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* added SPPF module from yolov8 * added conv_block, bottleneck modules * cleaned modules * c2f example * spf changes * C2f * fixed and tested bottleneck * improved detect class * tested spf and conv * checked c2f * DFL structure * fixed dfl * added dist2bbox function * added dist2bbox function * added and tested make_anchors function for the head * keeping functions above * creating the detection head * fixing head * untested blocks a. scale_boxes b. clip_boxes c. xywh2xyxy d. box_iou * head works * structure fixx * added darknet (backbone) * yolov8 neck, and intialize bias function while detection * fixed spacing * yolov8 class, init bias, and fixed c2f * forward pass almost working * fixed net structure * init bias not needed, forward pass working * load weights boilerplate * load weights done? * all variants loading! * post process: clip_boxes, scale_boxes, xywh2xyxy, and box_iou(untested) * fix scale_boxes * box_iou fixed and tested * created the pre nms function * fix nms * fixed load weights, apparently the latest commit broke something, excluding num_batches_tracked * added letterbox and pre_tranform for pre_process function * fixed letterbox, pre_transform and added preprocess function * custom NMS done, integrated prepare_boxes and nms, improved box_iou * added postprocess function till parsing * added draw_bounding_boxes_and_save function * testing full flow * using fetch for class names * fixed make_anchors + all tinygrad now * added command line arguments, weight downloading * single image for now only * made draw boxes more efficient * made NMS functions efficient * made compute_transform better * v8 working now, inference is done * prints objects detected in console now * fixed image loading (pre processing) * batch post processing * created initial tests * fixes bounding box thickness AND added get_detected_classes_with_frequency function * cleaning for testing * two tests * added url option for image, removed need for specifiying arguments * tests complete, but lots on things are printed on screen by ultralytics * remove parse arguments * fixed weight location * fixed colours of classes, and black font when high brightness * minor changes * TODOs for later * removed use of torch, using .npz weights * fixed tests * one path for fetch * preprocess now in tinygrad, plus test fix for that * updated tests * fix tests * no class labels needed * Add files via upload * Update showcase.md * Update showcase.md * added safe tensors as weights, and tests fix for that * safe tensors test * using safe_load * using tinygrad functions now to load weights * update tests --------- Co-authored-by: r3sist-uniq <amanmatreja@gmail.com> Co-authored-by: r3sist <72573738+r3sist-uniq@users.noreply.github.com>
60 lines
1.4 KiB
Markdown
60 lines
1.4 KiB
Markdown
# tinygrad Showcase
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Despite being a tiny library, tinygrad is capable of doing a lot of things. From state-of-the-art [vision](https://arxiv.org/abs/1905.11946) to state-of-the-art [language](https://arxiv.org/abs/1706.03762) models.
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## Vision
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### EfficientNet
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You can either pass in the URL of a picture to discover what it is:
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```sh
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python3 examples/efficientnet.py https://media.istockphoto.com/photos/hen-picture-id831791190
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```
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Or, if you have a camera and OpenCV installed, you can detect what is in front of you:
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```sh
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python3 examples/efficientnet.py webcam
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```
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### YOLOv8
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Take a look at [yolov8.py](/examples/yolov8.py).
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## Audio
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### Whisper
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Take a look at [whisper.py](/examples/whisper.py). You need pyaudio and torchaudio installed.
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```sh
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SMALL=1 python3 examples/whisper.py
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```
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## Generative
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### Generative Adversarial Networks
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Take a look at [mnist_gan.py](/examples/mnist_gan.py).
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### Stable Diffusion
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```sh
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python3 examples/stable_diffusion.py
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```
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*"a horse sized cat eating a bagel"*
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### LLaMA
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You will need to download and put the weights into the `weights/LLaMA` directory, which may need to be created.
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Then you can have a chat with Stacy:
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```sh
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python3 examples/llama.py
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```
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