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What this Colab tutorial does
The workflow takes an image, passes it to the original Darknet YOLO v1 model, and writes an annotated result:
input image → YOLO v1 inference → predictions.jpg
- You need a Google account and a Colab notebook. A GPU can help, but Google does not guarantee that one will be available.
- The full historical weight file is approximately 1.0 GB, so allow time and storage for the download.
- The commands below target the legacy
pjreddie/darknetrepository and itscfg/yolov1.cfgconfiguration. These are not commands for YOLOv5, YOLOv8, YOLO11, or another newer model.
Colab’s free resources, available hardware, idle limits, and session lifetime vary. Google says free notebooks can run for up to 12 hours depending on availability and usage patterns, but it does not guarantee a fixed GPU type or quota. See the Colab FAQ.
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What YOLO v1 is—and what it predicts
YOLO means “You Only Look Once.” The original method treats detection as a single regression problem: one convolutional network evaluates the complete image and predicts boxes and class-related values, rather than first proposing regions and then classifying each region. The original paper describes the PASCAL VOC setup as a 7 × 7 grid with two box predictions per cell and a 7 × 7 × 30 output tensor. That formulation yields 98 predicted boxes per image.
In that VOC formulation, a cell predicts one class assignment along with box and confidence values. A post-processing step, non-maximum suppression, helps remove overlapping duplicate predictions. The single-pass design was historically important for speed, but the grid and one-class-per-cell constraint make nearby objects, small objects, and crowded scenes difficult. The pretrained weights recognize the classes represented by their training setup; they do not automatically learn arbitrary categories in an uploaded image.
Choose a GPU runtime and verify it
-
In Colab, select Runtime → Change runtime type → Hardware accelerator → GPU, then save the setting. The exact GPU varies with account, location, and availability.
-
Check whether a GPU was assigned and whether CUDA is visible to PyTorch:
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In a separate cell, inspect NVIDIA driver visibility:
!nvidia-smi
A GPU selection is not proof that a program is using the GPU. If no GPU appears, you can still try CPU inference, but full YOLO v1 may be slow. Google also warns that hardware availability changes over time.
Clone and compile the legacy Darknet implementation
Run this historical build sequence in a Colab cell:
!git clone https://github.com/pjreddie/darknet.git
%cd /content/darknet
!sed -i 's/GPU=0/GPU=1/' Makefile
!sed -i 's/CUDNN=0/CUDNN=1/' Makefile
!sed -i 's/OPENCV=0/OPENCV=1/' Makefile
!makeThis Makefile-edit approach is a legacy pattern, not a guarantee that the repository will compile against the CUDA toolkit, compiler, and OpenCV packages in a future Colab image. The original project documents GPU and OpenCV compilation in its Darknet YOLO instructions; its repository is old, and later build guidance may differ. Inspect the Makefile and the full build output rather than assuming the substitutions worked. A current Darknet fork may use a different source layout and build process.
Confirm that compilation produced an executable:
!ls -lh /content/darknet/darknetIf the file is absent, the build did not produce a runnable binary. For CUDA errors, check !nvidia-smi and the runtime type; if the old code is incompatible, try a CPU build by setting GPU=0 and rebuilding, or use a maintained fork if Darknet compatibility is essential. Disable an unavailable optional feature such as OpenCV or cuDNN only when the build error identifies it as the problem.
Download and verify the YOLO v1 weights
From the Darknet directory, download the historically canonical full weights:
%cd /content/darknet
!wget https://pjreddie.com/media/files/yolov1.weights
!ls -lh /content/darknet/yolov1.weightsThe Darknet documentation describes the full file as approximately 1.0 GB. Verify that the file is present and has a plausible size before inference. If it is zero bytes, unexpectedly small, or an HTML error page, the historical host may have failed; that does not by itself indicate a Colab problem. Do not substitute an unverified mirror: weights must match the configuration, and a third-party file may have unknown provenance.
Run detection on a sample image
The legacy repository’s Darknet command pattern is ./darknet yolo test. With its bundled dog image, run:
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%cd /content/darknet
!./darknet yolo test cfg/yolov1.cfg /content/darknet/yolov1.weights data/dog.jpgA successful run loads the configuration and weights, reads the input image, prints detected classes and scores, and normally writes an annotated image to /content/darknet/predictions.jpg. Darknet’s command syntax and threshold option are documented in the original YOLO instructions.
Display the annotated image
The simplest option is IPython’s image display:
from IPython.display import display, Image
display(Image(filename="/content/darknet/predictions.jpg"))Or render it with OpenCV and Matplotlib:
import cv2
import matplotlib.pyplot as plt
image = cv2.imread("/content/darknet/predictions.jpg")
if image is None:
raise FileNotFoundError("Darknet did not create predictions.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(12, 8))
plt.imshow(image)
plt.axis("off")
plt.show()If you need the result after the Colab session ends, download it from the file pane or copy it to persistent storage. Files under /content are temporary and may disappear when the runtime resets.
