Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- object-detection
+- vision
+---
+
+# Yolos-small-crowd
+
+YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](https://github.com/hustvl/YOLOS). 
+
+## Model description
+This model has been finetuned on the following [crowd-detection dataset](https://universe.roboflow.com/institut-teknologi-nasional-bandung-mxgtc/crowd-detection-i75bl) with the following results on the test set:
+
+```
+IoU metric: bbox
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.630
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.908
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.672
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.005
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.636
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.431
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.740
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.762
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.300
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.766
+```
+
+## How to use
+
+```python
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
+import torch
+from PIL import Image
+import requests
+
+url = "https://latestbollyholly.com/wp-content/uploads/2024/02/Jacob-Gooch.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+image_processor = AutoImageProcessor.from_pretrained("AdamCodd/yolos-small-crowd")
+model = AutoModelForObjectDetection.from_pretrained("AdamCodd/yolos-small-crowd")
+inputs = image_processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+
+# convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
+target_sizes = torch.tensor([image.size[::-1]])
+results = image_processor.post_process_object_detection(outputs, threshold=0.7, target_sizes=target_sizes)[0]
+for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+    box = [round(i, 2) for i in box.tolist()]
+    print(
+        f"Detected {model.config.id2label[label.item()]} with confidence "
+        f"{round(score.item(), 3)} at location {box}"
+    )
+```
+
+Refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/yolos) for more code examples.
+
+## Intended uses & limitations
+
+This fine-tuned model performs best when detecting individuals who are relatively close to the viewpoint. As indicated by the metrics, it struggles to identify individuals farther away.
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
+- num_epochs: 5
+- weight_decay: 1e-4
+
+### Framework versions
+
+- Transformers 4.36.2
+- pycocotools 2.0.7
+
+If you want to support me, you can [here](https://ko-fi.com/adamcodd).