.74. |
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// Found "person" at [11.69, 164.45, 28.37, 200.11] with score 0.74. |
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// ... |
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``` |
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## Demo |
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Test it out [here](https://huggingface.co/spaces/Xenova/video-object-detection)! |
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<video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/AgNFx_3cPMh5zjR91n9Dt.mp4"></video> |
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--- |
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Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`). |