Run detection on an uploaded image
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Upload one or more images:
from google.colab import files uploaded = files.upload() -
Check the uploaded filename. Colab places uploaded files in
/content:import os print(os.listdir("/content")) -
Pass the exact filename to Darknet. Replace
my_image.jpgwith the name shown above:Special offer. See more information about Outbyte and uninstall instructions. Please review EULA and Privacy policy.%cd /content/darknet !./darknet yolo test cfg/yolov1.cfg /content/darknet/yolov1.weights /content/my_image.jpg -
Display the newly generated
/content/darknet/predictions.jpgusing either display method in the preceding section.
For filenames containing spaces or shell metacharacters, avoid inserting the name directly into a shell command. Rename the upload to a simple filename in Python first, then pass that path to Darknet.
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Adjust the confidence threshold
The historical default threshold is reported as 0.2. To show more, potentially weaker detections, try a lower threshold such as 0.10:
%cd /content/darknet
!./darknet yolo test cfg/yolov1.cfg /content/darknet/yolov1.weights data/dog.jpg -thresh 0.10A lower threshold can increase false positives; a higher one can suppress weak or incorrect detections. Treat this as a demonstration or diagnostic adjustment, not as a substitute for evaluating the model on representative data. A confidence score is not a guarantee that a class prediction is correct.
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Full versus tiny YOLO v1
| Variant | What it is useful for | Trade-offs |
|---|---|---|
| Full YOLO v1 | Studying the original model or reproducing historical Darknet workflows. | Approximately 1.0 GB of weights according to the Darknet documentation; greater storage and memory demands than the tiny model. |
| Tiny YOLO v1 | A smaller, quicker demonstration or a low-memory experiment. | Lower model capacity and generally lower accuracy than the full model. |
To try the tiny variant, pair its configuration and weights rather than mixing full and tiny files:
%cd /content/darknet
!wget https://pjreddie.com/media/files/tiny-yolov1.weights
!./darknet yolo test cfg/yolov1-tiny.cfg /content/darknet/tiny-yolov1.weights data/person.jpgThe original Darknet documentation gives approximately 611 MB of GPU memory and more than 150 FPS on a Titan X for the tiny model under its historical test conditions. Those are not Colab benchmarks and should not be used to predict your runtime’s speed; hardware, software, image size, and measurement method differ.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Troubleshoot common failures
Configuration or image path not found
Check the current directory and locate the expected file:
!pwd
!find /content -name "yolov1.cfg"Change to the directory containing the repository with %cd /content/darknet, or use the configuration path you actually found. A different Darknet fork may not include the legacy configuration.
Darknet executable missing or permission denied
Check !ls -lh /content/darknet/darknet and review the output of make. If compilation failed, resolve that error before trying to run inference; changing file permissions will not create a missing executable.
CUDA, cuDNN, or OpenCV build errors
- Confirm whether the runtime has an NVIDIA GPU with
!nvidia-smi. - Check whether the old source supports the installed CUDA and compiler versions; legacy code can fail on a newer Colab image.
- For a simple still-image test, try rebuilding without the failing optional feature, or with
GPU=0if a GPU build is not viable. - If you need Darknet specifically, consult the current instructions for a maintained fork such as hank-ai/darknet. Its structure and build steps are not necessarily interchangeable with the old repository.
Weights appear incomplete or invalid
Inspect both file type and size:
!file /content/darknet/yolov1.weights
!ls -lh /content/darknet/yolov1.weightsAn HTML response or tiny file indicates a failed download, not usable weights. Do not pair tiny weights with the full configuration or vice versa.
Runtime disconnected or files disappeared
Colab sessions are temporary. To keep files beyond the current runtime, mount Drive:
from google.colab import drive
drive.mount("/content/drive")Mounting Drive provides persistent file storage, not a persistent runtime: a reset still clears state under /content. Google also documents limitations for some externally controlled runtime arrangements in its Colab Marketplace notice.
The output is wrong even though inference completed
Successful execution only shows that the model ran. YOLO v1 can miss small or tightly grouped objects, confuse unusual appearances, and predict classes outside the image’s actual content. Its pretrained class set is limited to the training categories; changing the threshold cannot add new classes or correct a model trained on a different distribution. The original paper discusses these spatial limitations in detail.
Should you use YOLO v1 for a new project?
Use YOLO v1 when the goal is to understand early single-pass detection, follow a historical Darknet workflow, or reproduce a specific old experiment. It is not the default choice for a new application in 2026: it is a legacy implementation with constrained predictions, aging build assumptions, and pretrained classes that may not fit your data.
If you need current model support, custom training, or other modern vision tasks, use a maintained framework and name its model accurately. The official Ultralytics object-detection documentation and YOLOv5 Colab notebook show a separate, modern workflow. That is an alternative—not YOLO v1—and its package, weights, build process, and licensing terms are different. Check the terms for the specific implementation and model you plan to deploy.
If you already own a compatible GPU and need more control over drivers, files, and runtime setup, Google documents a local Colab runtime. For repeated or larger GPU workloads, Google Cloud infrastructure is another option, but it adds environment and billing management; see Google Cloud GPUs. A brief YOLO v1 demonstration generally does not require paid compute.
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