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README.md

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 ---
-license: mit
+library_name: transformers.js
+license: gpl-3.0
+pipeline_tag: object-detection
 ---
+
+https://github.com/WongKinYiu/yolov9 with ONNX weights to be compatible with Transformers.js.
+
+
+## Usage (Transformers.js)
+
+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:
+```bash
+npm i @xenova/transformers
+```
+
+**Example:** Perform object-detection with `Xenova/gelan-c_all`.
+
+```js
+import { AutoModel, AutoProcessor, RawImage } from '@xenova/transformers';
+
+// Load model
+const model = await AutoModel.from_pretrained('Xenova/gelan-c_all', {
+    // quantized: false,    // (Optional) Use unquantized version.
+})
+
+// Load processor
+const processor = await AutoProcessor.from_pretrained('Xenova/gelan-c_all');
+// processor.feature_extractor.size = { shortest_edge: 128 }    // (Optional) Update resize value
+
+// Read image and run processor
+const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg';
+const image = await RawImage.read(url);
+const inputs = await processor(image);
+
+// Run object detection
+const threshold = 0.3;
+const { outputs } = await model(inputs);
+const predictions = outputs.tolist();
+
+for (const [xmin, ymin, xmax, ymax, score, id] of predictions) {
+    if (score < threshold) break;
+    const bbox = [xmin, ymin, xmax, ymax].map(x => x.toFixed(2)).join(', ')
+    console.log(`Found "${model.config.id2label[id]}" at [${bbox}] with score ${score.toFixed(2)}.`)
+}
+// Found "car" at [63.06, 118.80, 139.61, 146.78] with score 0.84.
+// Found "bicycle" at [158.32, 166.13, 195.02, 189.03] with score 0.81.
+// Found "bicycle" at [123.22, 183.83, 162.71, 206.30] with score 0.79.
+// Found "bicycle" at [0.56, 180.92, 39.26, 203.94] with score 0.78.
+// Found "car" at [157.10, 132.38, 223.72, 167.69] with score 0.77.
+// Found "person" at [193.04, 90.98, 207.09, 116.78] with score 0.77.
+// Found "person" at [12.49, 164.97, 27.63, 197.55] with score 0.66.
+// Found "traffic light" at [102.80, 74.25, 124.12, 95.75] with score 0.62.
+// ...
+```
+
+## Demo
+
+Test it out [here](https://huggingface.co/spaces/Xenova/video-object-detection)!
+
+<video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/AgNFx_3cPMh5zjR91n9Dt.mp4"></video>
+
+---
+
+
+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`).

config.json

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+{
+  "id2label": {
+    "0": "paper",
+    "1": "glass",
+    "2": "cardboard",
+    "3": "trash",
+    "4": "metal",
+    "5": "plastic"
+  },
+  "label2id": {
+    "paper": 0,
+    "glass": 1,
+    "cardboard": 2,
+    "trash": 3,
+    "metal": 4,
+    "plastic": 5
+  },
+  "model_type": "yolov5"
+}

onnx/model.onnx

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onnx/model_fp16.onnx

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onnx/model_quantized.onnx

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preprocessor_config.json

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+{
+  "do_normalize": false,
+  "do_pad": false,
+  "do_rescale": true,
+  "do_resize": true,
+  "feature_extractor_type": "ImageFeatureExtractor",
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  },
+  "size_divisibility": 32
+}